**Application of Acoustic Emission for Quality Evaluation of Fruits and Vegetables**

Artur Zdunek

[13] Kowalski, S. J, & Rajewska, K. (2002). Dried induced stresses in elastic and viscoelastic

[14] Kowalski, S. J, Rajewska, K, & Rybicki, A. (2000). Destruction of wet materials by

[15] Kowalski, S. J, & Rybicki, A. (1996). Drying stress formation induced by inhomogene‐ ous moisture and temperature distribution. Transport in Porous Media , 24, 139-156.

[16] Kowalski, S. J, & Rybicki, A. (2007). The vapour-liquid interface and stresses in dried

[17] Luong Phong M(1994). Centrifuge simulation of Rayleigh waves in soils using a drop-

[18] Malecki, I, & Ranachowski, J. (1994). Acoustic emission: Sources, Methods Applica‐

[19] Mujumdar, A. S. Ed.) ((2007). Handbook of Industrial Drying (Third Edition), Taylor

[20] Strumiłło Cz(1983). Fundamentals of the Theory and the Technology of Drying, WNT

[21] Wert, C. A, & Thomson, R. M. (1974). Physics of solids, National Scientific Publishers,

ball arrangement, ASTM Special Technical Publication, (1213), 385-399.

saturated materials. Chem Eng Sci , 57, 3883-3892.

drying, Chem. Eng. Sci. , 55, 6755-6762.

174 Acoustic Emission - Research and Applications

bodies, Transport in Porous Media , 66, 43-58.

tions, Pascal Publications, Warszawa (in Polish).

& Francis Group, New York.

Warszawa, (in Polish).

London, UK

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/53985

#### **1. Introduction**

Food crushing sound is one of the main factors used for food quality evaluation. Crispness and crunchiness are attributes of high quality product and are usually pointed on the top of a list of consumer preferences. However, the meanings of crispness and crunchiness are still imprecise. Its perception varies from country to country and from individual to individual. Despite of this there is a general consensus that crispy and crunchy sensation is related to fracture properties. Crispy product is mechanically brittle, firm and acoustically noisy as a result of large number of small fractures. Crunchiness is probably related to events (frac‐ tures) occurring on subsequent layers in a cell structure what gives the sense of extension of sound duration in time.

In spite of sensory and subjective nature of food quality evaluation by human senses, a big effort is put for objective sound properties analysis during biting and chewing and for de‐ veloping instrumental methods for human independent food evaluation. The first instru‐ mental analysis of sound was published by Drake in 1963, who found that crisper products emit louder sound and an average amplitude of successive bursts during mastication de‐ creases [1]. Then, several authors used different sound descriptors for judging a chewing sound, as the number of sound burst in a bite *n*, the mean amplitude of the burst *A* or the products of these values *nA* or *nA/sound duration* [1, 2, 3]. The first hypothesis was that the sense of crispness is an auditory phenomena, i.e. is the air-conducted sound. However, work done by Christensen and Vickers in 1981 showed that crispness may be a vibratory phenomena, i.e. is the bone-conducted sound [4].

Most of studies on crispness and crunchiness concern dry food products, like cakes, chips, etc.. However, this problem has been found as important also for fruits and vegetables called as wet food products. In 2002, presumably for the first time, Fillion and Kilcast stated

that crispness and crunchiness are very complex concept containing sound, fracture charac‐ teristic, density and geometry of fruit and vegetables [5]. They found that crispy wet food would refer to a light and thin texture producing a sharp clean break with a high-pitch sound mainly during the first bite with the front teeth. Crunchy wet food would be hard and has a dense texture producing loud, low-pitch sound that occurs over successive chews.

structure level is also important not only from the point of view the sensory properties. For example, microcracking is important for blackspot bruising of potato which starts enzymatic

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177

**Figure 1.** A simplified mechanical model of parenchyma tissue of fruits and vegetables

**3. Acoustic emission (AE) for evaluation of the onset of plant tissue**

As it is for other solid materials, acoustic emission has been found as a very useful method for monitoring of cracking of plant tissue under external loading. AE has been applied for this group of materials for the first time in the late 90's [6]. The first attempt was aimed on observation of the AE signal from damaged sample of potato tissue. Figure 2 presents scheme of the first apparatus used. A one-column and low noise testing machine was used for studying the mechanical properties and forces that participate in the process of plant tis‐ sue cracking. In that work a wide-band piezoelectric sensor was used for the recording of the acoustic emission signal with high-sensitivity in the frequency range from 25 kHz to 1 MHz. Due to a small size of the samples which are usually used in experiments with plants and relatively large deformations which can cause friction between sensor and sample, fix‐ ing the AE sensor directly on the sample was impossible. This problem was solved by fixing the sensor to the jaw of the testing machine, like it is shown in the Fig. 2. Since the AE signal passes from the material with lower density and enters into the material with higher density at the border between the sample and the jaw (sample of tissue – steel), sensitivity of meas‐ urement is sufficient to record even small AE events. In order to eliminate any friction and improvement of sound conductivity, silicon grease was applied on each boundary on the way of elastic waves from sample to sensor. A set consisted of a pre-amplifier (40dB) with a high-pass filter (25 kHz) and a low-noise amplifier with adjustable gain was used for signal conditioning. The set was supplemented with a high speed transducer card A/D that al‐

browning of the tissue under the skin.

**cracking**

Many instrumental methods have been applied for crispness and crunchiness evaluation of fruits and vegetables. Due to the fact that mastication is a highly destructive process, mechani‐ cal tests are the most popular to simulate the biting. Results of such tests, like texture profile analysis, compression, tension, twist or three-point bending, show correlation with properties of a material thus can be also used for its texture evaluation. One of the simplest is a puncture test where probe is pushed into tissue and a maximum force is used as a firmness value.

As it was obtained by Christensen and Vickers [4] crispness may by the bone-conducted phenomena. Therefore, the acoustic emission (AE) method where a sensor is in a contact with material investigated is promising tool for food properties evaluation. In general, the acoustic emission is monitored during deformation of a material to provide information on cracking and internal friction of material pieces by analysis of AE descriptors like: ampli‐ tude, frequency, energy, counts, events, etc..

#### **2. Tissue model and source of AE in fruit and vegetables**

The largest volume of fruit and vegetable tissue is taken by parenchymatous cells therefore this part of fruit is particularly often studied, also from the reason that it is relatively easy to cut a sample for testing. A simplified model of parenchyma tissue, which considers the most important mechanical actors, is shown in Fig. 1. The mechanical model is built of cells which are fluid filled and walls which are elastic-like. Cell walls are made of polysaccharides: cel‐ lulose fibrils network embedded in a matrix of pectins and hemicelluloses. Cells adhere to each other through middle lamellas which are made of amorphous pectins. Tissue structure also contains intercellular spaces, which for some fruits, like apple, can even take 25%. It is generally agreed that cell walls have elastic properties whereas pectins in the middle lamella are plastic-like. External forces which cause deformation of such structure increase a pres‐ sure inside the cells and tension in the cell walls. Simultaneously, shearing forces between cells increase. Thus, two failure modes are possible: cell wall rupturing and cell-cell debond‐ ing when strength of cell walls or/and intercellular adhesion is overcome, respectively. These two processes can be the sources of acoustic emission in the case of cellular plants. However, acoustic emission from cell-cell debonding due to plastic character is less likely compared to sudden rupturing of elastic cell walls.

Studying of cracking process of plant tissue is thus indeed important for understanding of quality of fruits and vegetables. When tissue cracks between cells, for example for fruit and veggie which have been stored too long, the material may show a mealy character whereas the fresh one, just after picking up, cracking through cell walls reveals juicy and crispy prop‐ erties which are usually desired by consumers. Mechanical strength of plant tissue at micro‐ structure level is also important not only from the point of view the sensory properties. For example, microcracking is important for blackspot bruising of potato which starts enzymatic browning of the tissue under the skin.

**Figure 1.** A simplified mechanical model of parenchyma tissue of fruits and vegetables

that crispness and crunchiness are very complex concept containing sound, fracture charac‐ teristic, density and geometry of fruit and vegetables [5]. They found that crispy wet food would refer to a light and thin texture producing a sharp clean break with a high-pitch sound mainly during the first bite with the front teeth. Crunchy wet food would be hard and has a dense texture producing loud, low-pitch sound that occurs over successive chews. Many instrumental methods have been applied for crispness and crunchiness evaluation of fruits and vegetables. Due to the fact that mastication is a highly destructive process, mechani‐ cal tests are the most popular to simulate the biting. Results of such tests, like texture profile analysis, compression, tension, twist or three-point bending, show correlation with properties of a material thus can be also used for its texture evaluation. One of the simplest is a puncture

test where probe is pushed into tissue and a maximum force is used as a firmness value.

**2. Tissue model and source of AE in fruit and vegetables**

tude, frequency, energy, counts, events, etc..

176 Acoustic Emission - Research and Applications

compared to sudden rupturing of elastic cell walls.

As it was obtained by Christensen and Vickers [4] crispness may by the bone-conducted phenomena. Therefore, the acoustic emission (AE) method where a sensor is in a contact with material investigated is promising tool for food properties evaluation. In general, the acoustic emission is monitored during deformation of a material to provide information on cracking and internal friction of material pieces by analysis of AE descriptors like: ampli‐

The largest volume of fruit and vegetable tissue is taken by parenchymatous cells therefore this part of fruit is particularly often studied, also from the reason that it is relatively easy to cut a sample for testing. A simplified model of parenchyma tissue, which considers the most important mechanical actors, is shown in Fig. 1. The mechanical model is built of cells which are fluid filled and walls which are elastic-like. Cell walls are made of polysaccharides: cel‐ lulose fibrils network embedded in a matrix of pectins and hemicelluloses. Cells adhere to each other through middle lamellas which are made of amorphous pectins. Tissue structure also contains intercellular spaces, which for some fruits, like apple, can even take 25%. It is generally agreed that cell walls have elastic properties whereas pectins in the middle lamella are plastic-like. External forces which cause deformation of such structure increase a pres‐ sure inside the cells and tension in the cell walls. Simultaneously, shearing forces between cells increase. Thus, two failure modes are possible: cell wall rupturing and cell-cell debond‐ ing when strength of cell walls or/and intercellular adhesion is overcome, respectively. These two processes can be the sources of acoustic emission in the case of cellular plants. However, acoustic emission from cell-cell debonding due to plastic character is less likely

Studying of cracking process of plant tissue is thus indeed important for understanding of quality of fruits and vegetables. When tissue cracks between cells, for example for fruit and veggie which have been stored too long, the material may show a mealy character whereas the fresh one, just after picking up, cracking through cell walls reveals juicy and crispy prop‐ erties which are usually desired by consumers. Mechanical strength of plant tissue at micro‐

### **3. Acoustic emission (AE) for evaluation of the onset of plant tissue cracking**

As it is for other solid materials, acoustic emission has been found as a very useful method for monitoring of cracking of plant tissue under external loading. AE has been applied for this group of materials for the first time in the late 90's [6]. The first attempt was aimed on observation of the AE signal from damaged sample of potato tissue. Figure 2 presents scheme of the first apparatus used. A one-column and low noise testing machine was used for studying the mechanical properties and forces that participate in the process of plant tis‐ sue cracking. In that work a wide-band piezoelectric sensor was used for the recording of the acoustic emission signal with high-sensitivity in the frequency range from 25 kHz to 1 MHz. Due to a small size of the samples which are usually used in experiments with plants and relatively large deformations which can cause friction between sensor and sample, fix‐ ing the AE sensor directly on the sample was impossible. This problem was solved by fixing the sensor to the jaw of the testing machine, like it is shown in the Fig. 2. Since the AE signal passes from the material with lower density and enters into the material with higher density at the border between the sample and the jaw (sample of tissue – steel), sensitivity of meas‐ urement is sufficient to record even small AE events. In order to eliminate any friction and improvement of sound conductivity, silicon grease was applied on each boundary on the way of elastic waves from sample to sensor. A set consisted of a pre-amplifier (40dB) with a high-pass filter (25 kHz) and a low-noise amplifier with adjustable gain was used for signal conditioning. The set was supplemented with a high speed transducer card A/D that al‐ lowed for the recording of counts, events and energy in time intervals from 0.001 to 1 second or fast sampling with 2.5MHz short samples 0.25 s long.

The most useful method of analysis of AE signal bases on the transformation of the time signals to the form of descriptors recorded in time intervals. If a certain threshold is es‐ tablished for the amplitude of the received signal, called a discrimination threshold, then every time the amplitude goes above this signal is recorded as one count. Groups of the AE signal with the characteristics shape of a damped sinusoid curve are called events (Fig. 3). Instead of analysis the shape of event, it is possible to define an AE event as a group of counts recorded in consecutive samples. The number of counts and the number of events recorded in time gates are called count rate and event rate. Registered AE sig‐ nal, presented in Fig. 3 in amplitude – time coordinates can be also characterized by AE energy *E*. Assuming that a signal event of duration *t* and of peak amplitude *V* of an event, then energy of each event can be evaluated as:

$$E = \, 0.5V^2t \tag{1}$$

**Figure 3.** The method of determination of AE descriptors: counts and events from time-amplitude signal. The discrimi‐

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179

In Fig. 4 it is easy to notice that for a decrease in the slope of the stress-strain curve (bioyeld and rupture points in this case), a high value of AE counts was observed. The highest val‐ ues, however, appear at the moment of sudden rupture. Macro-cracks of the samples are clearly visible on the cross-sections of the sample after rupture, and sometimes are even au‐ dible to human ears. Before rupture, presumably some cracks also appear, particularly in the region close to bioyield, however it is difficult to detect them visually. The acoustic emis‐ sion signal shown in Fig. 4 unambiguously proves that bioyield point and rupture could be assigned to tissue cracking, however maybe at different scales. Moreover, a long before bio‐ yeld, AE signal has been detected too, although with significantly lower number of counts. In this region of deformation no noticeable decrease in the slope of the stress-strain curve was observed. Generally, the AE counts before bioyield is more irregular and have lower values in comparison to acoustic signal after bioyield point. It is believed that microcracking is developing gradually due to heterogeneous structure of the plant tissue even before it could be noted from force-deformation curve. This is particularly important because even a small crack of tissue, for example damage of intercellular plasmalemma, could start irrever‐

sible biochemical processes which decay quality of the material or affect its function.

nation level is found experimentally to avoid external noises, for example from the loading device

Besides the parameters mentioned above, other parameters based on the transformations of time or spectrum of the AE signal could be also used. Detailed definitions and de‐ scriptions of the AE signal descriptors can be found in literature [7]. The system present‐ ed in Fig. 2 also allowed for simultaneous measuring mechanical and acoustical properties. For example, stress and AE counts as a function of strain could be recorded together as it is shown in Fig. 4.

**Figure 2.** Scheme of the first apparatus used for acoustic emission recording from deformed plant tissue (based on the work of Zdunek and Konstankiewicz [6]). The EA 100 was the analyser which allowed both recording samples with 2.5MHz and conversion of the signal to AE descriptors

lowed for the recording of counts, events and energy in time intervals from 0.001 to 1 second

The most useful method of analysis of AE signal bases on the transformation of the time signals to the form of descriptors recorded in time intervals. If a certain threshold is es‐ tablished for the amplitude of the received signal, called a discrimination threshold, then every time the amplitude goes above this signal is recorded as one count. Groups of the AE signal with the characteristics shape of a damped sinusoid curve are called events (Fig. 3). Instead of analysis the shape of event, it is possible to define an AE event as a group of counts recorded in consecutive samples. The number of counts and the number of events recorded in time gates are called count rate and event rate. Registered AE sig‐ nal, presented in Fig. 3 in amplitude – time coordinates can be also characterized by AE energy *E*. Assuming that a signal event of duration *t* and of peak amplitude *V* of an

Besides the parameters mentioned above, other parameters based on the transformations of time or spectrum of the AE signal could be also used. Detailed definitions and de‐ scriptions of the AE signal descriptors can be found in literature [7]. The system present‐ ed in Fig. 2 also allowed for simultaneous measuring mechanical and acoustical properties. For example, stress and AE counts as a function of strain could be recorded

**Figure 2.** Scheme of the first apparatus used for acoustic emission recording from deformed plant tissue (based on the work of Zdunek and Konstankiewicz [6]). The EA 100 was the analyser which allowed both recording samples with

<sup>2</sup> *E Vt* = 0.5 (1)

or fast sampling with 2.5MHz short samples 0.25 s long.

178 Acoustic Emission - Research and Applications

event, then energy of each event can be evaluated as:

together as it is shown in Fig. 4.

2.5MHz and conversion of the signal to AE descriptors

**Figure 3.** The method of determination of AE descriptors: counts and events from time-amplitude signal. The discrimi‐ nation level is found experimentally to avoid external noises, for example from the loading device

In Fig. 4 it is easy to notice that for a decrease in the slope of the stress-strain curve (bioyeld and rupture points in this case), a high value of AE counts was observed. The highest val‐ ues, however, appear at the moment of sudden rupture. Macro-cracks of the samples are clearly visible on the cross-sections of the sample after rupture, and sometimes are even au‐ dible to human ears. Before rupture, presumably some cracks also appear, particularly in the region close to bioyield, however it is difficult to detect them visually. The acoustic emis‐ sion signal shown in Fig. 4 unambiguously proves that bioyield point and rupture could be assigned to tissue cracking, however maybe at different scales. Moreover, a long before bio‐ yeld, AE signal has been detected too, although with significantly lower number of counts. In this region of deformation no noticeable decrease in the slope of the stress-strain curve was observed. Generally, the AE counts before bioyield is more irregular and have lower values in comparison to acoustic signal after bioyield point. It is believed that microcracking is developing gradually due to heterogeneous structure of the plant tissue even before it could be noted from force-deformation curve. This is particularly important because even a small crack of tissue, for example damage of intercellular plasmalemma, could start irrever‐ sible biochemical processes which decay quality of the material or affect its function.

**Figure 4.** Examples of simultaneous recording of stress, strain and AE counts for potato tuber tissue. Bioyield point is visible as the short drop down of the stress-strain curve, rupture is visible as major final fall down of the stress, and the critical point as the onset of the acoustic signal (AE counts). The critical stress (Rc) and critical strain (εc) define the criti‐ cal point (result obtained by the author)

In the first paper on application of AE for potato tissue, new mechanical parameters have been proposed. Critical stress (Rc) and critical strain (εc) have been defined as the mechanical conditions at the onset of acoustic emission. In further studies critical stress and critical strain was analysed under different conditions of mechanical test (Fig. 5) and samples itself (Fig. 6).

**Figure 5.** Critical stress and critical strain obtained as the onset of AE during compression of two potato cultivars

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181

The relations between critical stress, critical strain, and osmolality of mannitol solutions in which the samples were hydrated or dehydrated are shown in Fig. 6. Higher osmolality of the mannitol solutions corresponds with lower turgor of the tissue reached after 24h treat‐ ment. A strong influence of tissue turgor on critical stress and failure stress was observed. Both critical stress and failure stress increase in a linear manner when turgor decrease (di‐ rection of turgor change is shown in Fig. 6). The turgor effect can be interpreted in terms of a model of a single cell. Before deformation, higher turgor causes larger preliminary tension in the cell wall. Thus, the additional cell deformation or the additional external force neces‐

Application of the acoustic emission method has proven that micro-cracking of tissue starts significantly earlier than it can be observed on the stress-strain curve. However, no correla‐ tion between the critical values and the failure values of samples tested under the same con‐ ditions (the same strain rate or turgor) has been observed [8]. This means that observation of the critical values does not allow prediction of the bioyielding conditions for example. This is presumably result of the fact that critical point and the bioyield point are different stages of cracking. Between them cracking is developing, from the first local micro-cracks to large macro-cracks. This propagation mat be very chaotic and accidental due to heterogeneous

'Danusia' and 'Kuba' with different strain rates (result obtained by the author)

sary for wall rupture are lower.

microstructure of a tissue.

Fig. 5 presents how the critical stress and critical strain change with strain rate of two potato cultivars (*Solanum tuberosum* cv. Danusia and Kuba). An increase in the strain rate decreases exponentially both parameters. From microscopic point of view, deformation of plant tissue causes changes in the cell shape. Since initially cells of parenchyma tend to have rounded shape due to incompressibility of the intracellular fluid, the ratio of cells surface to cells vol‐ ume increases. This means that cell walls are generally stretched during deformation. The tension force in the walls is a function of strain rate and wall permeability. In a simplified model, low strain rate has an effect similar to that of high permeability in the model. At a relatively high strain rate, a seepage of the intracellular fluid through the walls is limited and leads to a higher tensions at the same cell deformation. When the strain rate is low, the intracellular fluid has relatively more time to flow out of the cells, and this produces smaller increases of the tensile forces in the walls. Thus, in this case the strength limit of the cell wall can be reached at higher cell deformation and higher external forces. For relatively slow cell deformation, the cell can even be completely compressed without wall rupture. This ex‐ plains the pronounced increase of the critical values at very slow rates (Fig. 5).

**Figure 5.** Critical stress and critical strain obtained as the onset of AE during compression of two potato cultivars 'Danusia' and 'Kuba' with different strain rates (result obtained by the author)

**Figure 4.** Examples of simultaneous recording of stress, strain and AE counts for potato tuber tissue. Bioyield point is visible as the short drop down of the stress-strain curve, rupture is visible as major final fall down of the stress, and the critical point as the onset of the acoustic signal (AE counts). The critical stress (Rc) and critical strain (εc) define the criti‐

In the first paper on application of AE for potato tissue, new mechanical parameters have been proposed. Critical stress (Rc) and critical strain (εc) have been defined as the mechanical conditions at the onset of acoustic emission. In further studies critical stress and critical strain was analysed under different conditions of mechanical test (Fig. 5) and

Fig. 5 presents how the critical stress and critical strain change with strain rate of two potato cultivars (*Solanum tuberosum* cv. Danusia and Kuba). An increase in the strain rate decreases exponentially both parameters. From microscopic point of view, deformation of plant tissue causes changes in the cell shape. Since initially cells of parenchyma tend to have rounded shape due to incompressibility of the intracellular fluid, the ratio of cells surface to cells vol‐ ume increases. This means that cell walls are generally stretched during deformation. The tension force in the walls is a function of strain rate and wall permeability. In a simplified model, low strain rate has an effect similar to that of high permeability in the model. At a relatively high strain rate, a seepage of the intracellular fluid through the walls is limited and leads to a higher tensions at the same cell deformation. When the strain rate is low, the intracellular fluid has relatively more time to flow out of the cells, and this produces smaller increases of the tensile forces in the walls. Thus, in this case the strength limit of the cell wall can be reached at higher cell deformation and higher external forces. For relatively slow cell deformation, the cell can even be completely compressed without wall rupture. This ex‐

plains the pronounced increase of the critical values at very slow rates (Fig. 5).

cal point (result obtained by the author)

180 Acoustic Emission - Research and Applications

samples itself (Fig. 6).

The relations between critical stress, critical strain, and osmolality of mannitol solutions in which the samples were hydrated or dehydrated are shown in Fig. 6. Higher osmolality of the mannitol solutions corresponds with lower turgor of the tissue reached after 24h treat‐ ment. A strong influence of tissue turgor on critical stress and failure stress was observed. Both critical stress and failure stress increase in a linear manner when turgor decrease (di‐ rection of turgor change is shown in Fig. 6). The turgor effect can be interpreted in terms of a model of a single cell. Before deformation, higher turgor causes larger preliminary tension in the cell wall. Thus, the additional cell deformation or the additional external force neces‐ sary for wall rupture are lower.

Application of the acoustic emission method has proven that micro-cracking of tissue starts significantly earlier than it can be observed on the stress-strain curve. However, no correla‐ tion between the critical values and the failure values of samples tested under the same con‐ ditions (the same strain rate or turgor) has been observed [8]. This means that observation of the critical values does not allow prediction of the bioyielding conditions for example. This is presumably result of the fact that critical point and the bioyield point are different stages of cracking. Between them cracking is developing, from the first local micro-cracks to large macro-cracks. This propagation mat be very chaotic and accidental due to heterogeneous microstructure of a tissue.

1 kHz -20 kHz for lower band and 10kHz-900 kHz for higher band. Next, the analogue sig‐ nals are converted into digital one by A/D boards. Sampling rates per channel: 44 000 and 150 000 samples per second are more than double of the frequency range of the sensors used. The second channel of each card is used for recording an analogue signal of force de‐ livered from universal testing machine to synchronize both acoustic and mechanical signals.

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**Figure 7.** Scheme of the system for recording of acoustic emission from punctured fruit. Two channels were used: 1-16kHz and 20-75 kHz. Puncturing was performed with Lloyd LRX device. SA is the sensor for audible frequencies, SU is the sensor for ultrasound frequencies. Dimensions of the puncture probe are exemplary (scheme by the author)

In Fig. 8, typical profiles of acoustic emission counts recorded during puncturing of apple flesh are shown. In the case of apple puncturing, the AE signal starts just from the moments of touching puncture probe to tissue. The number of counts increases progressively up to a moment when force-deformation curve yields. At this moment the whole curved part of the probe is in a contact with the tissue. When the probe goes deeper into apple tissue the acous‐ tic activity decreases. This could be result of damping of acoustic waves by surrounding tis‐ sue and already damaged tissue layers under the probe. In many studies, softening of apples during ripening and storage has been reflected in a lower penetration force (lowering firmness). The integrative use of contact AE and the puncture test showed that major acous‐ tic signals are observed together with drops of force. The coincidence was interpreted as an

**Figure 6.** Critical stress and critical strain obtained as the onset of AE during compression of two potato cultivars 'Danusia' and 'Kuba' dehydrated and hydrated in different mannitol solutions (result obtained by the author)

#### **4. Acoustic emission and different mechanical tests**

#### **4.1. Puncture test**

Application of acoustic emission is possible in any mechanical test which are used common‐ ly for plants. The key issue is to apply AE sensor to the sample which in the case of plants is usually largely deformable. Solution presented previously uses indirect attachment through solid body. This solution was proven to be very effective in various mechanical tests. Sen‐ sors of AE could be placed inside device for mechanical tests, exactly in the probe for mate‐ rial deformation. Such device for puncture test is presented in Fig. 7. Here, acoustic emission during the puncture test is caused and recorded by head with two sensors placed inside. The head consists two parts. Top part is made of ertacetal and the bottom part is made of duraluminium which effectively conducts elastic waves. The application of two different material was intended to limit eventual disturbances from mechanical system. They are screwed to each other. Acoustic emission sensors are glued (or it can be screwed also) to the top surface of the metal part. In the system presented in Fig. 7, one sensor works in audible range 1-16kHz (SA), whereas the second sensor has maximum sensitivity in ultrasound range 25-100kHz (SU) to cover as wide as possible frequency region. The sensors are con‐ nected to individual amplifiers with adjustable amplifying. The signal is filtered in the range 1 kHz -20 kHz for lower band and 10kHz-900 kHz for higher band. Next, the analogue sig‐ nals are converted into digital one by A/D boards. Sampling rates per channel: 44 000 and 150 000 samples per second are more than double of the frequency range of the sensors used. The second channel of each card is used for recording an analogue signal of force de‐ livered from universal testing machine to synchronize both acoustic and mechanical signals.

**Figure 6.** Critical stress and critical strain obtained as the onset of AE during compression of two potato cultivars 'Danusia' and 'Kuba' dehydrated and hydrated in different mannitol solutions (result obtained by the author)

Application of acoustic emission is possible in any mechanical test which are used common‐ ly for plants. The key issue is to apply AE sensor to the sample which in the case of plants is usually largely deformable. Solution presented previously uses indirect attachment through solid body. This solution was proven to be very effective in various mechanical tests. Sen‐ sors of AE could be placed inside device for mechanical tests, exactly in the probe for mate‐ rial deformation. Such device for puncture test is presented in Fig. 7. Here, acoustic emission during the puncture test is caused and recorded by head with two sensors placed inside. The head consists two parts. Top part is made of ertacetal and the bottom part is made of duraluminium which effectively conducts elastic waves. The application of two different material was intended to limit eventual disturbances from mechanical system. They are screwed to each other. Acoustic emission sensors are glued (or it can be screwed also) to the top surface of the metal part. In the system presented in Fig. 7, one sensor works in audible range 1-16kHz (SA), whereas the second sensor has maximum sensitivity in ultrasound range 25-100kHz (SU) to cover as wide as possible frequency region. The sensors are con‐ nected to individual amplifiers with adjustable amplifying. The signal is filtered in the range

**4. Acoustic emission and different mechanical tests**

**4.1. Puncture test**

182 Acoustic Emission - Research and Applications

**Figure 7.** Scheme of the system for recording of acoustic emission from punctured fruit. Two channels were used: 1-16kHz and 20-75 kHz. Puncturing was performed with Lloyd LRX device. SA is the sensor for audible frequencies, SU is the sensor for ultrasound frequencies. Dimensions of the puncture probe are exemplary (scheme by the author)

In Fig. 8, typical profiles of acoustic emission counts recorded during puncturing of apple flesh are shown. In the case of apple puncturing, the AE signal starts just from the moments of touching puncture probe to tissue. The number of counts increases progressively up to a moment when force-deformation curve yields. At this moment the whole curved part of the probe is in a contact with the tissue. When the probe goes deeper into apple tissue the acous‐ tic activity decreases. This could be result of damping of acoustic waves by surrounding tis‐ sue and already damaged tissue layers under the probe. In many studies, softening of apples during ripening and storage has been reflected in a lower penetration force (lowering firmness). The integrative use of contact AE and the puncture test showed that major acous‐ tic signals are observed together with drops of force. The coincidence was interpreted as an energy release in the form of sound as a result of material fracturing. In the case of plant tissues, AE signal comes mainly from rupture of the cell wall because of its somewhat elastic properties, whereas the middle lamella due to plastic properties rather do not generate sound. This hypothesis is supported by analysis of AE during ripening of apples, which will be discussed later on in this chapter.

**Figure 9.** Acustograms of apple tissue in puncture test within frequency range 1-16 kHz (left) and 20-75 kHz (right), f

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**Figure 10.** Relationship between the total energy of acoustic signal recorded within frequency range 1-16 kHz and

– frequency, t – time (result obtained by the author)

20-75kHz for apples in puncture test (result obtained by the author)

**Figure 8.** AE counts and force recorded during puncturing of fresh (1 day of shelf life) and stored (10 days of shelf life) apples (result obtained by the author)

In Fig. 9, spectrum of the signal is presented from two frequency ranges in a form of "acustograms". Colors in the acustograms represent a power of the signal in time-fre‐ quency coordinates. A few dominant frequencies can be found: 5.5 kHz, 9.5 kHz,15 kHz, 32 kHz, 44 kHz and 56 kHz. They constantly appear during puncturing. Precise analysis showed that they are also characteristic for the system used because no significant changes were found with properties material used. The only one change observed with change of properties of the material was an overall change in amplitude that occurred uniformly for all bands. Additionally, Fig. 10 presents relation between AE energy in 1-16 kHz and 20-75 kHz obtained for a large set of apples in puncture test. It is shown that the relation is very linear and in the case of this material, investigation in higher band does not provide any additional information.

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energy release in the form of sound as a result of material fracturing. In the case of plant tissues, AE signal comes mainly from rupture of the cell wall because of its somewhat elastic properties, whereas the middle lamella due to plastic properties rather do not generate sound. This hypothesis is supported by analysis of AE during ripening of apples, which will

**Figure 8.** AE counts and force recorded during puncturing of fresh (1 day of shelf life) and stored (10 days of shelf life)

In Fig. 9, spectrum of the signal is presented from two frequency ranges in a form of "acustograms". Colors in the acustograms represent a power of the signal in time-fre‐ quency coordinates. A few dominant frequencies can be found: 5.5 kHz, 9.5 kHz,15 kHz, 32 kHz, 44 kHz and 56 kHz. They constantly appear during puncturing. Precise analysis showed that they are also characteristic for the system used because no significant changes were found with properties material used. The only one change observed with change of properties of the material was an overall change in amplitude that occurred uniformly for all bands. Additionally, Fig. 10 presents relation between AE energy in 1-16 kHz and 20-75 kHz obtained for a large set of apples in puncture test. It is shown that the relation is very linear and in the case of this material, investigation in higher

be discussed later on in this chapter.

184 Acoustic Emission - Research and Applications

apples (result obtained by the author)

band does not provide any additional information.

**Figure 9.** Acustograms of apple tissue in puncture test within frequency range 1-16 kHz (left) and 20-75 kHz (right), f – frequency, t – time (result obtained by the author)

**Figure 10.** Relationship between the total energy of acoustic signal recorded within frequency range 1-16 kHz and 20-75kHz for apples in puncture test (result obtained by the author)

Fig. 11 presents changes of total AE events and mean AE amplitude during shelf life storage of three apple cultivars. Data was obtained in puncture test. AE descriptors decrease almost linearly during shelf-life storage revealing large Pearson's correlation coefficients *R* with time of storage (Table 1). In Fig. 11 is visible that the acoustic emission method is very sensi‐ tive for registering changes that occur during postharvest storage of apples. Total number of AE events and mean AE amplitude usually shows higher *R* value when compared to firm‐ ness from puncture test (Table 1).

pectins and lower turgor lead to changes of fracturing mode toward cell–cell debonding. Due to the microstructure, the thin cell wall in plants is considered as an elasto-plastic mate‐ rial. The elasto-plastic character of the cell wall is responsible for the brittle fracturing neces‐ sary for sound generation. The intercellular lamella between cells consists of amorphous pectin and are considered as plastic. It is probable that the pectin plasticity causes slow dis‐ sipation of strain energy and no brittle fracturing without sound generation. Therefore, it is most likely that the sound made during puncturing is generated mostly when cell walls fracture. In material science terms, a crack propagates if there is a stress concentration into a small tip zone. Thus, propagation is ineffective if there is any plastic zone (in the case of ap‐ ples it would be pectin). Other features that halt crack propagation are the cell interiors or intercellular spaces. There is ~ 25 % space within apple tissue, and this amount increases with ripening. Thus, ripening attenuates conditions of cracking propagation. Again, in terms of material science, the cell wall (as the material where the stress can concentrate) has a key

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The maximum number of acoustic events recorded after pushing the probe into the apple

This number agrees roughly with the number of fractured cell walls during the test estimat‐ ed on the basis of two assumptions; that the mean cell diameter is ~0.25 mm, which is true for apples, and all cells in the path of the probe are damaged (i.e., all cell walls are broken). This result shows that the breakage of each cell wall would be the source of single acoustic event. The mean AE amplitude depends on a stress value in the source of cracking (i.e. the strength of the cell wall) and on the attenuation of the elastic waves on the way from the source to the sensor. As mentioned above, pectin degradation occurs during apple storage, which can decrease the strength of the cell walls due to an increased mobility of cellulose fibrils in the pectin matrix. On the other hand, softening of the bulk tissue caused by pectin degradation in general and the decrease of turgor, increases the attenuation of the elastic

Texture profile analysis (TPA) is used for simulation of eating process. Compression test is performed in two cycles to the same deformation level of a sample. Scheme of TPA test and graphical representation of TPA descriptors used for sample characterization are shown in Fig. 12. Texture profile analysis is performed on cylindrical samples in two cycles. Maxi‐ mum deformation applied could be about 20-40% of initial sample height for both cycles, depending on sample strength (20% for apple, 40% for potato). Maximum deformation l should be close to failure points of investigated material. TPA requires cracking of the test material to simulate the destructive process during eating. On the other hand, deformation should not be too far to prevent the compression of the small pieces of the initial sample in the second cycle which causes springiness and cohesiveness to become physically meaning‐ less. The probe always returns to the trigger point after the first cycle. No rest periods is pro‐ grammed between the TPA compression cycles to avoid material relaxation. The textural parameters are calculated in the following way (Fig. 12). Hardness 1 is the force peak of the

waves. Both causes that amplitude decrease with softening of apples.

(Fig. 11), which was obtained for fresh apples immediately after harvest.

role in crack propagation.

**4.2. Texture Profile Analysis (TPA)**

flesh was about 105

**Figure 11.** Total number of AE events and mean AE amplitude registered in puncture test of apples (three cultivars) during shelf life storage (result obtained by the author)


**Table 1.** Correlation coefficients *R* of changes of AE events, AE mean amplitude and firmness, in puncture test of apples with shelf-life days for three cultivars 'Elstar', 'Gloster' and 'Jonagold' (results obtained by the author)

The postharvest softening of apples is caused by biochemical processes. During apple ripen‐ ing two major processes occur that affect the mechanical properties of the tissue including its fracturing mechanism. Pectin degradation during ripening causes a decrease of adhesion between cells leading to tissue softening and changes the fracturing mode toward cell–cell debonding. As a result of respiration and metabolism during storage, turgor pressure can decrease, which has consequences for the fracturing process. The lower tension of the cell wall at low turgor causes a greater deformation that leads to wall fracture. Another conse‐ quence of low turgor is a decrease of cell–cell adhesion. Thus, in general, degradation of pectins and lower turgor lead to changes of fracturing mode toward cell–cell debonding. Due to the microstructure, the thin cell wall in plants is considered as an elasto-plastic mate‐ rial. The elasto-plastic character of the cell wall is responsible for the brittle fracturing neces‐ sary for sound generation. The intercellular lamella between cells consists of amorphous pectin and are considered as plastic. It is probable that the pectin plasticity causes slow dis‐ sipation of strain energy and no brittle fracturing without sound generation. Therefore, it is most likely that the sound made during puncturing is generated mostly when cell walls fracture. In material science terms, a crack propagates if there is a stress concentration into a small tip zone. Thus, propagation is ineffective if there is any plastic zone (in the case of ap‐ ples it would be pectin). Other features that halt crack propagation are the cell interiors or intercellular spaces. There is ~ 25 % space within apple tissue, and this amount increases with ripening. Thus, ripening attenuates conditions of cracking propagation. Again, in terms of material science, the cell wall (as the material where the stress can concentrate) has a key role in crack propagation.

The maximum number of acoustic events recorded after pushing the probe into the apple flesh was about 105 (Fig. 11), which was obtained for fresh apples immediately after harvest. This number agrees roughly with the number of fractured cell walls during the test estimat‐ ed on the basis of two assumptions; that the mean cell diameter is ~0.25 mm, which is true for apples, and all cells in the path of the probe are damaged (i.e., all cell walls are broken). This result shows that the breakage of each cell wall would be the source of single acoustic event. The mean AE amplitude depends on a stress value in the source of cracking (i.e. the strength of the cell wall) and on the attenuation of the elastic waves on the way from the source to the sensor. As mentioned above, pectin degradation occurs during apple storage, which can decrease the strength of the cell walls due to an increased mobility of cellulose fibrils in the pectin matrix. On the other hand, softening of the bulk tissue caused by pectin degradation in general and the decrease of turgor, increases the attenuation of the elastic waves. Both causes that amplitude decrease with softening of apples.

#### **4.2. Texture Profile Analysis (TPA)**

Fig. 11 presents changes of total AE events and mean AE amplitude during shelf life storage of three apple cultivars. Data was obtained in puncture test. AE descriptors decrease almost linearly during shelf-life storage revealing large Pearson's correlation coefficients *R* with time of storage (Table 1). In Fig. 11 is visible that the acoustic emission method is very sensi‐ tive for registering changes that occur during postharvest storage of apples. Total number of AE events and mean AE amplitude usually shows higher *R* value when compared to firm‐

**Figure 11.** Total number of AE events and mean AE amplitude registered in puncture test of apples (three cultivars)

AE events -0,80 -0,54 -0,81 AE amplitude -0,90 -0,70 -0,88 Firmness -0,80 -0,40 -0,82

**Table 1.** Correlation coefficients *R* of changes of AE events, AE mean amplitude and firmness, in puncture test of apples with shelf-life days for three cultivars 'Elstar', 'Gloster' and 'Jonagold' (results obtained by the author)

The postharvest softening of apples is caused by biochemical processes. During apple ripen‐ ing two major processes occur that affect the mechanical properties of the tissue including its fracturing mechanism. Pectin degradation during ripening causes a decrease of adhesion between cells leading to tissue softening and changes the fracturing mode toward cell–cell debonding. As a result of respiration and metabolism during storage, turgor pressure can decrease, which has consequences for the fracturing process. The lower tension of the cell wall at low turgor causes a greater deformation that leads to wall fracture. Another conse‐ quence of low turgor is a decrease of cell–cell adhesion. Thus, in general, degradation of

**R Elstar Gloster Jonagold**

ness from puncture test (Table 1).

186 Acoustic Emission - Research and Applications

during shelf life storage (result obtained by the author)

**Variable**

Texture profile analysis (TPA) is used for simulation of eating process. Compression test is performed in two cycles to the same deformation level of a sample. Scheme of TPA test and graphical representation of TPA descriptors used for sample characterization are shown in Fig. 12. Texture profile analysis is performed on cylindrical samples in two cycles. Maxi‐ mum deformation applied could be about 20-40% of initial sample height for both cycles, depending on sample strength (20% for apple, 40% for potato). Maximum deformation l should be close to failure points of investigated material. TPA requires cracking of the test material to simulate the destructive process during eating. On the other hand, deformation should not be too far to prevent the compression of the small pieces of the initial sample in the second cycle which causes springiness and cohesiveness to become physically meaning‐ less. The probe always returns to the trigger point after the first cycle. No rest periods is pro‐ grammed between the TPA compression cycles to avoid material relaxation. The textural parameters are calculated in the following way (Fig. 12). Hardness 1 is the force peak of the first cycle. Hardness 2 is the force peak of the second cycle. Cohesiveness is calculated as the ratio of the area under the curve of the second cycle to the area under the curve of the first cycle. Springiness is the ratio L2/L1, where L2 is the time or distance from the beginning of the second cycle to hardness 2 point and L1 is the time or distance from the beginning of the test to the hardness 1 point. Acoustic emission during the TPA could be recorded using the same head as for the puncture test described above.

recovery of the material after the first cycle. If a crack occurs during the first cycle, it can propagate during the second one. If the material failed during the first cycle (macro-crack‐ ing occurred) relaxation of the material is less and deformation in the second cycle is also smaller. Therefore cracking propagation is less and, as consequence, acoustic emission is low. In other words, the weakening of the material in the first cycle causes only small acous‐ tic emission due to the propagation of already existing cracks within the material during the

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Fig. 14 and Table 2 show decrease of acoustic descriptors during shelf life and that the corre‐ lation with days of storage is in general similar as for mechanical descriptors from this test.

**Figure 13.** Typical TPA curves with AE counts for fresh and soft apple, and for hydrated potato sample (result ob‐

second cycle.

tained by the author)

**Figure 12.** Scheme of TPA test performed on cylindrical sample of plant tissue. Proposed positioning of the AE sensors (1 and 2 with different frequency range) is shown. The upper plate for compression is removable to change a probe for other mechanical tests. Graph to the right presents typical TPA curve with the method of calculation texture de‐ scriptors (by the author)

Typical TPA curves together with acoustic emission counts for apple and potato are shown in Fig.13. For comparison, results for two apples, fresh and soft one are plotted, and for hy‐ drated potato sample. The fresh tissue has higher hardness 1 which was reached earlier than in the case of the soft tissue. The range of macro-cracking, visible as the gradual force de‐ crease in the first cycle, is longer and more jagged. The second cycle of TPA also shows larg‐ er forces in comparison to soft sample. It is typical that for fresher samples and with higher turgor, the failure occurs at a lower deformation or earlier on the time axis. Acoustic signal appears earlier and it has higher values in the case of fresh apple than in the case of soft one. In apple, acoustic counts are recorded almost from the beginning of the compression. This would be a result of both weaker cell walls and intercellular bonds than for potato which is actually very dense and strong tissue. Failure is accompanied by high acoustic emission counts for both materials as a result of macro-cracking (Fig. 13). This moment is also usually air-conducted and audible. The acoustic emission in TPA is recorded mainly during the downward movement of the machine probe. During the upward movement, a small signal is only observed just after the probe starts returning. It disappears at the end of the return‐ ing stage. The second cycle of TPA may also cause acoustic emission. However, the signal is usually weak especially in the case of apple. The second cycle in TPA starts from the trigger point of the first cycle. Thus, the time of deformation during the second cycle is related to recovery of the material after the first cycle. If a crack occurs during the first cycle, it can propagate during the second one. If the material failed during the first cycle (macro-crack‐ ing occurred) relaxation of the material is less and deformation in the second cycle is also smaller. Therefore cracking propagation is less and, as consequence, acoustic emission is low. In other words, the weakening of the material in the first cycle causes only small acous‐ tic emission due to the propagation of already existing cracks within the material during the second cycle.

first cycle. Hardness 2 is the force peak of the second cycle. Cohesiveness is calculated as the ratio of the area under the curve of the second cycle to the area under the curve of the first cycle. Springiness is the ratio L2/L1, where L2 is the time or distance from the beginning of the second cycle to hardness 2 point and L1 is the time or distance from the beginning of the test to the hardness 1 point. Acoustic emission during the TPA could be recorded using the

**Figure 12.** Scheme of TPA test performed on cylindrical sample of plant tissue. Proposed positioning of the AE sensors (1 and 2 with different frequency range) is shown. The upper plate for compression is removable to change a probe for other mechanical tests. Graph to the right presents typical TPA curve with the method of calculation texture de‐

Typical TPA curves together with acoustic emission counts for apple and potato are shown in Fig.13. For comparison, results for two apples, fresh and soft one are plotted, and for hy‐ drated potato sample. The fresh tissue has higher hardness 1 which was reached earlier than in the case of the soft tissue. The range of macro-cracking, visible as the gradual force de‐ crease in the first cycle, is longer and more jagged. The second cycle of TPA also shows larg‐ er forces in comparison to soft sample. It is typical that for fresher samples and with higher turgor, the failure occurs at a lower deformation or earlier on the time axis. Acoustic signal appears earlier and it has higher values in the case of fresh apple than in the case of soft one. In apple, acoustic counts are recorded almost from the beginning of the compression. This would be a result of both weaker cell walls and intercellular bonds than for potato which is actually very dense and strong tissue. Failure is accompanied by high acoustic emission counts for both materials as a result of macro-cracking (Fig. 13). This moment is also usually air-conducted and audible. The acoustic emission in TPA is recorded mainly during the downward movement of the machine probe. During the upward movement, a small signal is only observed just after the probe starts returning. It disappears at the end of the return‐ ing stage. The second cycle of TPA may also cause acoustic emission. However, the signal is usually weak especially in the case of apple. The second cycle in TPA starts from the trigger point of the first cycle. Thus, the time of deformation during the second cycle is related to

same head as for the puncture test described above.

188 Acoustic Emission - Research and Applications

scriptors (by the author)

Fig. 14 and Table 2 show decrease of acoustic descriptors during shelf life and that the corre‐ lation with days of storage is in general similar as for mechanical descriptors from this test.

**Figure 13.** Typical TPA curves with AE counts for fresh and soft apple, and for hydrated potato sample (result ob‐ tained by the author)

allow cutting desired by the standard sample dimensions, which should be also sufficient to produce detectable acoustic emission. According to the standard, S/W=4 (span/height) is suggested [10]. Sample of potato tissue of height W=16 mm and width B=8mm emits strong enough signal in the system showed in Fig. 7 and Fig. 15. Although, to keep the ratio the span should be 64mm, it is usually difficult to cut samples longer than L=40mm from typical potato or apple.. Therefore, the span most often must be shortened to S=32mm for example, which is reasonable and gives S/W=2 ratio. According to standard, in the middle of the sam‐

Scheme of SENB test configuration is presented in Fig. 15. Acoustic emission during the SENB could be recorded using the same head as for the puncture test described above where one or more AE sensors could be placed. Sample is placed on support with the notch

SENB allows determination of a critical stress intensity factor *Kc*. The *Kc* can be obtained us‐

. *<sup>C</sup>*

3( ) . <sup>2</sup> 2 1 (1 )

*W W*

*<sup>a</sup> <sup>A</sup>*

1/2

*W W W*

3/2

æ ö <sup>=</sup> ç ÷ è ø (2)

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191

2 2

³ (5)

=- - - + (4)

(3)

3 2

*P S <sup>a</sup> K f <sup>W</sup> BW*

to the bottom. Bending is performed up to fracture of the sample.

*c*

*<sup>f</sup> <sup>a</sup> <sup>W</sup> W a a*

æ ö ç ÷ <sup>=</sup> æ ö è ø ç ÷ + -

è ø

3.93 2.7 1.99 / (1 )(2.15 ). *a aa A aW*

<sup>2</sup> 2.5 .( ) *<sup>c</sup>*

s

*y K*

where: *Bc* is minimal width of a sample used, *σ<sup>y</sup>* is a failure stress in uniaxial compression of

*C*

*B*

where: *S*- span, *Pc* is a failure force. Function *f(a/W)* is given as:

*Kc* has a physical meaning if following formula is true:

ple a notch with depth of a=8mm is cut.

ing formula:

where:

the same material.

**Figure 14.** Change of acoustic descriptors obtained in TPA test during shelf life storage for three apple cultivars (result obtained by the author)


**Table 2.** Correlation coefficients between AE events, mean AE amplitude, hardness H1 and H2, cohesiveness Co, springiness SP in TPA test and shelf life days for three apple cultivars 'Elstar', Gloster' and 'Jonagold' (result obtained by the author)

#### **4.3. Single edge notched bending (SENB)**

Recently, new engineering mechanical tests has been introduced for analysing the fracture properties of plant tissue, so called single edge notched bending (SENB) [9]. In the test rec‐ tangular sample with a notch is bended to breaking up. From sample geometry and from failure force obtained from force-bending curve, a critical stress intensity factor *Kc* can be cal‐ culated. This material parameter is tried to correlate with textural properties of a tissue, like crispness or crunchiness. However, from mechanical point of view, the critical stress intensi‐ ty factor is a force criterion for starting cracking propagation up within material.

Single edge notched bending is performed on rectangular beams according to the ASTM Specification E-399 standard. It is very often that fruit or veggies size or geometry does not allow cutting desired by the standard sample dimensions, which should be also sufficient to produce detectable acoustic emission. According to the standard, S/W=4 (span/height) is suggested [10]. Sample of potato tissue of height W=16 mm and width B=8mm emits strong enough signal in the system showed in Fig. 7 and Fig. 15. Although, to keep the ratio the span should be 64mm, it is usually difficult to cut samples longer than L=40mm from typical potato or apple.. Therefore, the span most often must be shortened to S=32mm for example, which is reasonable and gives S/W=2 ratio. According to standard, in the middle of the sam‐ ple a notch with depth of a=8mm is cut.

Scheme of SENB test configuration is presented in Fig. 15. Acoustic emission during the SENB could be recorded using the same head as for the puncture test described above where one or more AE sensors could be placed. Sample is placed on support with the notch to the bottom. Bending is performed up to fracture of the sample.

SENB allows determination of a critical stress intensity factor *Kc*. The *Kc* can be obtained us‐ ing formula:

$$K\_c = \frac{P\_C S}{\frac{3}{2} \frac{3}{2}} f\left(\frac{a}{W}\right). \tag{2}$$

where: *S*- span, *Pc* is a failure force. Function *f(a/W)* is given as:

$$f\left(\frac{a}{W}\right) = \frac{3A(\frac{a}{W})^{1/2}}{2\left(1 + \frac{2a}{W}\right)(1 - \frac{a}{W})^{3/2}}.\tag{3}$$

where:

**Figure 14.** Change of acoustic descriptors obtained in TPA test during shelf life storage for three apple cultivars (result

AE events -0,58 -0,64 -0,78 Mean AE amplitude -0,83 -0,70 -0,82 *H1* -0,78 -0,19 -0,85 *H2* -0,78 -0,33 -0,83 *CO* -0,45 -0,19 -0,44 *SP* -0,23 0,27 0,38

**Table 2.** Correlation coefficients between AE events, mean AE amplitude, hardness H1 and H2, cohesiveness Co, springiness SP in TPA test and shelf life days for three apple cultivars 'Elstar', Gloster' and 'Jonagold' (result obtained by

Recently, new engineering mechanical tests has been introduced for analysing the fracture properties of plant tissue, so called single edge notched bending (SENB) [9]. In the test rec‐ tangular sample with a notch is bended to breaking up. From sample geometry and from failure force obtained from force-bending curve, a critical stress intensity factor *Kc* can be cal‐ culated. This material parameter is tried to correlate with textural properties of a tissue, like crispness or crunchiness. However, from mechanical point of view, the critical stress intensi‐

Single edge notched bending is performed on rectangular beams according to the ASTM Specification E-399 standard. It is very often that fruit or veggies size or geometry does not

ty factor is a force criterion for starting cracking propagation up within material.

**R Elstar Gloster Jonagold**

obtained by the author)

the author)

**Variable**

190 Acoustic Emission - Research and Applications

**4.3. Single edge notched bending (SENB)**

$$A = 1.99 - a / W (1 - \frac{a}{W}) (2.15 - \frac{3.93a}{W} + \frac{2.7a^2}{W^2}).\tag{4}$$

*Kc* has a physical meaning if following formula is true:

$$B\_{\odot} \ge 2.5(\frac{K\_c}{\sigma\_y})^2. \tag{5}$$

where: *Bc* is minimal width of a sample used, *σ<sup>y</sup>* is a failure stress in uniaxial compression of the same material.

Figure 16 presents typical SENB curve and acoustic emission events for fresh and soft apple. AE signal starts just from the beginning of bending which suggest that cracking propagation also starts from the tip of the notch. For fresh apples, which is also harder and has higher *Kc* value, AE is significantly larger than for the soft material however in both cases acoustic emission lasts up to sample fracture.

SENB test, similar to puncture and TPA, is able to distinguish sample according to its soft‐ ness. Fig. 17 presents example for three apple cultivars which were stored at shelf life condi‐ tions. It is visible that acoustic descriptors diminishes during storage. Table 3 presents correlation coefficients of parameters obtained from SENB test with time of shelf life storage. The coefficients for acoustic descriptors are higher than these for mechanical descriptors which shows again that AE method is very suitable for monitoring properties of fruits.

**Figure 16.** Typical SENB curves with acoustic emission for fresh and soft apples (result obtained by the author)

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**Figure 17.** Change of acoustic descriptors obtained in SENB test during shelf life storage for three apple cultivars (re‐

There are four key factors of food quality: appearance, smell, texture, and nutritional value [11]. The first three are known as sensory acceptability factors, since they are perceived by the human senses and can be evaluated directly by the consumers. Sensory acceptability of food products is incredibly important, since people want to enjoy eating their favorite prod‐ ucts. It can also be difficult to convince consumers to eat healthy products that are unappeal‐

**5. Evaluation of sensory properties with acoustic emission**

sult obtained by the author)

**Figure 15.** Scheme of the single edge notched bending SENB test for plant tissue with locations of AE sensors (1 and 2, for audible and ultrasound range for example), (by the author)


**Table 3.** Correlation coefficients *R* for changes work to maximum force, *Kc*, AE events, mean AE amplitude in SENB test and days of shelf life for three apple cultivars 'Elstar', 'Gloster' and 'Jonagold' (result obtained by the author)

Application of Acoustic Emission for Quality Evaluation of Fruits and Vegetables http://dx.doi.org/10.5772/53985 193

Figure 16 presents typical SENB curve and acoustic emission events for fresh and soft apple. AE signal starts just from the beginning of bending which suggest that cracking propagation also starts from the tip of the notch. For fresh apples, which is also harder and has higher *Kc* value, AE is significantly larger than for the soft material however in both cases acoustic

SENB test, similar to puncture and TPA, is able to distinguish sample according to its soft‐ ness. Fig. 17 presents example for three apple cultivars which were stored at shelf life condi‐ tions. It is visible that acoustic descriptors diminishes during storage. Table 3 presents correlation coefficients of parameters obtained from SENB test with time of shelf life storage. The coefficients for acoustic descriptors are higher than these for mechanical descriptors which shows again that AE method is very suitable for monitoring properties of fruits.

**Figure 15.** Scheme of the single edge notched bending SENB test for plant tissue with locations of AE sensors (1 and

AE events -0,77 -0,43 -0,83 Mean AE amplitude -0,78 -0,47 -0,84 Work to maximum force -0,52 -0,14 -0,64

*Kc* -0,70 -0,18 -0,73

**Table 3.** Correlation coefficients *R* for changes work to maximum force, *Kc*, AE events, mean AE amplitude in SENB test and days of shelf life for three apple cultivars 'Elstar', 'Gloster' and 'Jonagold' (result obtained by the author)

*R* **Elstar Gloster Jonagold**

2, for audible and ultrasound range for example), (by the author)

**Variable**

emission lasts up to sample fracture.

192 Acoustic Emission - Research and Applications

**Figure 16.** Typical SENB curves with acoustic emission for fresh and soft apples (result obtained by the author)

**Figure 17.** Change of acoustic descriptors obtained in SENB test during shelf life storage for three apple cultivars (re‐ sult obtained by the author)

#### **5. Evaluation of sensory properties with acoustic emission**

There are four key factors of food quality: appearance, smell, texture, and nutritional value [11]. The first three are known as sensory acceptability factors, since they are perceived by the human senses and can be evaluated directly by the consumers. Sensory acceptability of food products is incredibly important, since people want to enjoy eating their favorite prod‐ ucts. It can also be difficult to convince consumers to eat healthy products that are unappeal‐

ing in terms of appearance and texture. Food gives us pleasure not just through its flavour or fragrance; we also want to be aware that what we are eating is fresh. In case of fruit, we associate the latter with mechanical qualities; fruits are desirable when their texture is crun‐ chy, crisp and juicy, and less so when they are mealy.

For acoustic emission, the system presented in Fig. 7 may be used. It could be a laboratory system with commercial universal testing machine (machine noise at desired speed should be considered) completed with a low noise set up for AE recording and the most important: correctly chosen sensor. Since the goal is to relate sensory perception with the instrumental method, the frequency range of sensor used can be limited to the audible range: 1-16 kHz. This range can be covered easily by one sensor only. The use of commercial devices pro‐ vides possibility of easy adjusting of settings to different materials and application of differ‐ ent mechanical loadings programmes, however it is relatively expensive solution. Recently the first simplified system has been developed for apples only (Fig. 18). The CAED (contact acoustic emission detector developed by the author) has a fixed puncture probe and the pa‐ rameter of the puncture test adjusted exactly for apple. The device uses an accelerometer with sensitivity within the audible frequency range. To avoid large data sets, electronic con‐ verts time-amplitude signal into counts in 0.1s time intervals. Counts and actual force can be exported to ASCII whereas sum of all counts (called total AE counts) in the test and firmness

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A different instrument for texture evaluation was proposed by N. Sakurai's team from Japan (Fig. 19). The device uses a piezoelectric element, attached between wedge type probe for inserting into investigated material and piston driven by hydraulic mechanism [12]. Vibra‐ tions, caused by destruction by the wedge type probe of investigated fruit or veggies, are detected by piezoelement. The absolute amplitude in Volts (V) and time of duration of the signal (T) has been used for definition so called Texture Index (TI) according to the formula:

. *V*

TI could be determined within several frequency bands to check witch of them could dis‐ criminate a sample. TI has been used for many fruits and vegetables as well for dray food products which showed that TI has frequency related pattern characteristic for different ob‐ jects. TI was also compared with sensory texture of persimmon which showed that correla‐ tion of TI with several texture attributes (sweetness, juiciness, thickness, hardness, fragrance, appearance, and overall acceptability) can reach 0.8, particularly in the frequency range low‐

To calibrate the instrumental method with the use of acoustic emission, a generic descriptive analysis is a suitable method for obtaining sensory texture attributes. Sensory testing labora‐ tory should fulfils the general requirements of a standard, as an example ISO 8589:1988 standard for sensory testing conditions. Each test booth should be equipped with a system for data acquisition from panellists. The expert panel usually consists more than 6 trained persons selected on the basis of the ability of individuals to discriminate tastes and texture attributes. Before the experiment, the panellists usually take part in a training session, where definitions of attributes are discussed and clarified (as in Table 4). For the experiment pieces of fruits are assigned a code and the samples are presented to panellists in random order.

*<sup>T</sup>* <sup>=</sup> <sup>å</sup> (6)

*TI*

are displayed after each test.

er than 3 kHz [13].

From a mechanical perspective, crispness, juiciness, and mealiness are all associated with how the cellular structure is broken down. If biting into an apple causes the cell walls to rupture releasing intracellular juices, it makes the apple feel juicy and crispy. This is because of the acoustic signal generated as part of the process, which is perceived positively by our auditory system. It is believed also that crispness can be perceived as a combination of acoustic impressions and the strength required to break down the product, while the acous‐ tic signal is largely perceived as vibrations by the jaw bone (bone-conducted sound). Once the cellular walls rupture, the fruit takes on a mealy quality and the fruit is generally per‐ ceived to be overripe.

Texture is a sensory characteristic; assessing it objectively is extremely difficult since con‐ sumers' personal and cultural predispositions vary greatly, and perceptions can even de‐ pend on the person's mood or frame of mind at the time. Texture of fruits and veggies is also not a constant feature, and is affected by many factors, such as natural biological variability, treatment prior to picking, time of picking, and method and duration of stor‐ age. This is why it should be monitored on an ongoing basis, while at the same time the measurements should be simple, repeatable, and low-cost. Unfortunately sensory assess‐ ment conducted by a professional panel or representative group of consumers does not meet these criteria.

Since crispness may be the bone-conducted phenomena the approach of utilizing acoustic emission with use of sensor in contact with sample is appropriate way of instrumental anal‐ ysis of the sensory texture sound-related properties. An advantage of this approach is rela‐ tively low sensitivity to external noises comparing to air conducted methods, like these ones which use microphones placed close to sample. The use of the "contact" acoustic emission while mechanical test has also advantage of recording both important for consumers attrib‐ utes: acoustic and mechanical ones. Typically, system used must be calibrated with refer‐ ence to standard sensory analysis. Descriptors from instrumental method, independently or as a combination, should be compared with sensory texture attributes to provide the most robust calibration model as possible.

Despite of various mechanical methods used for quality testing of fruits, described previous‐ ly, the puncture method is still the most popular. This simple puncture test has been used for a long time in laboratories, orchards and industry. The output of the test is firmness val‐ ue expressed in Newton (N) defined as the maximum force needed to push probe into fruit flesh. In the most common configuration of the test, probe of 11.1 mm with dome-shaped ending with a radius of curvature of 8.73 mm is pushed 8 mm into the fruit. These settings are valid especially for apples. For other fruit they can be adjusted according to their hard‐ ness and dimension.

For acoustic emission, the system presented in Fig. 7 may be used. It could be a laboratory system with commercial universal testing machine (machine noise at desired speed should be considered) completed with a low noise set up for AE recording and the most important: correctly chosen sensor. Since the goal is to relate sensory perception with the instrumental method, the frequency range of sensor used can be limited to the audible range: 1-16 kHz. This range can be covered easily by one sensor only. The use of commercial devices pro‐ vides possibility of easy adjusting of settings to different materials and application of differ‐ ent mechanical loadings programmes, however it is relatively expensive solution. Recently the first simplified system has been developed for apples only (Fig. 18). The CAED (contact acoustic emission detector developed by the author) has a fixed puncture probe and the pa‐ rameter of the puncture test adjusted exactly for apple. The device uses an accelerometer with sensitivity within the audible frequency range. To avoid large data sets, electronic con‐ verts time-amplitude signal into counts in 0.1s time intervals. Counts and actual force can be exported to ASCII whereas sum of all counts (called total AE counts) in the test and firmness are displayed after each test.

ing in terms of appearance and texture. Food gives us pleasure not just through its flavour or fragrance; we also want to be aware that what we are eating is fresh. In case of fruit, we associate the latter with mechanical qualities; fruits are desirable when their texture is crun‐

From a mechanical perspective, crispness, juiciness, and mealiness are all associated with how the cellular structure is broken down. If biting into an apple causes the cell walls to rupture releasing intracellular juices, it makes the apple feel juicy and crispy. This is because of the acoustic signal generated as part of the process, which is perceived positively by our auditory system. It is believed also that crispness can be perceived as a combination of acoustic impressions and the strength required to break down the product, while the acous‐ tic signal is largely perceived as vibrations by the jaw bone (bone-conducted sound). Once the cellular walls rupture, the fruit takes on a mealy quality and the fruit is generally per‐

Texture is a sensory characteristic; assessing it objectively is extremely difficult since con‐ sumers' personal and cultural predispositions vary greatly, and perceptions can even de‐ pend on the person's mood or frame of mind at the time. Texture of fruits and veggies is also not a constant feature, and is affected by many factors, such as natural biological variability, treatment prior to picking, time of picking, and method and duration of stor‐ age. This is why it should be monitored on an ongoing basis, while at the same time the measurements should be simple, repeatable, and low-cost. Unfortunately sensory assess‐ ment conducted by a professional panel or representative group of consumers does not

Since crispness may be the bone-conducted phenomena the approach of utilizing acoustic emission with use of sensor in contact with sample is appropriate way of instrumental anal‐ ysis of the sensory texture sound-related properties. An advantage of this approach is rela‐ tively low sensitivity to external noises comparing to air conducted methods, like these ones which use microphones placed close to sample. The use of the "contact" acoustic emission while mechanical test has also advantage of recording both important for consumers attrib‐ utes: acoustic and mechanical ones. Typically, system used must be calibrated with refer‐ ence to standard sensory analysis. Descriptors from instrumental method, independently or as a combination, should be compared with sensory texture attributes to provide the most

Despite of various mechanical methods used for quality testing of fruits, described previous‐ ly, the puncture method is still the most popular. This simple puncture test has been used for a long time in laboratories, orchards and industry. The output of the test is firmness val‐ ue expressed in Newton (N) defined as the maximum force needed to push probe into fruit flesh. In the most common configuration of the test, probe of 11.1 mm with dome-shaped ending with a radius of curvature of 8.73 mm is pushed 8 mm into the fruit. These settings are valid especially for apples. For other fruit they can be adjusted according to their hard‐

chy, crisp and juicy, and less so when they are mealy.

ceived to be overripe.

194 Acoustic Emission - Research and Applications

meet these criteria.

ness and dimension.

robust calibration model as possible.

A different instrument for texture evaluation was proposed by N. Sakurai's team from Japan (Fig. 19). The device uses a piezoelectric element, attached between wedge type probe for inserting into investigated material and piston driven by hydraulic mechanism [12]. Vibra‐ tions, caused by destruction by the wedge type probe of investigated fruit or veggies, are detected by piezoelement. The absolute amplitude in Volts (V) and time of duration of the signal (T) has been used for definition so called Texture Index (TI) according to the formula:

$$TI = \frac{\sum |V|}{T}.\tag{6}$$

TI could be determined within several frequency bands to check witch of them could dis‐ criminate a sample. TI has been used for many fruits and vegetables as well for dray food products which showed that TI has frequency related pattern characteristic for different ob‐ jects. TI was also compared with sensory texture of persimmon which showed that correla‐ tion of TI with several texture attributes (sweetness, juiciness, thickness, hardness, fragrance, appearance, and overall acceptability) can reach 0.8, particularly in the frequency range low‐ er than 3 kHz [13].

To calibrate the instrumental method with the use of acoustic emission, a generic descriptive analysis is a suitable method for obtaining sensory texture attributes. Sensory testing labora‐ tory should fulfils the general requirements of a standard, as an example ISO 8589:1988 standard for sensory testing conditions. Each test booth should be equipped with a system for data acquisition from panellists. The expert panel usually consists more than 6 trained persons selected on the basis of the ability of individuals to discriminate tastes and texture attributes. Before the experiment, the panellists usually take part in a training session, where definitions of attributes are discussed and clarified (as in Table 4). For the experiment pieces of fruits are assigned a code and the samples are presented to panellists in random order. During the experiment the panellists determine the perceived intensity of texture attributes using linear, unstructured scale with a range of 0 – 100 points. After the test, the results are often converted to the most frequently used: 10-point scale.

**Figure 19.** Scheme of device for texture index (TI) evaluation (scheme based on Taniwaki et al. [13])

Crispness The sound intensity during the first bite with the

Hardness The resistance during the first bite with the front

Mealiness The mealy sense, especially on the tongue and

Overall texture The overall sensory harmonization of textural

**Table 4.** Definitions and scale of some sensory texture attributes.

**Sensory texture attribute Definition Scale**

Juiciness The sense of juice release during biting 0 = no juice, dry, 100 = very juicy

In the case of CAED which provides mechanical and acoustic indexes, for construction cali‐ bration models, several methods can be used: simple linear regression, multiple linear re‐ gression or multivariative regressions. For construction the models, averaged values from 10 apples (totally 244 samples from 19 apple cultivars) were taken, as it is usually assumed for sensory analysis to minimalize individual preferences. Examples of statistics for different calibration models are presented in Table 5 (after Zdunek et al. [14]). These data were ob‐ tained for different 19 apple cultivars, which were stored in various ways. This example shows that the performances of the linear regression models are satisfactory for crispness and hardness prediction by both firmness or by total AE counts however quantitative pre‐

front teeth 0 = no sound,100 = very noisy

Application of Acoustic Emission for Quality Evaluation of Fruits and Vegetables

http://dx.doi.org/10.5772/53985

197

teeth 0 = very soft, 100 = very hard

the palate 0 = not mealy, 100 = very mealy

attributes 0 = bad, 100 = very good

**Figure 18.** Contact acoustic emission detector (CAED) for apple testing developed by the author. Device uses AE sen‐ sor (1) which is placed in the AE head ended by the puncture probe (2). Apple is lift up by a motorized stage (3) to puncture probe. Force is recorded by the force sensor (5) with capacity of 200 N. Electronic (4) calculates on line AE counts and records the actual force within 0.1s time intervals. Sum of counts and firmness (N) are displayed on the screen after the test (photo by the author)

Application of Acoustic Emission for Quality Evaluation of Fruits and Vegetables http://dx.doi.org/10.5772/53985 197

**Figure 19.** Scheme of device for texture index (TI) evaluation (scheme based on Taniwaki et al. [13])


**Table 4.** Definitions and scale of some sensory texture attributes.

During the experiment the panellists determine the perceived intensity of texture attributes using linear, unstructured scale with a range of 0 – 100 points. After the test, the results are

**Figure 18.** Contact acoustic emission detector (CAED) for apple testing developed by the author. Device uses AE sen‐ sor (1) which is placed in the AE head ended by the puncture probe (2). Apple is lift up by a motorized stage (3) to puncture probe. Force is recorded by the force sensor (5) with capacity of 200 N. Electronic (4) calculates on line AE counts and records the actual force within 0.1s time intervals. Sum of counts and firmness (N) are displayed on the

screen after the test (photo by the author)

often converted to the most frequently used: 10-point scale.

196 Acoustic Emission - Research and Applications

In the case of CAED which provides mechanical and acoustic indexes, for construction cali‐ bration models, several methods can be used: simple linear regression, multiple linear re‐ gression or multivariative regressions. For construction the models, averaged values from 10 apples (totally 244 samples from 19 apple cultivars) were taken, as it is usually assumed for sensory analysis to minimalize individual preferences. Examples of statistics for different calibration models are presented in Table 5 (after Zdunek et al. [14]). These data were ob‐ tained for different 19 apple cultivars, which were stored in various ways. This example shows that the performances of the linear regression models are satisfactory for crispness and hardness prediction by both firmness or by total AE counts however quantitative pre‐ diction is impossible in any case using this modelling approach. Crispness is slightly better predicted by total AE counts than by firmness when these individuals are taken for simple linear model whereas hardness is apparently better predicted by firmness than by acoustic variable. It is presumably due to different origins of the variables: sensory crispness is gov‐ erned mostly from auditory phenomena whereas sensory hardness from mechanical one. Table 5 presents also performance statistics of multiple regression models (MLR) where both firmness F and total AE counts were considered in the linear model. General improvement of models is observed in the case of each sensory attribute. Furthermore, multivariative principal components regression (PCR) models, where total AE counts and firmness are used as the predictors of a group of sensory variables, show remarkable improvement of cal‐ ibration performance comparing to linear regression and multiple regression models. Full cross validation (CV) in the PCR for showed that satisfactory prediction is possible in the case of hardness. The models allow for prediction also crispness and overall texture with slightly less accuracy. In the case of juiciness, successful prediction seems to be doubtful whereas mealiness prediction is impossible. Test set validation (TSV) method showed appa‐ rently better model performance in the case of crispness and slightly better in the case of juiciness whereas for the rest of sensory attributes performance from TSV method is worse that from CV method. In general both validation methods show satisfactory prediction of crispness and hardness from multivariative PCR calibration models.

**Variables used for calibration**

Linear regression F

Linear regression CAE

Multi-linear regression F and

CAE

CAE

CAE

Principal component regression F and

Principal component regression F and

**Author details**

Artur Zdunek

**Validation method**

CV

CV

CV

CV Ncal=244

TSV Ncal=187 Ntest=57

higher than 2, the model can predict quantitatively sensory attributes [16]

Address all correspondence to: a.zdunek@ipan.lublin.pl

Institute of Agrophysics, Polish Academy of Sciences, Lublin, Poland

**Performance statistic of validation**

**Calibrated sensory texture attribute**

**overall texture** 199

http://dx.doi.org/10.5772/53985

**crispness hardness juiciness mealiness**

Application of Acoustic Emission for Quality Evaluation of Fruits and Vegetables

R2 0.57 0.68 0.40 0.38 0.52

RMSECV 1.10 0.86 0.95 0.83 1.05

RPD 1.53 1.75 1.29 1.27 1.45

R2 0.62 0.60 0.48 0.33 0.52

RMSECV 0.98 0.95 0.89 0.87 1.06

RPD 1.72 1.63 1.45 1.27 1.50

R2 0.71 0.77 0.53 0.43 0.62

RMSECV 0.90 0.73 0.84 0.80 0.96

RPD 1.87 2.07 1.46 1.32 1.59

R2 0.72 0.77 0.53 0.43 0.63

RMSECV 0.90 0.73 0.84 0.80 0.93

RPD 1.87 2.12 1.53 1.38 1.71

R2 0.90 0.77 0.67 0.25 0.51

RMSEP 0.53 0.66 0.68 0.96 1.01

RPD 2.91 2.04 1.61 1.15 1.44

**Table 5.** Performance statistics of linear regression models, multiple regression and principal component regression models for prediction sensory texture attributes of apples by CAED (after Zdunek et al [14]). Ncal – Number of samples used for calibration. Ntest – Number of samples used for validation, F-firmness, CAE – total AE counts, CV-cross validation, R2 – determination coefficient, RMSECV - root mean squared errors of cross validation or RMSEP - root mean squared error of prediction, RPD - ratio of prediction to deviation calculated as the ratio of standard deviation of validation data set to RMSECV or RMSEP. If the RPD was below 1.5 the model is not useful, and when the value was

The model improvement, when both acoustic and firmness are considered in calibration models, agrees with the hypothesis that crispness perception should be interpreted as coun‐ teraction of acoustic and mechanical phenomena. It is usually observed that firmer apples are also more crispy. In Fig. 8 it is visible that firmer apple has more jagged force-deforma‐ tion (FD) profile during puncturing whereas soft apple has more smoother one. It was ac‐ companied with higher AE counts at the each force dropping down. One can say that firm apple is also more brittle. The jaggedness of the FD is important from the point of view crispness because humans can detect loads of less than 0.1 N. Such interpretation is especial‐ ly true for dry food stuff however there is no reason to refuse it for plant tissue where sound is produced mainly from cell wall breakdowns and it could cause the momentary force drip‐ ping down. It has been shown for dry food that acoustic and mechanical parameters related with saw like force profile could be used for sensory crispness measurement [15], thus pre‐ sumably in a future it will be the case also for fruits and vegetables.

The above calibration models for CAED were obtained with use of averaged values from 10 apples for the each calibration point. Taking into account that RMSEP value of the calibra‐ tion models is slightly less than 1, an error of prediction is not larger than ±1. Since descrip‐ tive sensory analysis uses the 10 grade scale, the PCR calibration models allow for classification of sensory attribute to one of the 5 grades. This is very satisfactory results tak‐ ing into account that the results obtained is less expensive and testing of the 10 apples lasts less than 10 minutes only. This means that instrumental evaluation of fruit texture with use of combination of sound-related descriptors and mechanical descriptors could replace soon sensory panels as it is faster, and – as is usually the case with technology – it is objective and does not suffer from fatigue.


**Table 5.** Performance statistics of linear regression models, multiple regression and principal component regression models for prediction sensory texture attributes of apples by CAED (after Zdunek et al [14]). Ncal – Number of samples used for calibration. Ntest – Number of samples used for validation, F-firmness, CAE – total AE counts, CV-cross validation, R2 – determination coefficient, RMSECV - root mean squared errors of cross validation or RMSEP - root mean squared error of prediction, RPD - ratio of prediction to deviation calculated as the ratio of standard deviation of validation data set to RMSECV or RMSEP. If the RPD was below 1.5 the model is not useful, and when the value was higher than 2, the model can predict quantitatively sensory attributes [16]

#### **Author details**

Artur Zdunek

diction is impossible in any case using this modelling approach. Crispness is slightly better predicted by total AE counts than by firmness when these individuals are taken for simple linear model whereas hardness is apparently better predicted by firmness than by acoustic variable. It is presumably due to different origins of the variables: sensory crispness is gov‐ erned mostly from auditory phenomena whereas sensory hardness from mechanical one. Table 5 presents also performance statistics of multiple regression models (MLR) where both firmness F and total AE counts were considered in the linear model. General improvement of models is observed in the case of each sensory attribute. Furthermore, multivariative principal components regression (PCR) models, where total AE counts and firmness are used as the predictors of a group of sensory variables, show remarkable improvement of cal‐ ibration performance comparing to linear regression and multiple regression models. Full cross validation (CV) in the PCR for showed that satisfactory prediction is possible in the case of hardness. The models allow for prediction also crispness and overall texture with slightly less accuracy. In the case of juiciness, successful prediction seems to be doubtful whereas mealiness prediction is impossible. Test set validation (TSV) method showed appa‐ rently better model performance in the case of crispness and slightly better in the case of juiciness whereas for the rest of sensory attributes performance from TSV method is worse that from CV method. In general both validation methods show satisfactory prediction of

The model improvement, when both acoustic and firmness are considered in calibration models, agrees with the hypothesis that crispness perception should be interpreted as coun‐ teraction of acoustic and mechanical phenomena. It is usually observed that firmer apples are also more crispy. In Fig. 8 it is visible that firmer apple has more jagged force-deforma‐ tion (FD) profile during puncturing whereas soft apple has more smoother one. It was ac‐ companied with higher AE counts at the each force dropping down. One can say that firm apple is also more brittle. The jaggedness of the FD is important from the point of view crispness because humans can detect loads of less than 0.1 N. Such interpretation is especial‐ ly true for dry food stuff however there is no reason to refuse it for plant tissue where sound is produced mainly from cell wall breakdowns and it could cause the momentary force drip‐ ping down. It has been shown for dry food that acoustic and mechanical parameters related with saw like force profile could be used for sensory crispness measurement [15], thus pre‐

The above calibration models for CAED were obtained with use of averaged values from 10 apples for the each calibration point. Taking into account that RMSEP value of the calibra‐ tion models is slightly less than 1, an error of prediction is not larger than ±1. Since descrip‐ tive sensory analysis uses the 10 grade scale, the PCR calibration models allow for classification of sensory attribute to one of the 5 grades. This is very satisfactory results tak‐ ing into account that the results obtained is less expensive and testing of the 10 apples lasts less than 10 minutes only. This means that instrumental evaluation of fruit texture with use of combination of sound-related descriptors and mechanical descriptors could replace soon sensory panels as it is faster, and – as is usually the case with technology – it is objective and

crispness and hardness from multivariative PCR calibration models.

sumably in a future it will be the case also for fruits and vegetables.

does not suffer from fatigue.

198 Acoustic Emission - Research and Applications

Address all correspondence to: a.zdunek@ipan.lublin.pl

Institute of Agrophysics, Polish Academy of Sciences, Lublin, Poland

#### **References**

[1] Drake BK. Food Crushing Sounds: An Introductory Study, Journal of Food Science 1963; 28, 233-241.

[16] Saeys W, Mouazen AM, Ramon H. Potential for Onsite and Online Analysis of Pig Manure Using Visible and Near Infrared Reflectance Spectroscopy. Biosystems Engi‐

Application of Acoustic Emission for Quality Evaluation of Fruits and Vegetables

http://dx.doi.org/10.5772/53985

201

neering 2005; 91 (4), 393–402.


[16] Saeys W, Mouazen AM, Ramon H. Potential for Onsite and Online Analysis of Pig Manure Using Visible and Near Infrared Reflectance Spectroscopy. Biosystems Engi‐ neering 2005; 91 (4), 393–402.

**References**

1963; 28, 233-241.

200 Acoustic Emission - Research and Applications

1997; 11 (3) , 223-227.

457-473.

Pascal, Warsaw; 1984. (in Polish).

mer. Polymer Testing 1999; 9, 15-26.

Academic Press, London; 2002.

[1] Drake BK. Food Crushing Sounds: An Introductory Study, Journal of Food Science

[2] Chen J, Karlsson C, Povey M. Acoustic envelope detector for crispness assessment of

[3] Vickers Z. Crackliness: Relationships of Auditory Judgments to Tactile Judgments and Instrumental Acoustical Measurements. Journal of Texture Studies; 1983, 15, 49–58. [4] Christensen CM, Vickers ZM. Relationships of Chewing Sounds to Judgments of

[5] Fillion L, Kilcast D. Consumer Perception of Crispness and Crunchiness an Fruits

[6] Zdunek A, Konstankiewicz K. Acoustic emission as a method for the detection of fractures in the plant tissue caused by the external forces International Agrophysics

[7] Malecki I, Ranachowski J. Acoustic emission, sources, methods, applications. Biuro

[8] Zdunek A, Konstankiewicz K. Acoustic Emission in Investigation of Plant Tissue Mi‐

[9] Alvarez MD, Saunders DEJ, Vincent JFV, Jeronimidis G. An engineering method to evaluate the crisp texture of fruit and vegetables. Journal of Texture Studies 2000; 31,

[10] Williams JG, Cawood MJ. European Group on Fracture: Kc and Gc Methods for Poly‐

[11] Bourne MC. Food Texture and Viscosity: Concept and Measurement. Second Edition.

[12] Taniwaki M, Hanada T, Sakurai N. Device for Acoustic Measurement of Food Tex‐ ture Using a Piezoelectric Sensor. Food Research International 2006; 39, 1099–1105.

[13] Taniwaki M, Hanada T, Sakurai N. Postharvest Quality Evaluation of "Fuyu" and "Taishuu" Persimmons Using a Nondestructive Vibrational Method and an Acoustic

[14] Zdunek A, Cybulska J, Konopacka D, Rutkowski K. Evaluation of Apple Texture with Contact Acoustic Emission Detector: a Study on Performance of Calibration

[15] Luyten H, Plijter JJ, Van Vliet T. Crispy/Crunchy Crusts of Cellular Solid Foods: a Literature Review with Discussion. Journal of Texture Studies 2004; 35, 445–492.

Vibration Technique, Postharvest Biology and Technology 2009; 51 80–85.

Models. Journal of Food Engineering 2011; 106, 80-87.

biscuits. Journal of Texture Studies 2005; 36, 139-156.

Food Crispness. Journal of Food Science 1981; 46, 574.

and Vegetables. Food Quality and Preference 2002; 13, 23–29.

cro-Cracking. Transaction of the. ASAE 2004; 47(4), 1171-1177.

**Chapter 9**

**Otoacoustic Emissions**

Maria Patrizia Orlando

http://dx.doi.org/10.5772/55254

**1. Introduction**

**2. Inside cochlea**

Giovanna Zimatore, Domenico Stanzial and

Additional information is available at the end of the chapter

nisms, thus like the efficiency of the middle ear transmission chain.

In this chapter, we present a very special kind of acoustic emissions, coming from inside the cochlea and generated along the basilar membrane by the electro-motile (active) vibrations of outer hair cells of the organ of Corti. They are called OtoAcoustic Emissions (OAE) and are detected in the ear canal by means of microphones which are usually assembled as part of earphone-like probes. Since their discovery by Kemp [1], the study of otoacoustic emissions has become an hot topic both in basic and clinical research, due to OAE unique feature to inform directly about the normal and pathological functions of the cochlear receptors mecha‐

From the signal point of view, the most interesting characteristics of OAE is their broad band frequency spectrum so rousing also a new interest for broad band ear immittance measure‐ ments and interpretation [2]. In this respect, this chapter will focus the reader's attention on two very innovative topics to improve objective and non-invasive audiological tests: the potentiality of Transient-Evoked otoacoustic emissions (TEOAE) to detect hearing impairment and the availability of a new microprobe able to capture directly both the pressure and velocity acoustic signals in the ear canal so allowing the direct measurement of ear immittance.

The cochlea is located in the inner ear, consisting of the front labyrinth and rear labyrinth, the latter having peripheral vestibular formations. The cochlea has quite a complex structure, just as complex as the Organ of Corti, contained inside the cochlea that with its neuro-epitherial hair cells makes up the first mechanical-electrical transformation stage of the sound impulse;

> © 2013 Zimatore et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Zimatore et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **Chapter 9**

## **Otoacoustic Emissions**

Giovanna Zimatore, Domenico Stanzial and Maria Patrizia Orlando

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55254

#### **1. Introduction**

In this chapter, we present a very special kind of acoustic emissions, coming from inside the cochlea and generated along the basilar membrane by the electro-motile (active) vibrations of outer hair cells of the organ of Corti. They are called OtoAcoustic Emissions (OAE) and are detected in the ear canal by means of microphones which are usually assembled as part of earphone-like probes. Since their discovery by Kemp [1], the study of otoacoustic emissions has become an hot topic both in basic and clinical research, due to OAE unique feature to inform directly about the normal and pathological functions of the cochlear receptors mecha‐ nisms, thus like the efficiency of the middle ear transmission chain.

From the signal point of view, the most interesting characteristics of OAE is their broad band frequency spectrum so rousing also a new interest for broad band ear immittance measure‐ ments and interpretation [2]. In this respect, this chapter will focus the reader's attention on two very innovative topics to improve objective and non-invasive audiological tests: the potentiality of Transient-Evoked otoacoustic emissions (TEOAE) to detect hearing impairment and the availability of a new microprobe able to capture directly both the pressure and velocity acoustic signals in the ear canal so allowing the direct measurement of ear immittance.

#### **2. Inside cochlea**

The cochlea is located in the inner ear, consisting of the front labyrinth and rear labyrinth, the latter having peripheral vestibular formations. The cochlea has quite a complex structure, just as complex as the Organ of Corti, contained inside the cochlea that with its neuro-epitherial hair cells makes up the first mechanical-electrical transformation stage of the sound impulse;

© 2013 Zimatore et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Zimatore et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

it permits the stimulation of the afferent neural structures and the transmission of the infor‐ mation contained in the sound input through the acoustic canals right up to the cerebral cortex.

produces along the Basilar Membrane will be evident at different points according to the frequency of the topical tone sound. The result of such mechanical modifications by the Basilar Membrane and hairs is the releasing of neuro-receptor neurohumours located in their synaptic vesicles inside the Hair Cells and so generating the bio-electrical impulse. The Basilar Mem‐ brane has different physical and elastic properties along the cochlea spiral from its base, through the intermediate part, to the apex. Even resonance properties vary along the cochlea. This makes one part of the Basilar Membrane resonate and deform according to the frequency of the sound rather than another part of the Basilar Membrane and consequently the Organ of Corti, the activating groups of Hair Cells and their nerve fibres based on the different fre‐ quencies contained in the sound. The part of the Basilar Membrane that is most sensitive to low frequency sounds is the apex of the cochlea whilst the part most sensitive to high fre‐

Otoacoustic Emissions

205

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The first neuron of the auditory system is contained in the Corti Gland inside the cochlea where we find T cells whose peripheral extensions come from the Internal Hair Cells whilst the central extensions together make up the eighth cranial nerve and connect to the pontini bulb centres. It is important to keep in mind, according to the most recent theories, that inside the Organ of Corti at the External Hair Cell level there is an important active magnifying process of the signal that produces significant amplification, definition and resolution in the frequency of the sound inputs and a notable refinement of the auditory threshold. The fine longitudinal and transversal motility of the Outer Hair Cells, both spontaneous types and those stimulated externally, motility modulated by the efferent olivocochlea system, are the basis of such important functions. From this it can be deduced that a loss of Outer Hair Cells would produce a series of auditory problems more critical and complex in respect of damage to the Inner Hair Cells. Hearing loss (reduction of auditory function) connected to changes in analysis, periph‐ eral translation and conduction of apparatus is defined as neurosensory and gives way to distortions in frequency, intensity such as recruitment, a phenomenon that distorts the

subjective sound intensity (loudness), in phase, exertion and auditory conformation.

auditory canal: the Otoacoustic Emissions.

**3. What OAE are**

Outer Hair Cells are cells that belong to and are controlled by the efferent system more than sensor cells. They are more sensitive to auditory stimulation in respect of Internal Hair Cells which are anatomically connected to the afferent or sensorial system as previously stated. The particular sensitivity is mainly mechanical in nature and Is connected to 1) the presence of direct tectorial hair connections between stereohairs and the Tectorial Membrane and 2) their "active" vibratory motility, electrically and chemically mediated, that translates into acoustic phenomenon that can be picked up and recorded by a microphone positioned in the external

The discovery of otoemissions is attributed to the English physics professor David Kemp at the end of the '70s. He is merited with first putting forward the idea and then introducing clinical diagnosis using investigative methodologies capable of non-invasive exploration, in

quencies is the widest part of the coil that is the base of the cochlea.

**Figure 1.** The Organ of Corti The organ of Corti is attached to the basilar membrane on the side of the aqueous fluid of the scala media. It is comprised of the supporting cells for the hair cells, the hair cells themselves, and the tectorial membrane (TM).

The Organ of Corti is made up of the Basilar Membrane, hair cells, support cells, Deiter, Hensen and Claudius cells, and the Tectorial Membrane. The hair cells can be divided according to their position in respect of the cochlea canal, whether outer or inner. The outer cells are more numerous and are placed along three lines; their hairs contact directly with the Tectorial Membrane and are very sensitive, are mainly stimulated by the efferent medial olivocochlea system of control. Acetylcholine (Ach) is their principal chemical mediator. The internal hair cells are arranged in a single line, don't have direct contact with the Tectorial Membrane, are less vulnerable and are supplied by afferent medial olivocochlea nerves, whose first nerve cell is within the Organ of Corti, itself enclosed within the bony labyrinth inside the cochlea. The glutamate is the main neuro–transmitter of the Internal Hair Cells. Given their afferent innervations they make up the actual sensorial cells.

The mechanical-electrical transduction of the cochlea takes place through a series of biochemical and bio-mechanical mechanisms. The sound impulse is transmitted from the movement of the stirrup bone on the oval window to the endolymph fluid creating a defor‐ mation of the Basilar Membrane on which the Organ of Corti rests with its hair cells that also create a deformation of the auditory cells who in turn are partially in direct contact with the Tectorial Membrane and so generating a deformation wave in the Basilar Membrane (travel‐ ling wave) as a result of the sound wave. The amount of deformation that the travelling wave produces along the Basilar Membrane will be evident at different points according to the frequency of the topical tone sound. The result of such mechanical modifications by the Basilar Membrane and hairs is the releasing of neuro-receptor neurohumours located in their synaptic vesicles inside the Hair Cells and so generating the bio-electrical impulse. The Basilar Mem‐ brane has different physical and elastic properties along the cochlea spiral from its base, through the intermediate part, to the apex. Even resonance properties vary along the cochlea. This makes one part of the Basilar Membrane resonate and deform according to the frequency of the sound rather than another part of the Basilar Membrane and consequently the Organ of Corti, the activating groups of Hair Cells and their nerve fibres based on the different fre‐ quencies contained in the sound. The part of the Basilar Membrane that is most sensitive to low frequency sounds is the apex of the cochlea whilst the part most sensitive to high fre‐ quencies is the widest part of the coil that is the base of the cochlea.

The first neuron of the auditory system is contained in the Corti Gland inside the cochlea where we find T cells whose peripheral extensions come from the Internal Hair Cells whilst the central extensions together make up the eighth cranial nerve and connect to the pontini bulb centres. It is important to keep in mind, according to the most recent theories, that inside the Organ of Corti at the External Hair Cell level there is an important active magnifying process of the signal that produces significant amplification, definition and resolution in the frequency of the sound inputs and a notable refinement of the auditory threshold. The fine longitudinal and transversal motility of the Outer Hair Cells, both spontaneous types and those stimulated externally, motility modulated by the efferent olivocochlea system, are the basis of such important functions. From this it can be deduced that a loss of Outer Hair Cells would produce a series of auditory problems more critical and complex in respect of damage to the Inner Hair Cells. Hearing loss (reduction of auditory function) connected to changes in analysis, periph‐ eral translation and conduction of apparatus is defined as neurosensory and gives way to distortions in frequency, intensity such as recruitment, a phenomenon that distorts the subjective sound intensity (loudness), in phase, exertion and auditory conformation.

Outer Hair Cells are cells that belong to and are controlled by the efferent system more than sensor cells. They are more sensitive to auditory stimulation in respect of Internal Hair Cells which are anatomically connected to the afferent or sensorial system as previously stated. The particular sensitivity is mainly mechanical in nature and Is connected to 1) the presence of direct tectorial hair connections between stereohairs and the Tectorial Membrane and 2) their "active" vibratory motility, electrically and chemically mediated, that translates into acoustic phenomenon that can be picked up and recorded by a microphone positioned in the external auditory canal: the Otoacoustic Emissions.

#### **3. What OAE are**

it permits the stimulation of the afferent neural structures and the transmission of the infor‐ mation contained in the sound input through the acoustic canals right up to the cerebral cortex.

**Figure 1.** The Organ of Corti The organ of Corti is attached to the basilar membrane on the side of the aqueous fluid of the scala media. It is comprised of the supporting cells for the hair cells, the hair cells themselves, and the tectorial

The Organ of Corti is made up of the Basilar Membrane, hair cells, support cells, Deiter, Hensen and Claudius cells, and the Tectorial Membrane. The hair cells can be divided according to their position in respect of the cochlea canal, whether outer or inner. The outer cells are more numerous and are placed along three lines; their hairs contact directly with the Tectorial Membrane and are very sensitive, are mainly stimulated by the efferent medial olivocochlea system of control. Acetylcholine (Ach) is their principal chemical mediator. The internal hair cells are arranged in a single line, don't have direct contact with the Tectorial Membrane, are less vulnerable and are supplied by afferent medial olivocochlea nerves, whose first nerve cell is within the Organ of Corti, itself enclosed within the bony labyrinth inside the cochlea. The glutamate is the main neuro–transmitter of the Internal Hair Cells. Given their afferent

The mechanical-electrical transduction of the cochlea takes place through a series of biochemical and bio-mechanical mechanisms. The sound impulse is transmitted from the movement of the stirrup bone on the oval window to the endolymph fluid creating a defor‐ mation of the Basilar Membrane on which the Organ of Corti rests with its hair cells that also create a deformation of the auditory cells who in turn are partially in direct contact with the Tectorial Membrane and so generating a deformation wave in the Basilar Membrane (travel‐ ling wave) as a result of the sound wave. The amount of deformation that the travelling wave

innervations they make up the actual sensorial cells.

membrane (TM).

204 Acoustic Emission - Research and Applications

The discovery of otoemissions is attributed to the English physics professor David Kemp at the end of the '70s. He is merited with first putting forward the idea and then introducing clinical diagnosis using investigative methodologies capable of non-invasive exploration, in humans, the Organ of Corti functions and in particular the Outer Hair Cells. The basis of this methodology has produced a series of new and surprising evidence regarding the cochlea physiology that integrates, contradicts and supersedes the consolidated theories of von Békésy, Nobel Prize winner in 1960.

(milliseconds), as well as by a spectrogram that traces the size and frequency of the response. The DPOAE instead operates by way of sending a pair of pure tones (F1 and F2), with very small value frequency differences between them for example F1=1000 Hz, F2=1220 Hz, a ratio of F2/F1 = 1.22. The two tones, so-called primary tones, give rise to distortions in the cochlea deriving from their combination. The phenomenon of the combination of tones is mostly connected to the peripheral processing mechanisms of the signal which is still not wholly understood but that resides in the internal ear and in particular is connected to the active processes of the cochlea. So if two tones of differing frequency are sent simultaneously, the ear might perceive one or more tones superimposed that are the sum of the two tones or else are the difference (simple, cubic, quadratic, etc.) of the two primary tones. The response traces the form of a DP-gram, showing the extent of the response derived from the frequency of the

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Nowadays the major diagnostic clinical function is mostly engaged in the TEOAE and DPOAE being the spontaneous emissions less subject to interpretation despite having a notable scientific interest. Dedicated software systems permit the execution of a rapid measurement statistically adapted to the cochlea response. As regards the DPOAE it is interesting to note that it establishes a modern method to survey one of the more characteristic psychoacoustic phenomena: combination tones. The study of DPOAE in particular allows the design of cochlea responses in an audiometric-like way, frequency by frequency, on a graph that shows on the vertical axis the frequencies of stimulation and on the horizontal axis the intensity levels of the received Otoacoustic emissions showing immediately if the audiological threshold is within

The operating range is important in identifying the dysfunction of the cochlea in Ménière's disease, in evaluating damage from noise, in ototoxic type changes, in the study of some genetic and immunological cochlea alterations, in the differential cochlea diagnosis against retro cochlea diagnosis and the identification of new pathologies such as Auditory Neuropathy. Finally, the range of neonatal auditory screening establishes the most sensitive and specific means of recognising premature infantile deafness. Auditory screening is carried out at birth before the new-born baby is discharged from hospital normally the second day after birth and, given the simplicity and speed of testing, is the best method for definitive diagnosis or alerting and preparing for further diagnosis and rehabilitative therapy within a few months and before the child's first birthday, a period of great neuroplastic and linguistic activity. It's therefore possible to control and limit the damage from auditory sensorial deprivation, language

OAEs provide objectivity and greater accuracy, representing a non invasive tool for the assessment of OHC and the functionality of the cochlear amplifier, as demonstrated by experimental and clinical studies [3-5]; furthermore, the cochlear effects of exogenous factors, such as ototoxic drugs, solvents and high-level sound exposure [6-8], can be monitored by OAE. It has been suggested that OAEs may provide early indication of cochlear damage before evidence for NIHL appears in pure-tone audiometry [9-10]. Recently, TEOAE have been used to study in tinnitus subjects with normal hearing to assess whether a minor cochlear or efferent

primary tones.

normal limits or not.

disorders, communication and behaviour disorders.

dysfunction might play a role in tinnitus [11].

The direct contact between the stereo cilias of the Outer Hair Cells and the Tectorial Membrane create mechanical-electrical type reactions that transfer to the entire cell connected by ATP (Adenosin-TriPhosfate). The typical cytoskeleton-like network of muscle (actina–miosina) of which the cell is made, makes use of the electric charge originated at the level of the stereo cilia and moves either slowly or rapidly. These movements are modulated and regulated by the medial olivocochlea system, a true servo-system of control through various synaptic neurohumours and in particular Acetylcholine. The function of Outer Hair Cells is fundamental in conferring on our hearing the elevated threshold characteristics, the increased dynamics between minimum audible threshold and the perceptible maximum and frequency selectiveness.

A cochlea system with dysfunctional Outer Hair Cells rapidly loses these properties even if in theory the Inner Hair Cells are healthy. The information received mechanically from the Outer Hair Cells is transmitted in electric form as well as in mechanical form to the Inner Hair Cells and so to our proper sensory auditory system. To stress again, the Outer Hair Cells are particularly vulnerable, their high characteristic sensitivity to which are connected elevated bioenergetic and metabolic requests such that any cochlea noxae that is infected, toxic, traumatised or suffering from a metabolic disorder can bring about a lesion and become apparent prematurely. The study of Otoacoustic emissions appears significant and effective in the majority of auditory problems of peripheral receptors.

The otoacoustic emissions (OAE) are recorded by a particular probe positioned in the external auditory canal. If it is necessary to create responses the probe, other than being a receiver that records the emissions from the cochlea, contains a transducer capable of sending stimuli to the cochlea. These days it is possible to study the OAE mainly in one of three ways:


Apart from the SOAE method of recording whose clinical value is unfortunately less, we shall focus on the TEOAE and DPOAE recording methods. The first method involves sending a series of clicks from a probe and recording the acoustic response from the hair cells. The acoustic response is normally represented graphically by oscillations based on a time period (milliseconds), as well as by a spectrogram that traces the size and frequency of the response. The DPOAE instead operates by way of sending a pair of pure tones (F1 and F2), with very small value frequency differences between them for example F1=1000 Hz, F2=1220 Hz, a ratio of F2/F1 = 1.22. The two tones, so-called primary tones, give rise to distortions in the cochlea deriving from their combination. The phenomenon of the combination of tones is mostly connected to the peripheral processing mechanisms of the signal which is still not wholly understood but that resides in the internal ear and in particular is connected to the active processes of the cochlea. So if two tones of differing frequency are sent simultaneously, the ear might perceive one or more tones superimposed that are the sum of the two tones or else are the difference (simple, cubic, quadratic, etc.) of the two primary tones. The response traces the form of a DP-gram, showing the extent of the response derived from the frequency of the primary tones.

humans, the Organ of Corti functions and in particular the Outer Hair Cells. The basis of this methodology has produced a series of new and surprising evidence regarding the cochlea physiology that integrates, contradicts and supersedes the consolidated theories of von Békésy,

The direct contact between the stereo cilias of the Outer Hair Cells and the Tectorial Membrane create mechanical-electrical type reactions that transfer to the entire cell connected by ATP (Adenosin-TriPhosfate). The typical cytoskeleton-like network of muscle (actina–miosina) of which the cell is made, makes use of the electric charge originated at the level of the stereo cilia and moves either slowly or rapidly. These movements are modulated and regulated by the medial olivocochlea system, a true servo-system of control through various synaptic neurohumours and in particular Acetylcholine. The function of Outer Hair Cells is fundamental in conferring on our hearing the elevated threshold characteristics, the increased dynamics between minimum audible threshold and the

A cochlea system with dysfunctional Outer Hair Cells rapidly loses these properties even if in theory the Inner Hair Cells are healthy. The information received mechanically from the Outer Hair Cells is transmitted in electric form as well as in mechanical form to the Inner Hair Cells and so to our proper sensory auditory system. To stress again, the Outer Hair Cells are particularly vulnerable, their high characteristic sensitivity to which are connected elevated bioenergetic and metabolic requests such that any cochlea noxae that is infected, toxic, traumatised or suffering from a metabolic disorder can bring about a lesion and become apparent prematurely. The study of Otoacoustic emissions appears significant and effective

The otoacoustic emissions (OAE) are recorded by a particular probe positioned in the external auditory canal. If it is necessary to create responses the probe, other than being a receiver that records the emissions from the cochlea, contains a transducer capable of sending stimuli to the

**1.** Recording the spontaneous emissions produced by the cochlea in the absence of any acoustic stimulus. Such emissions are called 'Spontaneous Otoacoustic Emissions'

**2.** Recording the emissions produced inside the cochlea through the sending of temporary acoustic stimuli, such as clicks, that are able to involve synchronously and globally a large number of the acoustic cells from the base to the apex. These emissions are known as

**3.** Cochlea emissions created by pairs of tonal stimuli of differing frequency for intermodu‐ lation phenomena, so-called 'Distortion Product Otoacoustic Emissions' (DPOAE). Apart from the SOAE method of recording whose clinical value is unfortunately less, we shall focus on the TEOAE and DPOAE recording methods. The first method involves sending a series of clicks from a probe and recording the acoustic response from the hair cells. The acoustic response is normally represented graphically by oscillations based on a time period

cochlea. These days it is possible to study the OAE mainly in one of three ways:

Nobel Prize winner in 1960.

206 Acoustic Emission - Research and Applications

(SOAE).

perceptible maximum and frequency selectiveness.

in the majority of auditory problems of peripheral receptors.

'Transient Otoacoustic Emissions' (TEOAE).

Nowadays the major diagnostic clinical function is mostly engaged in the TEOAE and DPOAE being the spontaneous emissions less subject to interpretation despite having a notable scientific interest. Dedicated software systems permit the execution of a rapid measurement statistically adapted to the cochlea response. As regards the DPOAE it is interesting to note that it establishes a modern method to survey one of the more characteristic psychoacoustic phenomena: combination tones. The study of DPOAE in particular allows the design of cochlea responses in an audiometric-like way, frequency by frequency, on a graph that shows on the vertical axis the frequencies of stimulation and on the horizontal axis the intensity levels of the received Otoacoustic emissions showing immediately if the audiological threshold is within normal limits or not.

The operating range is important in identifying the dysfunction of the cochlea in Ménière's disease, in evaluating damage from noise, in ototoxic type changes, in the study of some genetic and immunological cochlea alterations, in the differential cochlea diagnosis against retro cochlea diagnosis and the identification of new pathologies such as Auditory Neuropathy. Finally, the range of neonatal auditory screening establishes the most sensitive and specific means of recognising premature infantile deafness. Auditory screening is carried out at birth before the new-born baby is discharged from hospital normally the second day after birth and, given the simplicity and speed of testing, is the best method for definitive diagnosis or alerting and preparing for further diagnosis and rehabilitative therapy within a few months and before the child's first birthday, a period of great neuroplastic and linguistic activity. It's therefore possible to control and limit the damage from auditory sensorial deprivation, language disorders, communication and behaviour disorders.

OAEs provide objectivity and greater accuracy, representing a non invasive tool for the assessment of OHC and the functionality of the cochlear amplifier, as demonstrated by experimental and clinical studies [3-5]; furthermore, the cochlear effects of exogenous factors, such as ototoxic drugs, solvents and high-level sound exposure [6-8], can be monitored by OAE. It has been suggested that OAEs may provide early indication of cochlear damage before evidence for NIHL appears in pure-tone audiometry [9-10]. Recently, TEOAE have been used to study in tinnitus subjects with normal hearing to assess whether a minor cochlear or efferent dysfunction might play a role in tinnitus [11].

One of the few limitations of OAE is related to the extent of hearing loss that we can explore: infact, already for cochlear hearing loss above 50 dB the OAE, just because otoacoustic emissions are produced by the activation of CCE, are no longer evoked.

**5. Broad band measurement of ear immittance and perspective for**

velocity (p-v) micro-probes is nowadays made available (see Figure 3).

The most innovative application of micro-electro-mechanical systems (MEMS) technology to acoustic sensors is the manufacturing of thermo-acoustic velocimeters based on the two-wire anemometric transduction principle. These new sensors allow to capture directly the acoustic particle velocity signal v, and thus, by coupling and assembling them with standard micro‐ phones which are instead sensitive to the pressure signal p, a new generation of pressure-

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**Figure 3.** A p-v sound intensity micro-probe consists in the assembly of a miniaturized pressure microphone and a MEMS technology based velocimeter in a single measurement system. While the pressure sensor is a standard electret one, the velocity signal is transduced thanks to the differential anemometric principle applied to two closely spaced heated wires 10 μm apart, 1mm long and 5μm large suspended in parallel in order to form a bridge. The wire compo‐ sition is 200 nm platinum (Pt) on a silicon nitride (Si3N4) substratum 150 nm thick. The captured pressure and velocity analog signals are conditioned through a common probe input and handled in output as two separate voltage sig‐

nals. (The commercial system shown in the figure is by courtesy of Microflown®: www.microflown.com).

**improving TEOAE detection**

#### **4. TEOAE recording**

To record the TEOAE signals the Otodynamic Analyzer (ILO92, Otodynamics Ltd, Hatfield, UnitedKingdom),waswidelyused,byinsertingaSGS-typegeneralpurposeTEOAEprobe into the external ear canal. The TEOAE recordings were carried out in a standard hospital room, corresponding to the usual clinical setting for these measurements. The automated differen‐ tial non-linear test paradigm was used: the stimulus was characterized by a train of four clicks, three with the same amplitude and polarity, followed by a fourth one with a 3-fold amplitude and opposite polarity with respect to the preceding ones. The 80 μs clicks presented at 50/s were 75–85 dB SPL. The responses were obtained evaluating an average among 260 stimuli trains (1040 clicks) stored into two different buffers (A and B) for a total of 2080 clicks. The value of the automatically computed correlation or reproducibility between the two obtained waveforms (AandB)ofanOAEsignalisnamedReproorwholewaveformreproducibility(REPRO)(Pearson correlation coefficient \*100) (see in Figure 2, on the right, Repro=99%).

**Figure 2.** TEOAE signals (ILO92, Otodynamics Ltd)

### **5. Broad band measurement of ear immittance and perspective for improving TEOAE detection**

One of the few limitations of OAE is related to the extent of hearing loss that we can explore: infact, already for cochlear hearing loss above 50 dB the OAE, just because otoacoustic

To record the TEOAE signals the Otodynamic Analyzer (ILO92, Otodynamics Ltd, Hatfield, UnitedKingdom),waswidelyused,byinsertingaSGS-typegeneralpurposeTEOAEprobe into the external ear canal. The TEOAE recordings were carried out in a standard hospital room, corresponding to the usual clinical setting for these measurements. The automated differen‐ tial non-linear test paradigm was used: the stimulus was characterized by a train of four clicks, three with the same amplitude and polarity, followed by a fourth one with a 3-fold amplitude and opposite polarity with respect to the preceding ones. The 80 μs clicks presented at 50/s were 75–85 dB SPL. The responses were obtained evaluating an average among 260 stimuli trains (1040 clicks) stored into two different buffers (A and B) for a total of 2080 clicks. The value of the automatically computed correlation or reproducibility between the two obtained waveforms (AandB)ofanOAEsignalisnamedReproorwholewaveformreproducibility(REPRO)(Pearson

emissions are produced by the activation of CCE, are no longer evoked.

correlation coefficient \*100) (see in Figure 2, on the right, Repro=99%).

**Figure 2.** TEOAE signals (ILO92, Otodynamics Ltd)

**4. TEOAE recording**

208 Acoustic Emission - Research and Applications

The most innovative application of micro-electro-mechanical systems (MEMS) technology to acoustic sensors is the manufacturing of thermo-acoustic velocimeters based on the two-wire anemometric transduction principle. These new sensors allow to capture directly the acoustic particle velocity signal v, and thus, by coupling and assembling them with standard micro‐ phones which are instead sensitive to the pressure signal p, a new generation of pressurevelocity (p-v) micro-probes is nowadays made available (see Figure 3).

**Figure 3.** A p-v sound intensity micro-probe consists in the assembly of a miniaturized pressure microphone and a MEMS technology based velocimeter in a single measurement system. While the pressure sensor is a standard electret one, the velocity signal is transduced thanks to the differential anemometric principle applied to two closely spaced heated wires 10 μm apart, 1mm long and 5μm large suspended in parallel in order to form a bridge. The wire compo‐ sition is 200 nm platinum (Pt) on a silicon nitride (Si3N4) substratum 150 nm thick. The captured pressure and velocity analog signals are conditioned through a common probe input and handled in output as two separate voltage sig‐ nals. (The commercial system shown in the figure is by courtesy of Microflown®: www.microflown.com).

These micro-probes are clearly the ideal device for carrying out advanced direct measurements of the sound field energetic properties like sound intensity j=pv or acoustic impedance Z=p/v. To this aim, an accurate calibration procedure [12] is needed (see Figure 4).

**Figure 4.** The facility for sound intensity micro-probes calibration installed at the Larix Lab of the Physics Department of University of Ferrara consists in a 48 m long wave guide where a progressive plane wave is generated through a biconical loudspeaker in the [50, 10000] Hz frequency range. The p-v micro-probe under calibration is inserted at a dis‐ tance of 12.5 m from the source and is calibrated by comparison with a reference pressure microphone using the correction function Γ(ω) defined in Equation 13 of Ref. [12].

Of course, the calibration filtering process can be implemented at post-processing level but, with few engineering effort, the calibration filters can also be programmed at hardware level so making, in particular, the measurement of acoustic impedance, a completely automatic task. The technological innovation driven by MEMS application to acoustic sensors can be easily transferred to audiometric devices so transforming for instance a traditional tympanometric probe in a new setup for p-v tympanometry (see Figure 5). The main advantages of a p-v tympanometric test with respect to a traditional one are: a) the direct measure of ear immitance for more precise results; b) the test is completely non-invasive for static pressure external pumping is no longer necessary (p-v test measurements are performed in standard pressure conditions); c) the test produces wideband results in the typical frequency range of multi-tonal tympanometry [100, 1200] Hz; d) the p-v audiometer provides sophisticated sound energy analysis capability for hearing models validation (see Ref. [13]).

**Figure 5.** A p-v tympanometer is designed as a laptop based dual channel analyzer (lower left) able to record both the Impulse Responses (IRs) of pressure and velocity signals captured with the p-v tympanometric probe shown in the up‐ per part of the figure. Once the p-v IRs of the ear canal have been measured in atmospheric pressure condition (lower right), the system calculates the external/middle ear specific immittance and displays its magnitude in dB relative to

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As an example of results obtained with p-v tympanometry, wideband p-v tympanograms measured in dB for 26 left and right normal ears belonging to 13 voluntary students are

the frequency dependent baseline Y0 obtained by plugging up the probe.

clustered and reported in Figure 6.

These micro-probes are clearly the ideal device for carrying out advanced direct measurements of the sound field energetic properties like sound intensity j=pv or acoustic impedance Z=p/v.

**Figure 4.** The facility for sound intensity micro-probes calibration installed at the Larix Lab of the Physics Department of University of Ferrara consists in a 48 m long wave guide where a progressive plane wave is generated through a biconical loudspeaker in the [50, 10000] Hz frequency range. The p-v micro-probe under calibration is inserted at a dis‐ tance of 12.5 m from the source and is calibrated by comparison with a reference pressure microphone using the

Of course, the calibration filtering process can be implemented at post-processing level but, with few engineering effort, the calibration filters can also be programmed at hardware level so making, in particular, the measurement of acoustic impedance, a completely automatic task. The technological innovation driven by MEMS application to acoustic sensors can be easily transferred to audiometric devices so transforming for instance a traditional tympanometric probe in a new setup for p-v tympanometry (see Figure 5). The main advantages of a p-v tympanometric test with respect to a traditional one are: a) the direct measure of ear immitance for more precise results; b) the test is completely non-invasive for static pressure external pumping is no longer necessary (p-v test measurements are performed in standard pressure conditions); c) the test produces wideband results in the typical frequency range of multi-tonal tympanometry [100, 1200] Hz; d) the p-v audiometer provides sophisticated sound energy

correction function Γ(ω) defined in Equation 13 of Ref. [12].

analysis capability for hearing models validation (see Ref. [13]).

To this aim, an accurate calibration procedure [12] is needed (see Figure 4).

210 Acoustic Emission - Research and Applications

**Figure 5.** A p-v tympanometer is designed as a laptop based dual channel analyzer (lower left) able to record both the Impulse Responses (IRs) of pressure and velocity signals captured with the p-v tympanometric probe shown in the up‐ per part of the figure. Once the p-v IRs of the ear canal have been measured in atmospheric pressure condition (lower right), the system calculates the external/middle ear specific immittance and displays its magnitude in dB relative to the frequency dependent baseline Y0 obtained by plugging up the probe.

As an example of results obtained with p-v tympanometry, wideband p-v tympanograms measured in dB for 26 left and right normal ears belonging to 13 voluntary students are clustered and reported in Figure 6.

the embedding procedure allows to expand a mono-dimensional signal into multidimensional space, thus permitting the identification of fine peculiarities of the sampled series that in turn

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**•** RQA introduces few parameters descriptive of the global complexity of a signal, starting

**•** RQA descriptors are calculated on the basis of the number and location of dots in the

The results obtained demonstrate how proposed new global index can recognize even mild hearing loss and that an assessment of the severity of cochlear damage can be realized.

To build the recurrence plot, the time behavior of the original signal was represented by a series of 512 points equally spaced in time (e.g. {a1 a2 …. a 512} where ai represents the value of the signal corresponding to the i-th time position). Then, the series was arranged in successive columns (the columns number is defined by the "embedding dimension" param‐ eter, N), each-one obtained by applying a delay in time (lag parameter) to the original sequence,

Finally, the recurrence plot was built, drawing a black dot (named "recurrent point") in the represented space if the distance between the corresponding rows (the distance between the j-th and the (j+1)th row is of the embedding matrix was lower than a fixed value (radius). In the obtained plot, the horizontal and vertical axes represented the relative position of the 512 points into the TEOAE waveform. RQA descriptors were then calculated on the basis of the number and the location of dots in the recurrence plot. In particular, percent of recurrence (Rec) is the percentage of recurrence points in a recurrent plot; percent of determinism (Det) is the percentage of recurrence points which form diagonal lines and it indicates the degree of deterministic structure of the signal; entropy (Ent) is the Shannon entropy of the probability distribution of the diagonal line lengths and is linked to the richness of deterministic structure [16-17] (Zimatore et al. 2002 and 2003). The presence of horizontal and vertical lines in the recurrence plot shows that part of the considered signal matches closely with a sequence

In TEOAE analysis the delay in the embedding procedure (lag) is set to 1; the number of the embedding matrix columns (embedding dimension) is set to 10; and the cut-off distance (radius) is set to 15; to eliminate the initial linear ringing, the first 2.8 ms of the recorded TEOAE

Comparing Figure 7 and 8, it is clear that recurrence plots distinguish between normal hearing and impaired hearing TEOAEs especially in terms of a reduction in the deterministic structure. As a further step of the post-processing analysis, the well known Principal Component Analysis (PCA), was applied on the obtained RQA descriptors. Briefly, PCA is a common statistical technique which provides the possibility to reduce the starting data set dimension

farther along the time (for more details see http://www.recurrence-plot.tk).

are described by few global parameters allowing for a synthetic patient description.

**•** RQA dynamic features are independent from signal amplitude

RQA in summary:

recurrence plot

signals are excluded.

from what is called "recurrence plot"

in this way an "embedding matrix" was created.

**Figure 6.** Wideband p-v tympanograms measured in dB for 26 left and right normal ears belonging to 13 voluntary students. One clearly see that all tympanograms converges between -10 and -15 dB for the standard frequency of 226 Hz used in traditional tympanometry. The mean value found at -12.7 dB can thus be considered the "normal" value of the immitance magnitude measured by p-v tympanometry at 226 Hz.

As the primary data collected by the p-v tympanometry are basically the measurement of the pressure and velocity ear canal IRs, a completely new perspective also for OAE studies is also opened. Specifically for the TEOAEs which could be simply detected as the non-linear byproducts of DSP algorithms used in the ear-canal immitance function calculations.

#### **6. TEOAE post-processing analysis**

The Recurrence Quantification Analysis (RQA) and Principal Component Analysis (PCA) have been carried on TEOAE waveforms [14-17] (Zimatore et al. 2000, 2001 2002, 2003) to extract new descriptors that could enlighten an early diagnosis of hearing loss.

In the last few years, a new parameter has been introduced to analyse TEOAE, to improve the specificity of diagnostic tests and to reduce inter-subject variability. The work was concen‐ trated on the analysis of the TEOAE focusing on their dynamics by the Recurrence Quantifi‐ cation Analysis (RQA). RQA is a post-processing analysis that is extremely fit to non-stationary signals and represents a valid alternative to Wavelet analysis used by other researchers. In fact, the embedding procedure allows to expand a mono-dimensional signal into multidimensional space, thus permitting the identification of fine peculiarities of the sampled series that in turn are described by few global parameters allowing for a synthetic patient description.

RQA in summary:

**Figure 6.** Wideband p-v tympanograms measured in dB for 26 left and right normal ears belonging to 13 voluntary students. One clearly see that all tympanograms converges between -10 and -15 dB for the standard frequency of 226 Hz used in traditional tympanometry. The mean value found at -12.7 dB can thus be considered the "normal" value of

As the primary data collected by the p-v tympanometry are basically the measurement of the pressure and velocity ear canal IRs, a completely new perspective also for OAE studies is also opened. Specifically for the TEOAEs which could be simply detected as the non-linear

The Recurrence Quantification Analysis (RQA) and Principal Component Analysis (PCA) have been carried on TEOAE waveforms [14-17] (Zimatore et al. 2000, 2001 2002, 2003) to

In the last few years, a new parameter has been introduced to analyse TEOAE, to improve the specificity of diagnostic tests and to reduce inter-subject variability. The work was concen‐ trated on the analysis of the TEOAE focusing on their dynamics by the Recurrence Quantifi‐ cation Analysis (RQA). RQA is a post-processing analysis that is extremely fit to non-stationary signals and represents a valid alternative to Wavelet analysis used by other researchers. In fact,

byproducts of DSP algorithms used in the ear-canal immitance function calculations.

extract new descriptors that could enlighten an early diagnosis of hearing loss.

the immitance magnitude measured by p-v tympanometry at 226 Hz.

**6. TEOAE post-processing analysis**

212 Acoustic Emission - Research and Applications


The results obtained demonstrate how proposed new global index can recognize even mild hearing loss and that an assessment of the severity of cochlear damage can be realized.

To build the recurrence plot, the time behavior of the original signal was represented by a series of 512 points equally spaced in time (e.g. {a1 a2 …. a 512} where ai represents the value of the signal corresponding to the i-th time position). Then, the series was arranged in successive columns (the columns number is defined by the "embedding dimension" param‐ eter, N), each-one obtained by applying a delay in time (lag parameter) to the original sequence, in this way an "embedding matrix" was created.

Finally, the recurrence plot was built, drawing a black dot (named "recurrent point") in the represented space if the distance between the corresponding rows (the distance between the j-th and the (j+1)th row is of the embedding matrix was lower than a fixed value (radius). In the obtained plot, the horizontal and vertical axes represented the relative position of the 512 points into the TEOAE waveform. RQA descriptors were then calculated on the basis of the number and the location of dots in the recurrence plot. In particular, percent of recurrence (Rec) is the percentage of recurrence points in a recurrent plot; percent of determinism (Det) is the percentage of recurrence points which form diagonal lines and it indicates the degree of deterministic structure of the signal; entropy (Ent) is the Shannon entropy of the probability distribution of the diagonal line lengths and is linked to the richness of deterministic structure [16-17] (Zimatore et al. 2002 and 2003). The presence of horizontal and vertical lines in the recurrence plot shows that part of the considered signal matches closely with a sequence farther along the time (for more details see http://www.recurrence-plot.tk).

In TEOAE analysis the delay in the embedding procedure (lag) is set to 1; the number of the embedding matrix columns (embedding dimension) is set to 10; and the cut-off distance (radius) is set to 15; to eliminate the initial linear ringing, the first 2.8 ms of the recorded TEOAE signals are excluded.

Comparing Figure 7 and 8, it is clear that recurrence plots distinguish between normal hearing and impaired hearing TEOAEs especially in terms of a reduction in the deterministic structure.

As a further step of the post-processing analysis, the well known Principal Component Analysis (PCA), was applied on the obtained RQA descriptors. Briefly, PCA is a common statistical technique which provides the possibility to reduce the starting data set dimension

**Figure 7.** Recurrence plot (top) of a typical TEOAE recorded in a Normal ear (%Det=88.89) (bottom)

without consistent loss of information and with a separation of the different and independent features characterizing the data set. PCA describes the original data set with a lower number of new parameters named main components (PC1, PC2) which explain more than 90% of the total variability in the data set. Having, by construction, PC1 and PC2 zero mean and standard deviation equal to 1, if a set of TEOAE signals from normal ears are studied, 96% of them will fall within a circle centered in the origin of the PC1/PC2 plane, and with a radius equal to 2 (reference circle in figure 12). The PC1/PC2 plane is defined starting from a representative data set made by 118 signals measured from normal hearing subjects [18]. The representative data set was used to define the circle in the PC1/PC2 plane in which the majority of TEOAE signals recorded in normal hearing subjects will fall. Mathematically, the parameter RAD2D is defined in the PC1/PC2 plane as the Euclidean distance of one point representing a TEOAE signal from

**Figure 8.** Recurrence Plot (top) of a representative Impaired Hearing (IH) TEOAE waveform (bottom) (% Det = 62.89)

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The relation correlating the RAD2D obtained for all the measured signals with the entity of cochlear damage is tested. Specifically, RAD2D was evaluated for real TEOAEs by applying

Furthermore, the post-processing analysis proposed is useful in screening of adults, in longitu‐ dinal studies, in test to evaluate the efficacy of new pharmacological treatments, in conserva‐ tion program in presbycusis and in protection program in noise induced hearing losses.

Figure 10 illustrates REPRO plotted *vs* RAD2D considering 30 subjects from Florence area (Italy). The examined ears will be classified as normal hearing (NORM) or mild hearing losses

the same procedure as for simulated signals combining RQA and PCA techniques.

the plane origin.

**Figure 8.** Recurrence Plot (top) of a representative Impaired Hearing (IH) TEOAE waveform (bottom) (% Det = 62.89)

in the PC1/PC2 plane as the Euclidean distance of one point representing a TEOAE signal from the plane origin.

without consistent loss of information and with a separation of the different and independent features characterizing the data set. PCA describes the original data set with a lower number of new parameters named main components (PC1, PC2) which explain more than 90% of the total variability in the data set. Having, by construction, PC1 and PC2 zero mean and standard deviation equal to 1, if a set of TEOAE signals from normal ears are studied, 96% of them will fall within a circle centered in the origin of the PC1/PC2 plane, and with a radius equal to 2 (reference circle in figure 12). The PC1/PC2 plane is defined starting from a representative data set made by 118 signals measured from normal hearing subjects [18]. The representative data set was used to define the circle in the PC1/PC2 plane in which the majority of TEOAE signals recorded in normal hearing subjects will fall. Mathematically, the parameter RAD2D is defined

**Figure 7.** Recurrence plot (top) of a typical TEOAE recorded in a Normal ear (%Det=88.89) (bottom)

214 Acoustic Emission - Research and Applications

The relation correlating the RAD2D obtained for all the measured signals with the entity of cochlear damage is tested. Specifically, RAD2D was evaluated for real TEOAEs by applying the same procedure as for simulated signals combining RQA and PCA techniques.

Furthermore, the post-processing analysis proposed is useful in screening of adults, in longitu‐ dinal studies, in test to evaluate the efficacy of new pharmacological treatments, in conserva‐ tion program in presbycusis and in protection program in noise induced hearing losses.

Figure 10 illustrates REPRO plotted *vs* RAD2D considering 30 subjects from Florence area (Italy). The examined ears will be classified as normal hearing (NORM) or mild hearing losses

**Figure 9.** RAD2D is defined in the Principal Components plane as the Euclidean distance from the plane origin; the points representing the normal TEOAE signals fall in the yellow reference circle and TEOAE signals recorded form sub‐ jects with hearing losses fall outside.

The 8 points-signals that fall in the B area correspond to 8 different subjects: 6 are hunters or they shoot for hobby and 2 work often with tractors or lawn mowers. The combined use of the two global parameters REPRO and RAD2D can enlighten points corresponding to the subjects

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In this chapter the application of technique such as RQA is proposed because, it allows the quantification of the *fine-structure* of TEOAE signals without any *a priori* hypothesis and any data manipulation; moreover, the dynamical structure of signals can be investigated without

An electronic model of human hearing system is used to test and improve new hypothesis of cochlear mechanisms and to anatomically distinguish different contributions to ear patholo‐

An electronic model of human hearing system can be used to test and improve new hypothe‐ sis of cochlear mechanisms and to anatomically distinguish different contributions to ear

The considered ear model is directly inspired to the so called "travelling wave" representation of the cochlear function mechanism and is able to simulate the TEOAE responses; the electric model of the whole ear, originally introduced by Guiguère and Woodland [20-21] and used in

with high risk of environmental noise exposure.

**Figure 10.** REPRO *vs* RAD2D from RQA parameters of TEOAE signals

taking into account the signal-amplitude differences.

**7. TEAOA simulation by mechanistic model**

gies [18-19].

pathologies.

(MHL) or (severe) hearing losses (HL) ears according to their pure tone thresholds at 0.250, 0.500, 1, 2, 3, 4, 6 and 8 kHz. The three groups according to the maximum hearing threshold level are: NORM, with threshold <10 dB at all audiometric frequencies, MHL, with threshold <20 dB at all audiometric frequencies and >10 dB at least at one frequency, and HL, with threshold >20 dB at least at one frequency. In Figure 10 the HL patients (white circles), in the MHL patients (blue diamonds) and in NORM subjects (black diamonds): each point corre‐ sponds to the recorded TEOAE waveform. A very simple and immediate description is available by observing the areas identified by threshold of REPRO (the horizontal line at 70%) and of RAD2D (the vertical line at 1.78). The points above the horizontal line indicate pass signals. To the left side of the vertical line, the points indicate signals that fall inside the normality circle, that is pass signals;. the main result is illustrated in the right upward rectangle of Figure 9 where the ears that have both high REPRO and high RAD2D are shown: these points-signals indicate 8 ears (3 HL, 4 MHL and 1 NORM) screened as pass by REPRO but identified as "fail" by our TEOAE parameter (possible false-negative of ILO test).

**Figure 10.** REPRO *vs* RAD2D from RQA parameters of TEOAE signals

The 8 points-signals that fall in the B area correspond to 8 different subjects: 6 are hunters or they shoot for hobby and 2 work often with tractors or lawn mowers. The combined use of the two global parameters REPRO and RAD2D can enlighten points corresponding to the subjects with high risk of environmental noise exposure.

In this chapter the application of technique such as RQA is proposed because, it allows the quantification of the *fine-structure* of TEOAE signals without any *a priori* hypothesis and any data manipulation; moreover, the dynamical structure of signals can be investigated without taking into account the signal-amplitude differences.

### **7. TEAOA simulation by mechanistic model**

(MHL) or (severe) hearing losses (HL) ears according to their pure tone thresholds at 0.250, 0.500, 1, 2, 3, 4, 6 and 8 kHz. The three groups according to the maximum hearing threshold level are: NORM, with threshold <10 dB at all audiometric frequencies, MHL, with threshold <20 dB at all audiometric frequencies and >10 dB at least at one frequency, and HL, with threshold >20 dB at least at one frequency. In Figure 10 the HL patients (white circles), in the MHL patients (blue diamonds) and in NORM subjects (black diamonds): each point corre‐ sponds to the recorded TEOAE waveform. A very simple and immediate description is available by observing the areas identified by threshold of REPRO (the horizontal line at 70%) and of RAD2D (the vertical line at 1.78). The points above the horizontal line indicate pass signals. To the left side of the vertical line, the points indicate signals that fall inside the normality circle, that is pass signals;. the main result is illustrated in the right upward rectangle of Figure 9 where the ears that have both high REPRO and high RAD2D are shown: these points-signals indicate 8 ears (3 HL, 4 MHL and 1 NORM) screened as pass by REPRO but

**Figure 9.** RAD2D is defined in the Principal Components plane as the Euclidean distance from the plane origin; the points representing the normal TEOAE signals fall in the yellow reference circle and TEOAE signals recorded form sub‐

jects with hearing losses fall outside.

216 Acoustic Emission - Research and Applications

identified as "fail" by our TEOAE parameter (possible false-negative of ILO test).

An electronic model of human hearing system is used to test and improve new hypothesis of cochlear mechanisms and to anatomically distinguish different contributions to ear patholo‐ gies [18-19].

An electronic model of human hearing system can be used to test and improve new hypothe‐ sis of cochlear mechanisms and to anatomically distinguish different contributions to ear pathologies.

The considered ear model is directly inspired to the so called "travelling wave" representation of the cochlear function mechanism and is able to simulate the TEOAE responses; the electric model of the whole ear, originally introduced by Guiguère and Woodland [20-21] and used in TEOAEs analysis [15, 18, 22], has been implemented into PSpice®. PSpice® is a standard electrical simulation tool for dc, transient and ac analyses [23] (see Figure 11). The input circuit can be defined by using a graphical interface or by compiling a list representing the circuit topology. The outputs of the system are current and voltage values within the circuit which can be displayed in both tabular and graphical formats. PSpice® has been already used to study an electric model of the cochlea [24] due to the possibility to relate the model parameters to physical and physiological issues. In [24], the used lumped parameter model is entirely passive, made of a resistive network combined with two capacitances in order to model the Reissner's membrane and the OHC in the Corti Organ.

To verify the hypothesis that TEOAE are strongly modulated by the middle ear [17], some elements in the middle ear section were varied according to the experimental study of Avan and colleagues [4]. The first change is the addition of a stapes capacitor (*C st*) to the middle-ear section of the circuit, as already considered by [20] Giguère and Woodland (1994a) and by [25]. When *C st* has a large value, its impedance is small, corresponding to small tension in the stapedius muscle (*C st* equal to infinity corresponds to no stiffness in the resting condition). Conversely, when *C st* is small, its impedance is large, corresponding to high muscle tension. Then, changes in the tympanic membrane stiffness (*C <sup>0</sup>*, *C d1*), to account for changes in the middle ear pressure, and in the tympanic membrane mass (*L <sup>0</sup>*, *L <sup>d</sup>*), to simulate an additive mass, have been considered [4]. Furthermore, a dead cochlea condition has been simulated by

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The role of middle ear effects is a hot topic in the OAE field, and would be of high interest to

Figure 12 (top) reports a typical simulated signal, and a real TEOAE signal recorded from a normoacousic subject is reported in figure 12 (bottom). In both simulated and real signals, recording starts after 2.5 ms from the initial external excitation (t = 0), to get rid of the initial ringing. Both signals show oscillations lasting up to 20 ms, with higher frequencies having shorter latency than lower frequencies, in agreement with the latency-frequency relationship typical of TEOAEs. In fact, according to the place–frequency (tonotopic) effect characteristic

de-activating the voltage sources in all cochlear sections.

**Figure 12.** TEOAE Simulated (top) and real (bottom) in normal ear

audiology and hearing researchers.

**Figure 11.** The electronic ear model

The considered ear model is depicted in figure 11 and encompasses the human ear anatomy from the auditory canal to the OHC within the cochlea. The auditory canal is represented by a cascade of four T-sections, corresponding to the segmented form of a uniform transmission line, while the middle ear is modeled as a complex electrical network based on its functional anatomy [25]. An ideal transformer connects the middle ear to the cochlea, to represent the acoustic transformer ratio between the eardrum and the oval window [20-21]. Finally, the cochlea is modeled as a non-uniform and non-linear transmission line, divided into several sections from the base to the apex, each one consisting of a series inductor, a shunt resonant circuit (composed of a resistor, an inductor, and a capacitor), and a non-linear voltage source. In the electro-acoustic analogy, the series inductors represent the acoustic mass of the cochlear fluids; the resistors, inductors and capacitors forming the shunt resonant circuits represent the acoustic resistance, mass and stiffness of the basilar membrane, respectively, and the non linear voltage sources represent the OHC active processes. Finally, the helicotrema is modeled by the inductor LT. The initial values of the electric ear model components are those reported in Table 1 of [20] and also used in [22]. Correspondingly, the cochlea was represented by 128 and 64 partitions [19]

To verify the hypothesis that TEOAE are strongly modulated by the middle ear [17], some elements in the middle ear section were varied according to the experimental study of Avan and colleagues [4]. The first change is the addition of a stapes capacitor (*C st*) to the middle-ear section of the circuit, as already considered by [20] Giguère and Woodland (1994a) and by [25]. When *C st* has a large value, its impedance is small, corresponding to small tension in the stapedius muscle (*C st* equal to infinity corresponds to no stiffness in the resting condition). Conversely, when *C st* is small, its impedance is large, corresponding to high muscle tension. Then, changes in the tympanic membrane stiffness (*C <sup>0</sup>*, *C d1*), to account for changes in the middle ear pressure, and in the tympanic membrane mass (*L <sup>0</sup>*, *L <sup>d</sup>*), to simulate an additive mass, have been considered [4]. Furthermore, a dead cochlea condition has been simulated by de-activating the voltage sources in all cochlear sections.

The role of middle ear effects is a hot topic in the OAE field, and would be of high interest to audiology and hearing researchers.

**Figure 12.** TEOAE Simulated (top) and real (bottom) in normal ear

TEOAEs analysis [15, 18, 22], has been implemented into PSpice®. PSpice® is a standard electrical simulation tool for dc, transient and ac analyses [23] (see Figure 11). The input circuit can be defined by using a graphical interface or by compiling a list representing the circuit topology. The outputs of the system are current and voltage values within the circuit which can be displayed in both tabular and graphical formats. PSpice® has been already used to study an electric model of the cochlea [24] due to the possibility to relate the model parameters to physical and physiological issues. In [24], the used lumped parameter model is entirely passive, made of a resistive network combined with two capacitances in order to model the

The considered ear model is depicted in figure 11 and encompasses the human ear anatomy from the auditory canal to the OHC within the cochlea. The auditory canal is represented by a cascade of four T-sections, corresponding to the segmented form of a uniform transmission line, while the middle ear is modeled as a complex electrical network based on its functional anatomy [25]. An ideal transformer connects the middle ear to the cochlea, to represent the acoustic transformer ratio between the eardrum and the oval window [20-21]. Finally, the cochlea is modeled as a non-uniform and non-linear transmission line, divided into several sections from the base to the apex, each one consisting of a series inductor, a shunt resonant circuit (composed of a resistor, an inductor, and a capacitor), and a non-linear voltage source. In the electro-acoustic analogy, the series inductors represent the acoustic mass of the cochlear fluids; the resistors, inductors and capacitors forming the shunt resonant circuits represent the acoustic resistance, mass and stiffness of the basilar membrane, respectively, and the non linear voltage sources represent the OHC active processes. Finally, the helicotrema is modeled by the inductor LT. The initial values of the electric ear model components are those reported in Table 1 of [20] and also used in [22]. Correspondingly, the cochlea was represented by 128 and

Reissner's membrane and the OHC in the Corti Organ.

**Figure 11.** The electronic ear model

218 Acoustic Emission - Research and Applications

64 partitions [19]

Figure 12 (top) reports a typical simulated signal, and a real TEOAE signal recorded from a normoacousic subject is reported in figure 12 (bottom). In both simulated and real signals, recording starts after 2.5 ms from the initial external excitation (t = 0), to get rid of the initial ringing. Both signals show oscillations lasting up to 20 ms, with higher frequencies having shorter latency than lower frequencies, in agreement with the latency-frequency relationship typical of TEOAEs. In fact, according to the place–frequency (tonotopic) effect characteristic of the basilar membrane, each element of the membrane acts as a resonator at a frequency inversely proportional to its distance from the oval window.

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A very important goal in prevention and clinical applications is to improve the specificity of diagnostic tests and to reduce inter-subject variability in TEOAE signals. A new pass/fail test could be useful for screening but the quantification of cochlear damage is of great interest in research programs. To determine the amount of damage, an ear model can be used to simulate different levels of cochlear damage by silencing a growing number of cochlear partitions. The relation between a new parameter and the number of silenced partitions in the model was evaluated.

From the comparison between the real and simulated RAD2D values it is possible to extrap‐ olate the corresponding number of "hypothetical silenced partitions". In this way, since each partition corresponded to a specific portion of uncoiled cochlea and to a specific number of outer hair cells, a descriptor of OHC integrity is obtained [26].

#### **8. Conclusion**

A very important goal in prevention and clinical applications is to improve the specificity of diagnostic tests and to reduce inter-subject variability in TEOAE signals. The availability of new micro-probes able to pick up both the pressure and the air particle velocity signals inside the ear canal, while allowing to update the standard multi-tonal tympanometry with the wideband implementation of p-v tympanometric non-invasive tests, points also to record and analyze TEOAEs as the non-linear by-product of DSP algorithms used in the ear-immitance function computing process. Furthermore, to prevent and to mitigate noise and aging effects on cochlea, a new post-processing procedure could be employed in *longitudinal studies* [27] as well as to test the efficacy of new pharmacological treatments and the opportunity to follow a subject over time.

#### **Author details**

Giovanna Zimatore1 , Domenico Stanzial2 and Maria Patrizia Orlando1

\*Address all correspondence to: domenico.stanzial@cnr.it

1 CNR-IDASC – Institute of Acoustics and Sensor "Orso Mario Corbino", Rome, Italy

2 CNR-IDASC - Institute of Acoustics and Sensor "Orso Mario Corbino", c/o Physics Depart‐ ment University of Ferrara, Italy

#### **References**

of the basilar membrane, each element of the membrane acts as a resonator at a frequency

A very important goal in prevention and clinical applications is to improve the specificity of diagnostic tests and to reduce inter-subject variability in TEOAE signals. A new pass/fail test could be useful for screening but the quantification of cochlear damage is of great interest in research programs. To determine the amount of damage, an ear model can be used to simulate different levels of cochlear damage by silencing a growing number of cochlear partitions. The relation between a new parameter and the number of silenced partitions in the model was

From the comparison between the real and simulated RAD2D values it is possible to extrap‐ olate the corresponding number of "hypothetical silenced partitions". In this way, since each partition corresponded to a specific portion of uncoiled cochlea and to a specific number of

A very important goal in prevention and clinical applications is to improve the specificity of diagnostic tests and to reduce inter-subject variability in TEOAE signals. The availability of new micro-probes able to pick up both the pressure and the air particle velocity signals inside the ear canal, while allowing to update the standard multi-tonal tympanometry with the wideband implementation of p-v tympanometric non-invasive tests, points also to record and analyze TEOAEs as the non-linear by-product of DSP algorithms used in the ear-immitance function computing process. Furthermore, to prevent and to mitigate noise and aging effects on cochlea, a new post-processing procedure could be employed in *longitudinal studies* [27] as well as to test the efficacy of new pharmacological treatments and the opportunity to follow a

and Maria Patrizia Orlando1

inversely proportional to its distance from the oval window.

220 Acoustic Emission - Research and Applications

outer hair cells, a descriptor of OHC integrity is obtained [26].

, Domenico Stanzial2

1 CNR-IDASC – Institute of Acoustics and Sensor "Orso Mario Corbino", Rome, Italy

2 CNR-IDASC - Institute of Acoustics and Sensor "Orso Mario Corbino", c/o Physics Depart‐

\*Address all correspondence to: domenico.stanzial@cnr.it

evaluated.

**8. Conclusion**

subject over time.

**Author details**

Giovanna Zimatore1

ment University of Ferrara, Italy


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343-349.

## *Edited by Wojciech Sikorski*

Acoustic emission (AE) is a phenomenon in which elastic or stress waves are emitted from rapid, localized change of strain energy in material. The practical application of the AE first emerged in the 1950's, but only in the last 20 years the science, technology and applications of AE have progressed significantly. Currently AE has become one of the most important non-destructive testing techniques. This interdisciplinary book consists of nine chapters, which is a proof of the fact that the AE method is continuously and intensively developing and widely applied in: on-line monitoring of civil-engineering structures (e.g. highway bridges, skyscrapers, dams etc.), fatigue cracks detection and location in pressure vessels and pipelines, damage assessment in fibre-reinforced polymer-matrix composites, monitoring welding applications and corrosion processes, bearing condition diagnostics, partial discharge sources detection and location in power transformers and generators, monitoring the drying process of materials, quality evaluation of fruits and vegetables and in otoacoustic emission analysis.

Acoustic Emission

Research and Applications

*Edited by Wojciech Sikorski*

ISBN 978-953-51-1015-6

ISBN 978-953-51-6312-1

Acoustic Emission - Research and Applications

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