**2.1 Avocado fruit samples**

214 Infrared Spectroscopy – Life and Biomedical Sciences


There have been limited investigations of avocado maturity based on %DM using NIRS. Schmilovitch et al. (2001) used a dispersive NIR spectrophotometer in reflectance mode to assess the 'Ettinger' and 'Fuerte' cultivars (both relatively thin-skinned) in the range 1200 - 2400 nm. Preliminary results identified standard errors of prediction for both 'Ettinger' and 'Fuerte' as 0.9 and 1.3%, respectively, over a 14 – 24 %DM range. Clark et al. (2003) investigated the use of a fixed polychromatic/diode array (PDA) spectrophotometer for estimating %DM in whole New Zealand 'Hass' avocado fruit using both reflectance and interactance modes. They concluded that interactance mode was a better predictor of %DM compared with reflectance. Reflectance models required high numbers (12 to 20) of latent variables (LV), indicating the models struggled against spectral noise and so required incorporation of many small spectral features to improve accuracy. Clark et al. (2003) reported interactance validation statistics of R2 (coefficient of determination) prediction >0.83, and root mean square error of prediction (RMSEP) <1.8 %DM, over a range of 20 – 45 %DM, while the corresponding reflectance results were <0.75 and >1.9 %DM, respectively. Walsh et al. (2004), using a fixed PDA spectrophotometer (Ziess MMS1/NIR-enhanced spectrometer, Germany) in the 300 - 1100 nm range, reported calibration results of r (correlation coefficient) = 0.89, root mean square error of crossvalidation (RMSECV) = 1.14, with a standard deviation ratio (SDR = standard deviation of the data set divided by the RMSECV or RMSEP) = 2.2, for %DM of avocado fruit of unspecified cultivar. The SDR statistic is the measurement of the ability of an NIRS model to predict a constituent and enables comparison of model performance across populations with different standard deviations (Baillères et al., 2002; Golic & Walsh, 2006). The higher the SDR statistic the greater the power of the model to predict the chemical composition accurately (Cozzolino et al., 2004). SDR values between 2.0 and 2.4 for 'difficult' applications, such as high moisture materials including fruit and vegetables are regarded as adequate for rough screening; a value between 2.5 and 2.9 are regarded as adequate for screening; a value between 3.0 and 3.4 is regarded as satisfactory for quality control; a value between 3.4 and 4.0 is regarded as very good for process control; values above 4.1 are excellent for any application (Nicolaï et al., 2007; Schimleck et al., 2003; Williams,

This study assessed the potential of FT-NIR diffuse reflectance spectroscopy as an objective non-invasive method for determining internal quality attributes of whole 'Hass' avocado fruit. These include: (a) to predict maturity and thereby eating quality based on %DM; (b) to predict the risk of developing internal rot disorders (i.e., rot susceptibility) as an indication of shelf-life; (c) to detect bruises. The study also demonstrates the importance of the calibration model development process to incorporate seasonal and geographical variability

Miyanoto & Yoshinobu, 1995).

2008).

to ensure model robustness.

### **2.1.1 Fruit for dry matter model development**

'Hass' avocado fruit were obtained over the 2006, 2007 and 2008 growing seasons (Harvest months: May to November) from two commercial farms in the major production districts of Bundaberg, South East Queensland (Latitude: 24° 52' South, Longitude: 152° 21' East) and Childers, South East Queensland (Latitude: 25° 14' South, Longitude: 152° 16' East). Avocado fruit were harvested at three maturity stages through each season, corresponding to early, mid and late season harvests over the three growing seasons. This allowed for sufficient variability in the %DM range and other seasonal factors to be included in the calibration procedure. A minimum of 100 fruit were collected at each harvest giving a total of a minimum of 900 individual fruit for each growing region. All fruit were harvested at the hard green stage of ripeness.

### **2.1.2 Fruit for impact and rot model development**

'Hass' avocado fruit were obtained over the 2008 growing season from two farms in Queensland, Australia. The first farm is located near Ravenshoe on the Atherton Tablelands in North Queensland (Latitude: 17° 38' South, Longitude: 145° 29' East) and the second farm is located in the major production district of Toowoomba, South East Queensland (Latitude: 27° 33' South, Longitude: 151° 58' East). Fruit from Ravenshoe were used for the impact assessment trials (n = 102), while Toowoomba fruit (n = 125) were used for rot susceptibility (shelf life) trials. All fruit were harvested at the hard green stage of ripeness.

### **2.2 NIR data collection**

#### **2.2.1 Dry matter NIR data**

The spectra of whole, intact avocado fruit were collected using a commercially available Matrix-F, FT-NIR spectrophotometer (Bruker Optics, Ettlingen, Germany; operating software: OPUS™ version 5.1 - 6.5) in the 830 – 2500 nm range. Spectra were obtained in diffuse reflectance mode, using a standard 4 x 20 watt tungsten light source fibre-coupled emission head fitted to the spectrometer. The external emission head was placed directly above the avocado fruit (0o configuration). A light reducing box with a 60 mm diameter cut out window was used to hold the fruit, so that the fruit skin was directly exposed to the focal point of the emission head. A path-length of approximately 170 mm from the external emission head light source to the surface of the fruit provided a spectral scan diameter on the avocado of approximately 50 mm. In obtaining each sample spectrum, 32 scans at a resolution of 8 cm-1 were collected and averaged. Due to the large variability in the %DM within a fruit (Schroeder, 1985; Wedding et al., 2010; Woolf et al., 2003) two NIR spectra were collected from each fruit, one spectra from each opposing side midway from the peduncle and base (i.e., equatorial region). A white spectralon standard was used as the optical reference standard for the system prior to the collection of each set of sample spectra. Fruit spectra were acquired after sample temperature equilibration in an air-conditioned laboratory at approximately 22 - 24 C, and within two days of harvest.

The Application of Near Infrared Spectroscopy

Model performance was based on the R2 of the calibration (Rc

predicted and actual values) (Buning-Pfaue, 2003), and the SDR.

Fig. 1. Typical absorbance spectra for whole 'Hass' avocado fruit.

to weighting by the standard deviation prior to analysis.

**0**

**2.4.2 Impact and rot data analysis** 

**0.4**

**0.8**

**Absorbance**

**1.2**

**1.6**

for the Assessment of Avocado Quality Attributes 217

each data set were separated into a calibration set and prediction set to develop the calibration and prediction models respectively. Fruit were assigned to the calibration set from the principal component analysis (PCA) results to provide a global representation of the attributes of the entire fruit population while eliminating repetition. All remaining fruit where used in the validation sets. Partial least squares (PLS) regression was used to build the prediction models of the diffuse reflectance spectral data, using segmented cross validation (20 segments in this case). Before calibration model development, the variation of the spectral data was analysed by PCA, and obvious spurious spectra eliminated. Data pretreatment and smoothing for the individual %DM models for each growing location in this study were based on a combination of: a 25 point Savitsky-Golay (SG) spectral smoothing (2nd order polynomial) and a second derivative transformation (25 point SG smoothing and 2nd order polynomial) for the Bundaberg models; and a 25 point SG spectral smoothing (2nd order polynomial) and a multiplicative scatter correction (MSC) transformation for the Childers models. For the combined Bundaberg and Childers model, data pretreatment and smoothing was based on a combination of a 25 point SG spectral smoothing (2nd order polynomial) and a first derivative transformation (25 point SG smoothing and 2nd order polynomial). Among all spectra collected, significant noise was found at the extremities of the spectral range (830 – 843 and 2414 - 2503 nm). Therefore all raw spectra used for analysis were truncated to a range of 843 - 2414 nm before model development. Typical absorbance spectra for 'Hass' avocado fruit are shown in Figure 1.

(Rv2) data sets; RMSECV; RMSEP in relation to the bias (average difference between

**800 1200 1600 2000 2400**

'The Unscrambler' version 10.1, (CAMO, Oslo, Norway) was used for discriminative analysis to separate the avocados into categories based on percentage rot and percentage bruise development of the scanned area. The 1 – 2 hours impact wavelengths were subjected

**Wavelength (nm)**

2) and validation/prediction

#### **2.2.2 Impact and rot assessment NIR data**

For both impact (bruise) and rot assessment trials, diffuse reflectance spectra of whole, intact 'Hass' avocado fruit were collected in the 830 – 2500 nm range using a Bruker Matrix-F, FT-NIR spectrophotometer as discussed in section 2.2.1. Spectra for rot susceptibility prediction were collected from each opposing half of the hard green fruit prior to fruit being placed into 20 C storage at 85 - 95% relative humidity. At eating ripe fruit were then assessed for rots based on a weight percentage of the flesh volume affected.

For impact assessment, hard green fruit were stored at 20 C and 85 - 95% relative humidity until fruit reached the sprung stage of ripeness. The sprung stage of ripeness is where the flesh deforms by 2 - 3 mm under extreme thumb pressure (White et al., 2001). Individual spectra were collected from a single side of the fruit on reaching the sprung stage of ripeness. Following initial spectra collection, fruit were dropped from a height of 100 cm against a slate paver (height: 400 mm, length: 400 mm, width: 40 mm) placed upright and supported by concrete blocks to simulate impact damage. Individual fruit were placed into a cotton mesh bag which was firmly suspended by two strings attached to the laboratory ceiling. Each fruit was positioned so that the scanned area would impact against the paver. The fruit in the mesh bag was pulled backwards away from the slate paver and released to swing in a pendulum motion to impact against the slate paver. Fruit were only allowed to impact the paver once. The height from the ground to the middle of the fruit was measured with the fruit sitting freely against the slate paver. The drop height was measured as the difference between the height at the top of the arch, and the height at the bottom of the arch where the fruit hit the paver.

The impacted area was re-scanned after 1 - 2 hours (maximum of 4) and again after 24 hours. Fruit were then placed back into 20 C storage at 85 - 95% relative humidity and assessed for bruises at eating ripe (approximately 5 days following impact). Bruise assessment was based on visual estimate of percentage bruise development of the flesh within the scanned area.

### **2.3 Chemical analysis**

The %DM reference measurement was obtained from the same area of the fruit that was used to obtain the NIR spectra. To determine the %DM, a 50 mm diameter core equal to the NIR scan area was taken perpendicular to the surface of the fruit, at a depth of approximately 10 - 15 mm. The skin (2 - 4 mm) was removed from the avocado flesh, and the flesh was diced to facilitate drying in a fan-forced oven at 60 - 65 C to constant weight (approximately 72 hours). The %DM is defined by the percentage ratio of the weight of the dried flesh sample to the original moist flesh sample. It should be emphasized that fruit spectra and %DM were acquired after sample temperature equilibration in an airconditioned laboratory at approximately 22 - 24 C and within two days of harvest.

#### **2.4 Data analysis**

#### **2.4.1 Dry matter data analysis**

Data analysis was carried out using the commercially available chemometric software package 'The Unscrambler™' version 9.8 (CAMO, Oslo, Norway). The sample spectra for

For both impact (bruise) and rot assessment trials, diffuse reflectance spectra of whole, intact 'Hass' avocado fruit were collected in the 830 – 2500 nm range using a Bruker Matrix-F, FT-NIR spectrophotometer as discussed in section 2.2.1. Spectra for rot susceptibility prediction were collected from each opposing half of the hard green fruit prior to fruit being placed into 20 C storage at 85 - 95% relative humidity. At eating ripe fruit were then assessed for

For impact assessment, hard green fruit were stored at 20 C and 85 - 95% relative humidity until fruit reached the sprung stage of ripeness. The sprung stage of ripeness is where the flesh deforms by 2 - 3 mm under extreme thumb pressure (White et al., 2001). Individual spectra were collected from a single side of the fruit on reaching the sprung stage of ripeness. Following initial spectra collection, fruit were dropped from a height of 100 cm against a slate paver (height: 400 mm, length: 400 mm, width: 40 mm) placed upright and supported by concrete blocks to simulate impact damage. Individual fruit were placed into a cotton mesh bag which was firmly suspended by two strings attached to the laboratory ceiling. Each fruit was positioned so that the scanned area would impact against the paver. The fruit in the mesh bag was pulled backwards away from the slate paver and released to swing in a pendulum motion to impact against the slate paver. Fruit were only allowed to impact the paver once. The height from the ground to the middle of the fruit was measured with the fruit sitting freely against the slate paver. The drop height was measured as the difference between the height at the top of the arch, and the height at the bottom of the arch

The impacted area was re-scanned after 1 - 2 hours (maximum of 4) and again after 24 hours. Fruit were then placed back into 20 C storage at 85 - 95% relative humidity and assessed for bruises at eating ripe (approximately 5 days following impact). Bruise assessment was based on visual estimate of percentage bruise development of the flesh

The %DM reference measurement was obtained from the same area of the fruit that was used to obtain the NIR spectra. To determine the %DM, a 50 mm diameter core equal to the NIR scan area was taken perpendicular to the surface of the fruit, at a depth of approximately 10 - 15 mm. The skin (2 - 4 mm) was removed from the avocado flesh, and the flesh was diced to facilitate drying in a fan-forced oven at 60 - 65 C to constant weight (approximately 72 hours). The %DM is defined by the percentage ratio of the weight of the dried flesh sample to the original moist flesh sample. It should be emphasized that fruit spectra and %DM were acquired after sample temperature equilibration in an air-

Data analysis was carried out using the commercially available chemometric software package 'The Unscrambler™' version 9.8 (CAMO, Oslo, Norway). The sample spectra for

conditioned laboratory at approximately 22 - 24 C and within two days of harvest.

**2.2.2 Impact and rot assessment NIR data** 

where the fruit hit the paver.

within the scanned area.

**2.3 Chemical analysis** 

**2.4 Data analysis** 

**2.4.1 Dry matter data analysis** 

rots based on a weight percentage of the flesh volume affected.

each data set were separated into a calibration set and prediction set to develop the calibration and prediction models respectively. Fruit were assigned to the calibration set from the principal component analysis (PCA) results to provide a global representation of the attributes of the entire fruit population while eliminating repetition. All remaining fruit where used in the validation sets. Partial least squares (PLS) regression was used to build the prediction models of the diffuse reflectance spectral data, using segmented cross validation (20 segments in this case). Before calibration model development, the variation of the spectral data was analysed by PCA, and obvious spurious spectra eliminated. Data pretreatment and smoothing for the individual %DM models for each growing location in this study were based on a combination of: a 25 point Savitsky-Golay (SG) spectral smoothing (2nd order polynomial) and a second derivative transformation (25 point SG smoothing and 2nd order polynomial) for the Bundaberg models; and a 25 point SG spectral smoothing (2nd order polynomial) and a multiplicative scatter correction (MSC) transformation for the Childers models. For the combined Bundaberg and Childers model, data pretreatment and smoothing was based on a combination of a 25 point SG spectral smoothing (2nd order polynomial) and a first derivative transformation (25 point SG smoothing and 2nd order polynomial). Among all spectra collected, significant noise was found at the extremities of the spectral range (830 – 843 and 2414 - 2503 nm). Therefore all raw spectra used for analysis were truncated to a range of 843 - 2414 nm before model development. Typical absorbance spectra for 'Hass' avocado fruit are shown in Figure 1. Model performance was based on the R2 of the calibration (Rc 2) and validation/prediction (Rv2) data sets; RMSECV; RMSEP in relation to the bias (average difference between predicted and actual values) (Buning-Pfaue, 2003), and the SDR.

Fig. 1. Typical absorbance spectra for whole 'Hass' avocado fruit.

### **2.4.2 Impact and rot data analysis**

'The Unscrambler' version 10.1, (CAMO, Oslo, Norway) was used for discriminative analysis to separate the avocados into categories based on percentage rot and percentage bruise development of the scanned area. The 1 – 2 hours impact wavelengths were subjected to weighting by the standard deviation prior to analysis.

The Application of Near Infrared Spectroscopy

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

(a) (b)

(c) (d)

(e) (f)

2007 season, (e) Bundaberg 2008 season, and (f) Childers 2008 season.

Fig. 2. Model predictions plotted against actual constituent values for %DM for (a) Bundaberg 2006 season, (b) Childers 2006 season, (c) Bundaberg 2007 season, (d) Childers

Large seasonal effects have a major consequence for calibration models for horticultural produce, since the spectral deviations due to biological variability of future samples cannot be predicted (Peirs et al., 2003). The influence of seasonal variability was investigated for the Bundaberg and Childers growing locations over three years. For both growing locations, the

**Predicted %Dry Matter**

**Predicted %Dry Matter**

**Predicted %Dry Matter**

for the Assessment of Avocado Quality Attributes 219

**Predicted %Dry Matter**

**Predicted %Dry Matter**

**Predicted %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**

14 19 24 29 34 **Reference %Dry Matter**
