**3. Validation protocol for the NIRS method**

Demonstration that the NIRS method is fit for intended purpose has the great importance for the compliance with regulations, process control, making regulatory decisions, support national and international trade, support research etc.

When we talk about the validation of the NIRS as an analytical method we must be aware of the duality of the term "validation'' used within the analytical chemists utilizing NIRS. The most common use of the term "validation" implies using an independent sample set to test the accuracy of the calibration model developed (Mark & Workman, 2007). That process results in certain statistical indicator explained later in the text. This process must be distinguished from the process of validation of NIR spectroscopic method aimed at confirmation, through the provision of objective evidence, that the requirements for a specific intended use of application are fulfilled (Lauwaars & Anklam, 2004; Dybkaer, 2011). Hence, the objective of the validation of the NIRS method is to demonstrate that its characteristics are suitable for its intended purpose. When we talk about the application of the NIRS method in wheat quality control, the validation experiment should include the testing of characteristics of NIRS method and demonstration its fitness for purpose.

The validation protocol that was performed in our laboratory in order to provide the evidence that the NIRS method as being applied in wheat quality control for protein content prediction fitted for purpose included: accuracy, repeatability, reproducibility, intermediate precision, linearity, robustness and transferability (Fig. 3). The NIRS instrument used was a

Fig. 3. The validation protocol of the NIRS method as being applied in wheat quality control

scanning monochromator Infratec 1241 Grain Analyzer with the ANN calibration model for protein content used in the transmittance mode (FOSS Analytical, Denmark).

#### **3.1 Accuracy**

172 Infrared Spectroscopy – Life and Biomedical Sciences

Analytical methods based on near infrared spectroscopy have the potential to significantly improve the quality of final cereal products by testing the products through the entire production process in processing industry (raw materials, intermediate and final products). For these purposes, on-line instruments as well as stand-alone bench type instruments designed for testing whole grains by measuring the intensity of transmission of NIR radiation from the spectral range 850-1050 nm or the intensity of diffuse reflection of NIR radiation from the spectral range 1000-1400 nm are the most suitable. For example, the determination of ash by NIRS is particularly useful for process control in the wheat milling industry to monitor the consistency of milling and the compliance with flour specifications. Although inorganic substances do not absorb energy in the NIR spectral region, some authors demonstrated that the NIRS method can be used for reliable prediction of the ash content (Dowell et al, 2006; Deaville & Flinn, 2000; Osborne, 2007; Pérez-Marín et al., 2004; Armstrong et al., 2006; Mentink et al., 2006; Pojić et al., 2010). Since ash content cannot be directly measured by NIRS, it is assumed that it is predicted by correlation with the total amount of organic compounds and water present, because of the large number of wavelengths used in the process of calibration development that give significant information (Osborne, 2007; Clark et al., 1987; Garnsworthy et al., 2000;

Demonstration that the NIRS method is fit for intended purpose has the great importance for the compliance with regulations, process control, making regulatory decisions, support

When we talk about the validation of the NIRS as an analytical method we must be aware of the duality of the term "validation'' used within the analytical chemists utilizing NIRS. The most common use of the term "validation" implies using an independent sample set to test the accuracy of the calibration model developed (Mark & Workman, 2007). That process results in certain statistical indicator explained later in the text. This process must be distinguished from the process of validation of NIR spectroscopic method aimed at confirmation, through the provision of objective evidence, that the requirements for a specific intended use of application are fulfilled (Lauwaars & Anklam, 2004; Dybkaer, 2011). Hence, the objective of the validation of the NIRS method is to demonstrate that its characteristics are suitable for its intended purpose. When we talk about the application of the NIRS method in wheat quality control, the validation experiment should include the

testing of characteristics of NIRS method and demonstration its fitness for purpose.

The validation protocol that was performed in our laboratory in order to provide the evidence that the NIRS method as being applied in wheat quality control for protein content prediction fitted for purpose included: accuracy, repeatability, reproducibility, intermediate precision, linearity, robustness and transferability (Fig. 3). The NIRS instrument used was a

monitoring of the accuracy of measurements of the central (master) device,

monitoring of the stability of measurements of individual NIRS device.

standardization of individual NIRS devices and

**3. Validation protocol for the NIRS method** 

national and international trade, support research etc.

**2.3 The role of NIRS in cereal processing** 

Frankhuizen, 2008).

Accuracy, defined as the closeness of agreement between a measured value and a true value of a measurand, in the case of the NIRS method expresses a measure of how well NIRS predicted value match a given reference value obtained by a reference (wet chemistry) method. The accuracy of the NIRS method is commonly described by statistical terms such as SEC (standard error of calibration), SECV (standard error of cross validation), R2 (coefficient of determination), explained variance (1-VR), residual predictive deviation (RPD), standard error of prediction (SEP) etc. (Konieczka & Namieśnik, 2009). These values describe the agreement between the predicted NIRS values and the reference method values from the same sample (Ritchie et al., 2002; Moffat, 2004). SEC, SECV, R2 and 1-VR values are calculated on the basis of samples used to develop the calibration model itself, whilst the SEP value is calculated on the basis of independent sample set not included in the calibration model development procedure:

$$\text{SEP} = \sqrt{\frac{\sum\_{i=1}^{N} (\mathbf{y}\_i - \mathbf{y}\_P)^2}{N - 1}} \tag{2}$$

where yr is the reference value of *i* samples, yp is the NIRS predicted value of i sample, N the number of samples.

The selection of suitable statistical term to express the accuracy of the NIRS method depends on the availability of the samples covering the whole range of component concentration with its even distribution. The accuracy of the NIRS method to a large extent is influenced by nonhomogeneity of sample, laboratory error, physical and chemical variation in sample

The Application of Near Infrared Spectroscopy in Wheat Quality Control 175

Fig. 4. Comparable view of protein content obtained by the NIRS and reference method,

dispersion of results around the mean value. The precision can be considered by monitoring of repeatability, intermediate precision and reproducibility. All of them can be quantified on the basis of standard deviation, relative standard deviation, or the coefficient of variation

0 2 4 6 8 10 12 14 16 Sample

REF NIRS - 5 NIRS - 7 NIRS - 10

Repeatability is a method characteristic that indicates the measure of dispersion of results obtained under the same measurement conditions (a given laboratory, analyst, measuring instrument, reagents, etc.). Since the recommendation for repeatability determination implies measurements on samples characterized with different analyte concentrations and different matrix composition, repeatability of the NIRS methods for protein content prediction included eight consecutive measurements under the repeatability condition using

To assess the acceptability of the repeatability, a modified Horwitz's equation can be used:

 RSDr = 2(1-0,5logC) \* 0,67 (4) where the acceptable repeatability is determined on the basis of comparison of actual relative standard deviation (RSDr,i) calculated from measured values and predicted relative

 RSDr,i < RSDr (5) The results showed in Table 2 indicates that the repeatability of the NIRS method is better

The results shown in Fig. 5 show that repeatability relative standard deviation calculated from actual NIRS results for protein content (RSDr,i) are lower than Horwitz's relative standard deviation (RSDr). Thereby, the criterion of repeatability of NIRS method for

derived from 5, 7 and 10 subsamples, before and after bias adjustment

0 2 4 6 8 10 12 14 16 Sample

standard deviation (RSDr) calculated from the Horwitz's equation:

than that of the reference methods expressed by lower SDr,i, RSDr,i and ri.

(Konieczka & Namieśnik, 2009).

protein content prediction is fulfilled.

**3.2.1 Repeatability** 

Protein content, % d.m.

a set of 15 samples.

with time, population sampling error, different nature of spectroscopic and wet chemical measurements, instrument noise, sample presentation, calibration modeling, calibration transfer etc. (Workman, Jr., 2008). Statistical term used to express a systematic difference between the two sets of results obtained by the reference method and the NIRS method is bias (Workman, Jr., 2008; Shenk et al., 2008):

$$\sum\_{\mathbf{i}=\mathbf{j}=1}^{\text{N}} (\mathbf{y}\_{\mathbf{r}} - \mathbf{y}\_{\mathbf{P}})$$
 
$$\text{Bias} = \frac{\mathbf{i} = 1}{\text{N}} \tag{3}$$

where yr is the reference value of *i* samples, yp is the NIRS predicted value of i sample, N the number of samples.

The accuracy can be improved by a bias and slope adjustment, but it requires precaution due to the fact that the bias only fixes the problem on a temporary basis.


*Values in the same column marked with the same letters are no significantly different (P <0.05, LSD test)* 

Table 1. The predictive ability of the NIRS model for protein content prediction depending on the number of subsamples in a single measurement

The accuracy of the NIRS method was evaluated by calculating bias, RMSEP and SEP in three different cases, using five, seven and ten subsamples in a single NIRS measurement. To determine the accuracy of protein content prediction, two independent validation sample sets were used where the first validation set was used before, and the second one after the bias correction. The parameters of predictive ability of protein content (RMSEP and SEP) were not influenced by variable number of subsamples. Bias adjustment affected better predictive ability for protein content expressed by lower RMSEP and SEP values for the second validation set (Table 1). By the assessment of comparable views of protein content obtained by the reference and NIRS method from measurements of 5, 7 and 10 subsamples, before and after bias adjustment, the negligible difference between the reference and NIRS method could be noticed after the bias adjustment regardless the number of subsamples in a single measurement (Fig. 4).

#### **3.2 Precision (repeatability, reproducibility)**

Precision is more important NIRS method characteristics than accuracy since it cannot be changed by a simple adjustment. It is affected by the instrument, the calibration and the operator. Precision can be defined as the closeness of agreement between measured values obtained by replicate measurements on the same or similar objects under specified conditions. It is commonly associated with random errors and represents a measure of

Fig. 4. Comparable view of protein content obtained by the NIRS and reference method, derived from 5, 7 and 10 subsamples, before and after bias adjustment

dispersion of results around the mean value. The precision can be considered by monitoring of repeatability, intermediate precision and reproducibility. All of them can be quantified on the basis of standard deviation, relative standard deviation, or the coefficient of variation (Konieczka & Namieśnik, 2009).

#### **3.2.1 Repeatability**

174 Infrared Spectroscopy – Life and Biomedical Sciences

with time, population sampling error, different nature of spectroscopic and wet chemical measurements, instrument noise, sample presentation, calibration modeling, calibration transfer etc. (Workman, Jr., 2008). Statistical term used to express a systematic difference between the two sets of results obtained by the reference method and the NIRS method is

N

i 1

where yr is the reference value of *i* samples, yp is the NIRS predicted value of i sample, N the

The accuracy can be improved by a bias and slope adjustment, but it requires precaution

 

*Values in the same column marked with the same letters are no significantly different (P <0.05, LSD test)*  Table 1. The predictive ability of the NIRS model for protein content prediction depending

The accuracy of the NIRS method was evaluated by calculating bias, RMSEP and SEP in three different cases, using five, seven and ten subsamples in a single NIRS measurement. To determine the accuracy of protein content prediction, two independent validation sample sets were used where the first validation set was used before, and the second one after the bias correction. The parameters of predictive ability of protein content (RMSEP and SEP) were not influenced by variable number of subsamples. Bias adjustment affected better predictive ability for protein content expressed by lower RMSEP and SEP values for the second validation set (Table 1). By the assessment of comparable views of protein content obtained by the reference and NIRS method from measurements of 5, 7 and 10 subsamples, before and after bias adjustment, the negligible difference between the reference and NIRS method could be noticed after the bias adjustment regardless the number of subsamples in a

Precision is more important NIRS method characteristics than accuracy since it cannot be changed by a simple adjustment. It is affected by the instrument, the calibration and the operator. Precision can be defined as the closeness of agreement between measured values obtained by replicate measurements on the same or similar objects under specified conditions. It is commonly associated with random errors and represents a measure of

Bias

due to the fact that the bias only fixes the problem on a temporary basis.

on the number of subsamples in a single measurement

r p

1. validation set 2. validation set BIAS, % RMSEP, % SEP, % BIAS, % RMSEP, % SEP, %

(3)

(y y )

N

5 -0.32a 0.4887a 0.3851a 0.03a 0.2461a 0.2494a 7 -0.30a 0.4926a 0.4028a 0.02a 0.2385a 0.2444a 10 -0.32a 0.5096a 0.4088a 0.04a 0.2474a 0.2517a

bias (Workman, Jr., 2008; Shenk et al., 2008):

number of samples.

Subsamples

single measurement (Fig. 4).

**3.2 Precision (repeatability, reproducibility)** 

Repeatability is a method characteristic that indicates the measure of dispersion of results obtained under the same measurement conditions (a given laboratory, analyst, measuring instrument, reagents, etc.). Since the recommendation for repeatability determination implies measurements on samples characterized with different analyte concentrations and different matrix composition, repeatability of the NIRS methods for protein content prediction included eight consecutive measurements under the repeatability condition using a set of 15 samples.

To assess the acceptability of the repeatability, a modified Horwitz's equation can be used:

$$\text{RSD}\_{\text{r}} = \text{2}^{(1 \cdot 0.5 \log \text{C})} \ast \text{0}\_{\text{t}} \text{67} \tag{4}$$

where the acceptable repeatability is determined on the basis of comparison of actual relative standard deviation (RSDr,i) calculated from measured values and predicted relative standard deviation (RSDr) calculated from the Horwitz's equation:

$$\text{RSD}\_{t,i} \le \text{RSD}\_{t} \tag{5}$$

The results showed in Table 2 indicates that the repeatability of the NIRS method is better than that of the reference methods expressed by lower SDr,i, RSDr,i and ri.

The results shown in Fig. 5 show that repeatability relative standard deviation calculated from actual NIRS results for protein content (RSDr,i) are lower than Horwitz's relative standard deviation (RSDr). Thereby, the criterion of repeatability of NIRS method for protein content prediction is fulfilled.

The Application of Near Infrared Spectroscopy in Wheat Quality Control 177

The results shown in Fig. 6 indicates excellent reproducibility of the NIRS method as being applied for protein content prediction by the chosen NIRS instrument (Infratec 1241, FOSS

Mean 0,1743 1,400 0,4872 0,0712 0,5449 0,1993 Min 0,1231 0,9613 0,3443 0,0350 0,2178 0,0980 Max 0,2622 1,9544 0,7320 0,1825 1,6357 0,5110

Table 3. Results that define the reproducibility of the reference and NIRS methods for

Fig. 6. HORRAT values for assessing the reproducibility of NIRS method for determining

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sample

Intermediate precision is a method characteristic that indicates the measure of dispersion of results obtained in a given laboratory over a long-term process of measuring defining the longterm stability or variability of a measurement process. In this way, the influence of various random effects in the measurement process (e.g. personal effects, instrumental effects, environmental effects etc.) can be monitored and quantified. Due to that, this characteristic has a wider scope than repeatability. The intermediate precision for protein content prediction was monitored over 62 days by using control (Shewart) chart (Fig. 7), having a key significance for raising wheat quality control system to the highest level. Even in cases where the measurement process is under control, control chart is a valuable tool for detection the disturbances in the measuring process. Also, this type of control represents a contribution to

The results shown in Fig. 7 indicats excellent stability of the NIRS method as being applied for protein content prediction by the chosen NIRS instrument (Infratec 1241, FOSS

the accreditation of the NIRS method based on the requirements of ISO/IEC 17025.

Analytical AB) since all measured values are within control limits.

REF method NIRS method SDR,i, % RSDR,i, % Ri SDR,i, % RSDR,i, % Ri

Analytical AB) since all HORRAT values were less than 2.

protein content determination

protein content

0.0

0.5

1.0

HORRATR

1.5

2.0

**3.2.3 Intermediate precision**


Table 2. Results that define the repeatability of the reference and NIRS methods for protein content determination

Fig. 5. Comparable view of actual RSDr,i and Horwitz's RSDr values for determining protein content by the NIRS method

#### **3.2.2 Reproducibility**

Reproducibility is a method characteristic that indicates the measure of dispersion of results obtained in different laboratories using a given measurement method. To assess the reproducibility of NIRS determination of protein in wheat with the Infratec 1241, set of 15 samples was measured with the 5 available NIRS anaylzers Infratec 1241. As a criterion for the acceptability of the reproducibility the HORRAT value was used, which was calculated by dividing the actual value of RSDR,i and RSDR calculated from the Horwitz equation:

$$\text{HORRAT}\_{\text{R}} = \text{RSD}\_{\text{R},i} / \text{RSD}\_{\text{R}} \tag{6}$$

Values lower than 2.0 are considered acceptable for among-laboratory precision, expressed as a HORRATR value (Fig. 6).

The results showed in Table 3 indicates that the reproducibility of the NIRS method is better than that of the reference methods expressed by lower SDR,i, RSDR,i and Ri.

Mean 0,0858 0,6301 0,2401 0,0755 0,5736 0,2113 Min 0,0503 0,3540 0,1408 0,0041 0,0280 0,0115 Max 0,2484 1,7407 0,6955 0,1173 1,0867 0,3284

Table 2. Results that define the repeatability of the reference and NIRS methods for protein

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 5. Comparable view of actual RSDr,i and Horwitz's RSDr values for determining protein

Reproducibility is a method characteristic that indicates the measure of dispersion of results obtained in different laboratories using a given measurement method. To assess the reproducibility of NIRS determination of protein in wheat with the Infratec 1241, set of 15 samples was measured with the 5 available NIRS anaylzers Infratec 1241. As a criterion for the acceptability of the reproducibility the HORRAT value was used, which was calculated by dividing the actual value of RSDR,i and RSDR calculated from the

 HORRATR = RSDR,i/ RSDR (6) Values lower than 2.0 are considered acceptable for among-laboratory precision, expressed

The results showed in Table 3 indicates that the reproducibility of the NIRS method is better

than that of the reference methods expressed by lower SDR,i, RSDR,i and Ri.

content determination

content by the NIRS method

**3.2.2 Reproducibility** 

Horwitz equation:

as a HORRATR value (Fig. 6).

0.0

0.5

1.0

1.5

2.0

REF method NIRS method

RSDr,i RSDr

SDr,i, % RSDr,i, % ri SDr,i, % RSDr,i, % ri

The results shown in Fig. 6 indicates excellent reproducibility of the NIRS method as being applied for protein content prediction by the chosen NIRS instrument (Infratec 1241, FOSS Analytical AB) since all HORRAT values were less than 2.


Table 3. Results that define the reproducibility of the reference and NIRS methods for protein content determination

Fig. 6. HORRAT values for assessing the reproducibility of NIRS method for determining protein content

#### **3.2.3 Intermediate precision**

Intermediate precision is a method characteristic that indicates the measure of dispersion of results obtained in a given laboratory over a long-term process of measuring defining the longterm stability or variability of a measurement process. In this way, the influence of various random effects in the measurement process (e.g. personal effects, instrumental effects, environmental effects etc.) can be monitored and quantified. Due to that, this characteristic has a wider scope than repeatability. The intermediate precision for protein content prediction was monitored over 62 days by using control (Shewart) chart (Fig. 7), having a key significance for raising wheat quality control system to the highest level. Even in cases where the measurement process is under control, control chart is a valuable tool for detection the disturbances in the measuring process. Also, this type of control represents a contribution to the accreditation of the NIRS method based on the requirements of ISO/IEC 17025.

The results shown in Fig. 7 indicats excellent stability of the NIRS method as being applied for protein content prediction by the chosen NIRS instrument (Infratec 1241, FOSS Analytical AB) since all measured values are within control limits.

The Application of Near Infrared Spectroscopy in Wheat Quality Control 179

**Lamp aging (L) Old** 13.83a

Table 4. Values in the same column marked with the same letters are no significantly

850 900 950 1000 1050

Wavelength, nm

Fig. 8. The average second derivative spectra of wheat samples obtained at different experimental conditions within OVAT experimental design (NS 5, NS 15 – Five and fifteen subsamples in NIRS measurement; NC – Nominal conditions; AT 10, AT 30 – Ambient temperature of 10 and 30C; ST 5, ST 35 – Sample temperature of 5 and 35C; IV 200, IV 240 – Instrument voltage of 200 and 240V; NL – New lamp; EH 40, EH 80 – Environmental

**Number of subsamples (NS)** 

**Sample temperature (ST)** 

**Ambient temperature (AT)** 

**Environmental humidity (EH)** 

different (P <0.05, LSD test) (Pojić et al., 2012).

humidity of 40% and 80 %) (Pojić et al., 2012).

**Instrument voltage (IV)** 






Log (1/T)

0

0.1

0.2

0.3

0.4

0.5

**Experimental factors Protein,** 

**5** 13.79a **10** 13.79a **15** 13.83a

**5°C** 13.90a **20°C** 13.83a **35°C** 13.84a

**10°C** 13.83a **20°C** 13.83a **30°C** 13.81a

**40%** 13.81a **60%** 13.83a **80%** 13.86a

**200V** 13.80a **220V** 13.83a **240V** 13.84a

**New** 13.85a

**% d.m.** 

NS 5 NS 15 NC AT 10 AT 30 ST 5 ST 35 IV 200 IV 240 NL EH 40 EH 80

Fig. 7. Shewart (control) chart for determing protein content by the NIRS method

#### **3.3 Robustness**

The robustness can be defined as the resistance of a method to small deliberate changes in the experimental conditions that provides an indication of its reliability during routine use (Vander Heyden et al., 2001; Goupy, 2005; Dejaegher & Vander Heyden, 2007). The robustness test of the NIRS method for its application in analysing wheat samples examines the potential sources of variability in responses (analytical and spectral). The factors that can cause variability in the NIRS responses refer to the operational and environmental conditions. The robustness of a method is commonly examined in an experimental design, in the intervals that slightly exceed the variation that can be expected in a routine use of the method (Vander Heyden et al., 2001). Experimental design used to determine the robustness of an applied analytical method can be based on unvariate (one-variable-at-a-time, OVAT) or multivariate approaches (multi-variate-at-atime, MVAT) (Dejaegher et al., 2007; Pojić et al., 2012).

To check the robustness of the NIRS method, the OVAT experimental design included deliberate changes of number of subsamples to be measured in single NIRS measurement, environmental and sample temperature, environmental air humidity, instrument voltage and lamp aging in order to determine how tightly controlled the experimental factors should be (Table 4). The obtained results indicated that the NIRS method for determination of protein content appeared to be robust for its application in wheat quality control regardless the deliberate changes in operational conditions (Table 4) (Pojić et al., 2012).

Fig. 8 shows the average SNV second derivative spectra of wheat samples in spectral range 850-1050 nm obtained within the OVAT experimental design. It could be noticeable that the spectral differences were very small. The largest spectral variations were observed in the spectral region around 950 nm associated with OH band for water and around 968 nm, 982 nm and 1014 nm associated with overtone bands (Infrasoft International, 2000; Pojić et al., 2012).

Mean

Lower warning limit Lower action limit

Upper warning limit

Upper action limit

Fig. 7. Shewart (control) chart for determing protein content by the NIRS method

days

time, MVAT) (Dejaegher et al., 2007; Pojić et al., 2012).

The robustness can be defined as the resistance of a method to small deliberate changes in the experimental conditions that provides an indication of its reliability during routine use (Vander Heyden et al., 2001; Goupy, 2005; Dejaegher & Vander Heyden, 2007). The robustness test of the NIRS method for its application in analysing wheat samples examines the potential sources of variability in responses (analytical and spectral). The factors that can cause variability in the NIRS responses refer to the operational and environmental conditions. The robustness of a method is commonly examined in an experimental design, in the intervals that slightly exceed the variation that can be expected in a routine use of the method (Vander Heyden et al., 2001). Experimental design used to determine the robustness of an applied analytical method can be based on unvariate (one-variable-at-a-time, OVAT) or multivariate approaches (multi-variate-at-a-

To check the robustness of the NIRS method, the OVAT experimental design included deliberate changes of number of subsamples to be measured in single NIRS measurement, environmental and sample temperature, environmental air humidity, instrument voltage and lamp aging in order to determine how tightly controlled the experimental factors should be (Table 4). The obtained results indicated that the NIRS method for determination of protein content appeared to be robust for its application in wheat quality control regardless the deliberate changes in operational conditions (Table 4) (Pojić et al., 2012).

Fig. 8 shows the average SNV second derivative spectra of wheat samples in spectral range 850-1050 nm obtained within the OVAT experimental design. It could be noticeable that the spectral differences were very small. The largest spectral variations were observed in the spectral region around 950 nm associated with OH band for water and around 968 nm, 982 nm and 1014 nm associated with overtone bands (Infrasoft International, 2000;

**3.3 Robustness** 

12.50

12.70

12.90

13.10

Protein content, % d.m.

13.30

13.50

13.70

Pojić et al., 2012).


Table 4. Values in the same column marked with the same letters are no significantly different (P <0.05, LSD test) (Pojić et al., 2012).

Fig. 8. The average second derivative spectra of wheat samples obtained at different experimental conditions within OVAT experimental design (NS 5, NS 15 – Five and fifteen subsamples in NIRS measurement; NC – Nominal conditions; AT 10, AT 30 – Ambient temperature of 10 and 30C; ST 5, ST 35 – Sample temperature of 5 and 35C; IV 200, IV 240 – Instrument voltage of 200 and 240V; NL – New lamp; EH 40, EH 80 – Environmental humidity of 40% and 80 %) (Pojić et al., 2012).

The Application of Near Infrared Spectroscopy in Wheat Quality Control 181

Dejaegher, B.; Dumarey, M., Capron, X., Bloomfield, M. S. & Vander Heyden, Y. (2007).

Delwiche, S. R. (1995). Single wheat kernel analysis by near-infrared transmittance: protein

Delwiche, S. R.; Bean, M. M., Miller, R. E., Webb, B. D. & Williams, P. C. (1995). Apparent

*Cereal Chemistry,* 72, 2, (March-April 1995), pp. (182-187). ISSN: 0009-0352. Delwiche, S. R.; Graybosch, R. A. & Peterson, J. (1998). Predicting protein composition,

Dowell, F. E.; Maghirang, E. B., Xie, F., Lookhart, G. L., Pierce, R. O., Seabourn, B. W., Bean,

Dybkaer, R. (2011). ''Verification'' versus ''validation'': a terminological comparison.

Goupy, J. (2005). What kind of experimental design for finding and checking robustness of

Hrušková, M. & Šmejda, P. (2003). Wheat flour dough alveograph characteristics predicted

Hrušková, M.; Bednářová, M. & Novotný, F. (2001). Wheat flour dough rheological

Hulasare, R. B.; Jayas, D. S. & Dronzek, B. L. (2003). Grain-grading systems, In: *Handbook of* 

ISO 5725-1:2003 (2003): Accuracy (trueness and precision) of measurement methods and

Jirsa, O.; Hrušková, M. & Švec, I. (2008). Near-infrared prediction of milling and baking

Konieczka, P. & Namieśnik, J. (2009). *Quality Assurance and Quality Control in the Analytical* 

Lauwaars, M. & Anklam, E. (2004). Method validation and reference materials. *Accreditation and Quality Assurance*, 9, 4-5, (March 2004), pp. (253-258), ISSN: 1432-0517.

(September-October 2006), pp. (529-536). ISSN: 0009-0352.

*Analytica Chimica Acta*, 595, 1-2, (July 2007), pp. (59-71). ISSN: 0003-2670. Delwiche, S. R. & Massie, D. R. (1996). Classification of wheat by visible and near-infrared

405). ISSN: 0009-0352.

(412-416), ISSN: 0009-0352.

0352.

1432-0517.

1800.

ISSN: 0003-2670.

pp. (213-218). ISSN: 1212-1800.

25), ISSN: 0260-8774.

4200-8270-8, Boca Raton.

Dekker, Inc., New York, ISBN: 0-8247-0514-9.

results—part 1: general principles and definitions

Comparison of Plackett–Burman and supersaturated designs in robustness testing.

reflectance from single kernels. *Cereal Chemistry*, 73, 3, (May/June 1996), pp. (399-

content. *Cereal Chemistry*, 72, 1, (January/February 1995), pp. (11-16), ISSN: 0009-

amylose content of milled rice by near-infrared reflectance spectrophotometry.

biochemical properties, and dough-handling properties of hard red winter wheat flour by near-infrared reflectance. *Cereal Chemistry*, 75, 4, (July/August 1998), pp.

S. R., Wilson, J. D. & Chung, O. K. (2006). Predicting wheat quality characteristics and functionality using near-infrared spectroscopy. *Cereal Chemistry,* 83, 5,

*Accreditation and Quality Assurance*, 16, 2, (February 2011), pp. (105–108), ISSN:

analytical methods?. *Analytica Chimica Acta*, 544, 1-2, (July 2005), pp. (184-190).

by NIRsystems 6500. *Czech Journal of Food Sciences,* 21, 1, pp. (28–33). ISSN: 1212-

characteristics predicted by NIRSystems 6500. *Czech Journal of Food Sciences*, 19, 6,

*Postharvest Technology - Cereals, Fruits, Vegetables, Tea, and Spices*, A. Chakraverty, A. S. Majumdar, G.S.V. Raghavan, H. S. Ramaswamy (Eds), pp. (41-55), Marcel

parameters of wheat varieties. *Journal of Food Engineering*, 87, 1, (July 2008), pp. (21-

*Chemical Laboratory – A Practical Approach*, Taylor & Francis Group, ISBN: 978-1-
