**2.2.1 The role of NIRS networks**

170 Infrared Spectroscopy – Life and Biomedical Sciences

etc. Most of these parameters are dependent on the protein-proteinase complex of wheat

First attempt to determine the functional properties of wheat was made by Rubenthaler and Pomeranz (1987) by development of NIRS model for prediction of water absorption, mixing time and loaf volume. The following work presented by Williams et al. (1988) showed the potential of NIRS to predict Farinograph stability, Extensigraph energy and Alveograph deformation energy. Further research reported by Pawlinsky and Williams (1998), Hrušková et al. (2001), Hrušková and Šmejda (2003), Miralbés (2003, 2004), Dowell et al. (2006) and Vázquez et al. (2007) were carried out by using analyzers of higher generation – scanning monochromators. The efficiency of the models developed were affected by the form of the samples used (whole grain, ground grain or flour), by the composition of the sample sets which were insufficiently variable and by the inherent variability of the reference

One of the first attempts to develop NIRS calibration model for prediction of protein composition was made by Delwiche et al. (1998). It was possible due to the fact that the main fractions of gluten - glutenin and gliadin, exhibit some differences in their NIR spectra which enable them to be determined in mixtures with starch (Wesley et al., 1999) Total glutenin, insoluble glutenin and gliadin contents can also be measured in whole wheat kernel by NIRS against HPLC as a reference method (Delwiche et al., 1998; Dowel et al., 2006; Wesley et al., 1999; Seabourn et al., 1998; Wesley, 2001) with sufficient accuracy for screening purposes in breeding programs. Although some authors has recommended the use of instrument with a monochromator in reflectance mode over the range of 2000 to 2300 nm for these application (Wesley, 2001),it has been proved that use of instruments in transmittance mode with narrower spectral range below 2000 nm could also be applicable (Dowell et al., 2006; Scholz et al., 2007). Concerning the carbohydrate complex of grain, the NIR spectra of amylose and amylopectin are very similar since they consist of the same glucose unit. Therefore, very little progress has been made in estimating the quality of carbohydrate components in wheat. Scanning visible–NIR spectrophotometers are often applicable for research and development purposes, since this application requires wider spectral ranges such as 400-2500 nm, 400-1700 nm, 1100-2500 or 1000-2600 nm. Also, since breeders commonly face with insufficient quantity of samples, the development of NIRS

Methods based on near infrared spectroscopy are accepted worldwide for cereal quality control in trade, especially according to the fact that it is capable of generating results for several quality parameters rapidly and in a non-destructive way. Although different countries established their own systems for classifying wheat on the basis of different quality parameters, wheat grading systems is commonly based on the wheat protein content (Williams, 2007; Hulasare et al., 2003; Váradi et al., 1999). The price of wheat is dependent on the protein content, often with substantial price increments between grades. Measuring protein content in wheat and wheat flour has been demonstrated as successful NIRS application due to its strong and broad absorption bands in the NIR region which affect easy calibration model development. Therefore, the segregation or blending grain prior to delivery is inconceivable without the use of NIR technology. When using NIR analysis for

grain and the condition of carbohydrate complex as well (Williams, 2007).

single kernel characterization systems has been initiated.

**2.2 The role of NIRS in cereal trade** 

rheological methods.

A significant advance in the application of the NIRS technique in cereal trade has been achieved by the development of global ANN calibration models, and by the establishment of measurement infrastructure composed of multiple NIRS devices interconnected in the network. Operation of the NIRS instruments through the network significantly improved the routine application of NIRS method, eliminated specified shortcomings and significantly facilitated the application of the NIRS method for the end-users. Hence, the independent measurements of protein content that are internationally equivalent have triggered off the establishment the NIRS networks in many countries around the world (Büchman, 1996; Pojić & Mastilović, 2006).

NIRS networks are formed in order to:


Establishing a network of NIRS devices allows achieving of the same level of accuracy of determining the protein content regardless of location of devices. In addition, the NIRS networks ensure reliability and uniformity of quality control of grain crops as well as simplification of procedures for calibration model monitoring and their improvement.

NIRS network consists of two to several hundred or even thousands of NIRS devices that are controlled and configured from the central so-called master device. The initial idea of operation of NIRS instruments through the network came from FOSS Analytical AB, and currently the most impressive networks worldwide consist of FOSS's instruments - scanning monochromator Infratec 1241 Grain Analyzer. The success of such measurement infrastructure is highly dependent on the network organization and procedures and tasks proposed and divided between:


Procedures that enable undisturbed functioning of the NIRS network and confirm compliance of results obtained by wet chemical tests on the one hand and consistency of results of individual devices with the central (master) device on the other hand is achieved through the following activities:

The Application of Near Infrared Spectroscopy in Wheat Quality Control 173

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

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

N

i 1

 

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

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

SEP

2 r p

(2)

(y y )

N 1

protein content used in the transmittance mode (FOSS Analytical, Denmark).

**3.1 Accuracy** 

number of samples.

calibration model development procedure:

