**The Application of Near Infrared Spectroscopy in Wheat Quality Control**

Milica Pojić1, Jasna Mastilović1 and Nineta Majcen2 *1University of Novi Sad, Institute of Food Technology 2Metrology Institute of the Republic of Slovenia, Celje 1Serbia 2Slovenia* 

#### **1. Introduction**

The application of near infrared spectroscopic technique for the quantitative analysis of food products and commodities is nowadays widely accepted. However, 160 years passed from the discover of near infrared part of the spectrum to its first analytical application which is related to the work of Karl Norris who firstly demonstrated the potential of the NIRS in quantitative analysis particularly for prediction of moisture and protein content in wheat. The intense development of this technique during the last 50 years has been challenged by the development of powerful computers, softwares and chemometric tools, since the NIRS data processing is quite demanding task. The near infrared spectroscopy is an instrumental technique based on measuring the intensity of reflectance or intensity of transmission of radiation from the near infrared region of the electromagnetic spectrum (800-2500 nm) by the test sample. The intensity of the reflection and transmission depends on the rate of absorption of radiation by the sample, which leads to excitation of hydrogen bonds (CH, NH, OH). As the tested samples are very complex in composition, it happens that on the same wavelength, several organic bonds involving hydrogen vibrate producing overlapped spectral bands. Therefore, the resulting NIR spectrum looks like a slightly wavy line with no clearly defined features, with very broad and overlapped molecular overtone and combination bands, which complicate to assign them to specific chemical constituent and make impossible to determine the direct relationship between the concentration of ingredients of interest and the absorbed radiation energy (Fig. 1).

Due to the significant overlapping of NIR bands, the prerequisite for the NIRS application is the development of the calibration model which relates the concentration of certain analyte found in a sample to the spectral data collected from that sample. Calibration model development process implies the extraction of useful information from the NIR spectra by applying chemometrics methods. Multivariate calibration techniques (e.g. principal components analysis, partial least squares, or artificial neural networks) are often employed to extract the desired chemical information from the spectral data. Calibration model allows relating the NIR optical data with the compound (or property) of interest that is used to define the quality of the sample:

$$\begin{aligned} [\mathbf{C}] &= [\mathbf{A}][\beta] + [\varepsilon] \end{aligned} \tag{1}$$

The Application of Near Infrared Spectroscopy in Wheat Quality Control 169

facilitate the application of this method, it is necessary to develop the calibration model by which the obtained spectral data are translated into the required result – the content of the selected compound of interest, and it is often necessary to check and update the calibration models due to changes in the sample matrix. However, this restriction has been significantly overcome by development and application of calibration models based on artificial neural networks that handle very large data set and proved to be very accurate, stable, transferable

The potential of wheat to be processed in wide range of different final products gives it the significance of upmost grain of commerce (Williams, 2002). Application of the NIRS technique in wheat quality control has been characterized by rapid development from prediction of major constituents in wheat grains (moisture, protein, oil starch, cellulose) to prediction of functional properties of wheat that define its capability to meet the requirements of the intended purposes (production of bread, pastry, cookies, pasta). Since the functionality of the commodity is strongly affected by its physico-chemical properties which do not manifest characteristics absorption in NIR spectral region, the use of NIRS to predict functionality is based on the relationship between physico-chemical properties and certain constituent having absorptions in NIR region (protein, oil, starch etc) (Williams, 2007). The value of wheat grain is dependent on its composition, functionality and safety, that all have an equal importance in

Fig. 2. The relationship between different NIRS applications in wheat quality control

The breeding purposes require the knowledge on both composition and functional properties of grain, whilst the functionality of wheat grain has always been an issue of a great concern for wheat breeders. Functionality in wheat includes prediction of milling yield, kernel texture, rheological parameters of dough, loaf volume, product appearance,

**2.1 The role of NIRS in cereal breeding** 

and therefore globally applicable (Büchman et al, 2001).

wheat breeding, trade and processing (Fig. 2) (Williams, 2002).

Fig. 1. NIR reflectance spectrum of wheat flour (1100-2500 nm) recorded by FOSS's NIRSystem 6500

Also, the application of NIRS technique has been extended to determination of certain functional properties of tested samples which do not represent the unique chemical entities which manifest absorption in NIRS spectral region, but manifest relationship with certain constituents that can be used as a basis for calibration model development. So far, a number of applications of the NIRS technique have been demonstrated having commercial and/or scientific significance that was developed on the basis on different NIR spectral ranges, different ways of recording and processing of spectra, different sample presentations, various chemometrics techniques used for calibration development and different use of validation statistics.
