Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra Original Research Article

In the Italian context, bread wheat flour is commercially classified in different quality categories on the basis of a Synthetic Index of Quality (Indice Sintetico di Qualità, ISQ), which is defined by means of specific parameters, i.e., hectolitric weight, falling number, protein content, alveographic indexes (W, P/L) and farinograph stability. The analyses involved in the determination of these parameters are expensive, time consuming and require specialized personnel, thus there is concern to develop alternative methods to be applied during the commercial transactions, when the products need to be characterized in very short times. For this reason, a fast technique such as an automated classification on the basis of NIR spectra acquired on the wheat flour samples could be a very useful tool. In this work, various wheat flour samples belonging to four different ISQ classes have been analysed by means of NIR spectroscopy, and the obtained spectra have been classified both by SIMCA applied to the signals subjected to different pretreatment methods, and by using a wavelet-based feature selection/classification algorithm, called WPTER. Due to the high overlap of the two intermediate quality classes, it was not possible to classify all the data set signals. However, when considering only the two extreme categories, an acceptable degree of class separation can be gained after feature selection by WPTER. Moreover, this approach allowed us to locate the NIR spectral regions that are mainly involved in the assignment of the wheat flour samples to these two quality categories.