Application of NIR for Rapid Determination of Flour Quality by Machine Learning

Abstract

Wheat is the most produced cereal in the world, and its varieties, locations and planting times directly interfere with its technological quality and derivatives. Therefore, the quality control of wheat flour is of fundamental importance to guarantee the necessary adjustments in the processes due to the natural variations of the Wheat. In this context, this work aimed to evaluate the use of Near Infrared Reflectance spectroscopy (NIR) to estimate rapid results for the quality of wheat flours through Regressions Trees, a supervised Machine Learning approach. Sixty-five analyses were performed to obtain the parameters, and 20 repetitions were to confirm the results obtained. For each of the repetitions, different flours were used, which were analysed in the NIR Analyzer Spectrastar XT-R and, in the sequence, the main physical-chemical and rheological characteristics were analysed: Moisture (AACC 54-21 (2000)), ash (AACC 08-01 (1995)). Colour by Minolta CR400 colourimeter, total and dry gluten (Yucebas Machinery, Mod. Y070073, Turkey), farinographic characteristics (AACC 54-21 (1995)), using Farinografo®?-AT and alveography (AACC 54-3- (2000) by Alveograph Chopin. It was possible to build decision trees that related physical-chemical and rheological variables with the variables from the NIR, resulting in decision rules with reasonable accuracy. In this way, it is concluded that the NIR can be used as a rapid test for the quality control of wheat flour. However, it is necessary to confirm the results of the rheological analyses for more accurate results.