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An overview on the applications of typical non-linear algorithms coupled with NIR spectroscopy in food analysis
(Food Engineering Reviews, 2020) Zareef, M.; Chen, Q.; Hassan, M. M.; Arslan, M.; Hashim, M. M.; Ahmad, W.; Kutsanedzie, F. Y.; Agyekum, A. A.
Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast nature, precision, control, accuracy, repeatability, and reproducibility has been extensively employed in most industries for food quality measurements. Its coupling to different modeling techniques has been identified as a way of improving the accuracy and robustness of non-destructive measurement of foodstuffs. This review provides an overview of the application of non-linear algorithms in food quality and safety specific to NIR spectroscopy. The review also provides in-depth knowledge about the principle of NIR spectroscopy along with different non-linear models such as artificial neural network (ANN), AdaBoost, local algorithm (LA), support vector machine (SVM), and extreme learning machine (ELM). Moreover, non-linear algorithms coupled with NIR spectroscopy for ensuring food quality and their future perspective has been discussed.