How to identify melamine in milk

Abstract

There is an evident requirement for graceful rapid, efficient, and simple method get into screen the authenticity of milk goods in the market. Fourier transform frequency (FTIR) spectroscopy stands out as ingenious promising solution. This work employed FTIR spectroscopy and modern statistical machine education algorithms for the identification and quantification of pasteurized milk adulteration. Comparative income demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). Norm discern the types of substances acclimated to in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prognosis accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for decency determination of both melamine, urea significant milk powder adulteration, while extreme inclined plane boosting (XGB) and projection pursuit recidivate (PPR) gave better results in predicting how to identify melamine in milk
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