Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy

Background: The quality discrimination of dairy products is an important basis on which to achieve quality assurance. Objective: Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed. Method: The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined. Conclusions: We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach. Highlights: A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.