卷积神经网络模型结合近红外光谱对特种乳掺水的鉴别OA

To address the increasingly severe issue of water adulteration in the non-bovine milks market,this study proposed a rapid,non-destructive method for identifying multiple types of adulterated non-bovine milks based on portable near-infrared spectroscopy combined with convolutional neural networks(CNN).Spectral data were collected for four types of non-bovine milks,camel,horse,donkey,and yak milks,as well as samples of these milks diluted with water at varying concentrations(2％-80％,V/V).We compared the effects of standard normal variate transformation(SNV)and Savitzky-Golay filter smoothing preprocessing on the performance of partial least squares discriminant analysis(PLS-DA)and CNN models.The results showed that non-bovine milks exhibited significant characteristic absorption of moisture at 6 882 and 10 200 cm-1.The CNN model with SNV preprocessing achieved the best performance,with a classification accuracy of 98.71％on the test set,and its precision and recall were superior to those of traditional PLS-DA model.This model not only accurately distinguished between milks from different species but also exhibited high sensitivity in detecting water adulteration as low as 2％.The present study demonstrates that the discrimination system based on a portable near-infrared spectrometer and CNN can effectively reduce matrix interference in minor species milks.