乳业科学与技术2026，Vol.49Issue(3)：41-47,7.DOI:10.7506/rykxyjs1671-5187-20260205-011
卷积神经网络模型结合近红外光谱对特种乳掺水的鉴别
Identification of Water Adulteration in Non-bovine Milks Using Near-Infrared Spectroscopy Combined with a Convolutional Neural Network Model
摘要
Abstract
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.关键词
近红外光谱/特种乳/掺水鉴别/卷积神经网络Key words
near-infrared spectroscopy/non-bovine milks/water adulteration identification/convolutional neural network分类
轻工纺织引用本文复制引用
邵子祥,托尔坤·买买提..卷积神经网络模型结合近红外光谱对特种乳掺水的鉴别[J].乳业科学与技术,2026,49(3):41-47,7.基金项目
新疆战略人才培养计划一流科技领军人才项目(XJRC-2025-KJ-PY-KJLJ-126) （XJRC-2025-KJ-PY-KJLJ-126）
