Quantitative and qualitative analysis of buffalo milk adulteration using Raman spectroscopy and chemometric-deep learning models

文献类型： 外文期刊

第一作者： Li, Henggang

作者： Li, Henggang;Li, Wangchang;Xie, Qiyang;Cao, Duming;Xiao, Dilin;Yang, Xiaogan;Li, Henggang;Xie, Qiyang;Cao, Duming;Shang, Jianghua

作者机构：

关键词： Food adulteration; Dairy authentication; Convolutional neural networks; Partial least squares discriminant analysis; Chemometrics; Spectral preprocessing; Non-destructive analysis

期刊名称：FOOD CONTROL （ 影响因子：6.3； 五年影响因子：6.1 ）

ISSN： 0956-7135

年卷期： 2026 年 179 卷

页码：

收录情况： SCI

摘要： Buffalo milk is nutritionally rich but vulnerable to adulteration, posing challenges to food safety. This study analyzes buffalo milk, soybean milk, Holstein milk, and five common additives-ammonium chloride, urea, sodium bicarbonate, sodium citrate, and sucrose-using Raman spectroscopy. Six spectral preprocessing methods were systematically evaluated to enhance model performance. For qualitative detection, PLS-DA models combined with Multiplicative Scatter Correction (MSC) preprocessing achieved excellent classification accuracy (up to 100 %) for pure buffalo milk, water, and soybean milk adulteration. For quantitative analysis, both PLS and MSC-CNN regression models were developed. The MSC-CNN model achieved high predictive performance for sodium bicarbonate (R-2 = 0.97) and sodium citrate (R-2 = 0.93), with RMSEP <5 % of full scale. Detection limits were as low as 17.4 mg/kg for sodium bicarbonate and 20.9 mg/kg for sodium citrate, meeting practical sensitivity requirements. Compared with existing PLS-based methods, our approach improved predictive accuracy and expanded low-concentration detection. The proposed Raman-deep learning strategy offers a rapid, accurate, and non-destructive solution for milk adulteration monitoring and quality control.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序