文献类型： 外文期刊

作者： Chu, Chu ¹ ; Wang, Haitong ¹ ; Luo, Xuelu ¹ ; Wen, Peipei ¹ ; Nan, Liangkang ¹ ; Du, Chao ¹ ; Fan, Yikai ¹ ; Gao, Dengying ² ; Wang, Dongwei ¹ ; Yang, Zhuo ¹ ; Yang, Guochang ¹ ; Liu, Li ¹ ; Li, Yongqing ¹ ; Hu, Bo ³ ; Abula, Zunongjiang ³ ; Zhang, Shujun ¹ ;

作者机构： 1.Huazhong Agr Univ, Minist Educ, Frontiers Sci Ctr Anim Breeding & Sustainable Prod, Wuhan 430070, Peoples R China

2.Huazhong Agr Univ, Coll Anim Sci & Technol, Minist Educ, Key Lab Anim Genet Breeding & Reprod, Wuhan 430070, Peoples R China

3.Xinjiang Acad Anim Sci, Qual Stand Institue Anim Husb, Urumqi 830012, Peoples R China

关键词： mid-infrared spectroscopy; machine learning; milk adulteration; milk

期刊名称：FOODS （ 影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2023 年 12 卷 20 期

页码：

收录情况： SCI

摘要： Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.