Chu Chu, Wang Haitong, Luo Xuelu, Wen Peipei, Nan Liangkang, Du Chao, Fan Yikai, Gao Dengying, Wang Dongwei, Yang Zhuo, Yang Guochang, Liu Li, Li Yongqing, Hu Bo, Abula Zunongjiang, Zhang Shujun
Frontiers Science Center for Animal Breeding and Sustainable Production, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Foods. 2023 Oct 21;12(20):3856. doi: 10.3390/foods12203856.
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 R = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and R = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.
用价格较低的牛奶掺假高价牛奶以获取额外利润，会对消费者健康和市场产生不利影响。在本研究中，使用了纯水牛乳（BM）、山羊乳（GM）、骆驼乳（CM），以及它们与5 - 50%（体积/体积）牛乳或水的混合物。采用中红外光谱法（MIRS）结合现代统计机器学习方法，对BM、GM和CM中牛乳或水的掺假情况进行鉴别和定量分析。与偏最小二乘法（PLS）相比，现代统计机器学习方法——尤其是支持向量机（SVM）、投影寻踪回归（PPR）和贝叶斯正则化神经网络（BRNN）——在掺假检测方面表现出卓越性能。不同预测特征的最佳预测模型如下：由SVM开发的二元分类模型实现了CM - 牛乳以及GM/CM - 水混合物的区分。PLS实现了BM/GM - 牛乳和BM - 水混合物的区分。上述所有模型的分类准确率均为100%。SVM用于开发多分类模型，以识别BM、GM和CM中牛乳的高、低比例，以及BM和GM中水掺假的高、低比例，正确分类率分别为94%、100%、100%、99%和100%。此外，还开发了基于PLS的模型，用于识别CM中水掺假的高、低比例，正确分类率为100%。使用主成分分析（PCA）+ BRNN开发了用于定量BM中牛乳的回归模型，RMSEV = 5.42%，R = 0.88。使用PCA + PPR开发了用于定量BM中水掺假的回归模型，RMSEV = 1.70%，R = 0.99。与PLS相比，现代统计机器学习更有效地提高了MIRS预测BM、GM和CM掺假情况的准确性。
