Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China; Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China.
Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou 310012, China.
Food Chem. 2023 Dec 15;429:136986. doi: 10.1016/j.foodchem.2023.136986. Epub 2023 Jul 24.
The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.
在烘焙食品的制造过程中，非法将非乳制品奶油掺入乳脂奶油中，这极大地阻碍了乳制品行业的蓬勃发展。在本研究中，建立了一种基于快速蒸发电离质谱（REIMS）脂质组学模式识别与机器学习算法相结合的方法。采用多元统计分析共挑选出 26 个重要离子作为显著特征，用于区分乳脂奶油和非乳制品奶油。此外，采用判别分析、决策树、支持向量机和神经网络分类器，利用机器学习模型对非乳制品奶油、乳脂奶油和微量非乳制品奶油掺假的乳脂奶油进行分类。通过超参数优化和特征工程，这些方法的准确率达到了 98.4-99.6%。这种基于机器学习引导的 REIMS 模式识别的人工智能方法可以准确识别搅打奶油的掺假情况，有助于打击食品欺诈。
