Zhejiang Engineering Research Institute of Food & Drug Quality and Safety, Zhejiang Gongshang University, Hangzhou 310018, P.R. China.
Zhejiang Engineering Research Institute of Food & Drug Quality and Safety, Zhejiang Gongshang University, Hangzhou 310018, P.R. China.
J Dairy Sci. 2024 Oct;107(10):7609-7618. doi: 10.3168/jds.2024-24886. Epub 2024 Jun 20.
This study established a method for rapid classification of milk products by combining MALDI-TOF MS analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as potential markers, integrated machine learning strategies based on 6 feature selection techniques combined with support vector machine (SVM) classifier were implemented to screen the informative features and classify the milk samples. The models were evaluated and compared by accuracy, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The results showed the least absolute shrinkage and selection operator (LASSO) combined with SVM performs best, with prediction accuracy of 100% ± 0%, AIC of -360 ± 22, and BIC of -345 ± 22. Six features were selected by LASSO and identified based on the available protein molecular mass data. These results indicate that MALDI-TOF MS coupled with machine learning technique could be used to search for potential key targets for authentication and quality control of food products.
本研究建立了一种通过 MALDI-TOF MS 分析与机器学习技术相结合快速分类乳制品的方法。以 2 种不同类型的乳制品分析为例。为了选择关键变量作为潜在标志物，采用了基于 6 种特征选择技术与支持向量机（SVM）分类器相结合的集成机器学习策略，筛选出信息特征并对乳样进行分类。通过准确性、赤池信息量准则（AIC）和贝叶斯信息量准则（BIC）对模型进行评估和比较。结果表明，最小绝对值收缩和选择算子（LASSO）与 SVM 结合效果最佳，预测准确率为 100%±0%，AIC 值为-360±22，BIC 值为-345±22。通过 LASSO 选择了 6 个特征，并根据可用的蛋白质分子量数据对其进行了鉴定。这些结果表明，MALDI-TOF MS 结合机器学习技术可用于寻找食品认证和质量控制的潜在关键目标。
