Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification
Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification
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DOI：
10.1016/j.dche.2022.100020
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发表时间：
2022-06-01
期刊：
影响因子：
--
通讯作者：
Wu, Hung-Jen
中科院分区：
文献类型：
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作者：
Hu, Qiang;Sellers, Chase;Wu, Hung-Jen
表面增强拉曼光谱 (SERS) 是分子识别的强大工具。然而，分析复杂样品仍然是一个挑战，因为当单个样品中存在多种分析物时，SERS 峰可能会重叠，从而混淆特征。此外，由于 SERS 基底不均匀，SERS 信号增强常常存在高度可变性。广泛用于面部识别的机器学习分类技术是克服 SERS 数据解释复杂性的优秀工具。在此，我们报道了一种通过集成 SERS、特征提取和机器学习分类器对咖啡饮料进行分类的传感器。一种多功能且低成本的 SERS 基底（称为纳米纸）用于增强咖啡饮料中稀释化合物的拉曼信号。使用主成分分析（PCA）和主成分判别分析（DAPC）两种经典的多元分析技术来提取显着的光谱特征，并评估各种机器学习分类器的性能。 DAPC 与支持向量机 (SVM) 或 K 最近邻 (KNN) 的组合显示了咖啡饮料分类的最佳性能。这种用户友好且多功能的传感器有潜力成为食品行业实用的质量控制工具。
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.