Surface-Enhanced Raman Scattering Integrating with Machine Learning for Green Tea Storage Time Identification.

It still remains a great challenge for the rapid and accurate identification of complex samples, especially those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case, green tea products with different storage times. The surface functionalized Ag NPs was used as SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA) based support vector machines (SVM) classification was employed to extract the key spectral features and identify green tea with different storage time. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multi-class SVM classifier successfully recognized green tea with different storage time at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates the SERS-based PCA-SVM platform might be a facile and reliable tool for identification of complex matrices with subtle differentiations.