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.  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.
SERS
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.  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.
PCA
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 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.
SVM