Multidimensional Surface-Enhanced Raman Scattering (SERS) Strategy for Tea Differentiation

Surface-enhanced Raman scattering (SERS) displays great potential for food analysis due to the molecular fingerprint providing capability. Herein, we explore the effectiveness of the strategy based on multidimensional SERS fingerprint analysis for tea differentiation. It is known that a complex target analyte can generate different SERS signal responses once a combination of functionalized SERS substrates has been introduced (multidimensional SERS). The hypothesis is that increasing the spectral data dimension could improve the possibility and accuracy for analyte discrimination, in this case, tea products. Bearing this in mind, multidimensional SERS spectra of various tea products from diverse SERS substrates were obtained. Then, the tea differentiation, including tea category, geographical origin, and grade level, was achieved with the help of principal component analysis (PCA) and linear discriminant analysis (LDA). This work indicates that multidimensional SERS analysis will probably present a great application prospect in food authenticity identification.