Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms

Abstract

Tea polyphenols are one of the most important ingredients in Qingzhuan tea. Usually, a chemical method is used to determine tea polyphenols content, but it was time-consuming and laborious. This paper attempted to use near infrared spectroscopy (NIRS) technology combined with three partial least squares methods to predict tea polyphenols content quickly and nondestructively. The partial least squares (PLS), synergy interval PLS (siPLS) and genetic algorithm based PLS (gaPLS) were used to establish prediction models, the performance of the final model was showed by root mean square error of prediction (RMSEP) and determination coefficient (Rp²) in prediction set. The best spectral preprocessing method was multivariate scattering correction (MSC); the RMSEP and R_p² of PLS model were 0.145% and 0.8974, respectively; the siPLS model was established with four spectral regions (4377.6 cm^-1-4751.7 cm^-1, 4755.6 cm^-1-5129.7 cm^-1, 6262.7 cm^-1-6633.9 cm^-1 and 7386 cm^-1-7756.3 cm^-1), whose RMSEP and R_p² were 0.0652% and 0.9235, respectively; the gaPLS model was established with 36 spectra dada points and showed the best performance (RMSEP=0.0624%, Rp²=0.9769) compared with the PLS and si-PLS models. Therefore, the application of near infrared technology combined with the gaPLS method could predict tea polyphenols content in Qingzhuan tea more accurately and rapidly.

Keywords:
Qingzhuan tea; tea polyphenols; near infrared spectroscopy; partial least squares; genetic algorithm