Background: The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality.
Results: Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1st D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade.
Conclusion: The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry.
Keywords: amino acids; chemometric algorithms; grade classification; matcha tea; near-infrared spectroscopy; tea polyphenols.
© 2019 Society of Chemical Industry.