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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 (1stD) 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

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Raman spectroscopy and surface‐enhanced Raman spectroscopy (SERS) have been extensively explored in the design of accurate, transparent, and conclusive food safety and quality control assays. Its hyphenation with chemometric algorithms is instrumental in securing safe food campaigns. To provide valuable recommendations and meet the growing demands for food screening, the current study begins with a brief description of the Raman spectroscopy and SERS theory followed by a comprehensive overview of spectral preprocessing, qualitative algorithms, variable selection methods, and quantitative algorithms. The review emphasizes on the importance of food monitoring practices using multivariate regression models. The applicability of the distinct chemometrics modes toward monitoring pesticide, food and illicit additives, heavy metals, pathogens, and its metabolites in Raman spectroscopy and SERS is covered in dairy, poultry, oil, honey, beverages, and other selected food matrices. Its pertinence toward classification and/or discrimination in food quality and safety monitoring and authentication is examined. Finally, it also complies with the limitations, key challenges, and prospects. The chemometrics processing spectra implemented with simpler or no complicated sample pretreatment step make Raman spectroscopy/SERS technique a potential approach that is expected to achieve simultaneous and fast detection of multiple analytes in food matrices.