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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 (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.

Energy efficiency is one of the most important factors in wireless sensor networks. In this paper, we present a centralized “cluster-chain” power efficient routing algorithm SOC-IACO which consists of self-organizing clustering algorithm SOC and intelligent Ant Colony Algorithm IACO. The Base Station (BS) firstly uses SOC algorithm to form clusters and select associated cluster heads (one of which will be the main head) and a cluster- heads chain will be constructed; then BS uses IACO algorithm to construct a near-optimal nodes chain for each cluster. Data will be aggregated and transmitted along cluster nodes chain to cluster head. Then aggregated data of every head will be transmitted along cluster-heads chain to BS. Simulation results show that SOC-IACO reduces the node energy consumption and effectively extend the network lifetime by considering nodes distribution in clustering process and using double-layer chain structure to transmit data.