Rapid assessment of green tea storage quality using copper-amino acid nanozyme sensor arrays

Green tea quality declines during storage, but rapid assessment methods are lacking. Six key catechins were identified as storage time markers through sensory and chemical analysis. Copper-amino acid (Cu-AA) nanozymes with peroxidase-like activity were prepared to catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine in the presence of H₂O₂, producing a blue color, which is inhibited by catechins. Seven colorimetric sensor arrays (CSAs) using different Cu-AA nanozymes were constructed to detect catechin contents. For catechin reference standards, the CSA correlation coefficients range from 0.9623 to 0.9979. Machine learning models (backpropagation neural networks and random forest) achieved maximum accuracy >91.89% in predicting storage duration. Least squares support vector regression provided a calibrated prediction coefficient of 0.9328 and a prediction-deviation ratio of 4.4230 for catechin contents. The CSAs enable real-time monitoring of storage duration and catechin levels, offering a tool to evaluate stored green tea quality.