Display omitted •Colorimetric sensor with spectral was attempted to classify different grades matcha.•Different classification methods were compared in developing identification models.•Back-propagation artificial neural network model had optimal prediction performance.•Volatile compounds differences between matcha grades were verified by HS-SPME-GC–MS. Matcha tea powder is considered as an integral part of a healthy diet due to its enormous health benefits. In the current study, visible near-infrared (Vis-NIR) and colorimetric sensor array (CSA) techniques are applied to identify the matcha grades. The color-sensitive dyes reacted with their volatile compounds and the information was recorded by Vis-NIR spectroscopy, namely Vis-NIR-CSA. Specifically, three linear and three nonlinear classification models were compared, yielding the optimal identification rate by the back-propagation artificial neural network (BPANN) model with 99% and 98% in the calibration and prediction sets, respectively. The results indicated the sensor combined with the BPANN model could be applied satisfactorily in identification of different matcha grades. Additionally, the variations in volatile compounds between different matcha grades and eight characteristic volatile compounds were identified, which verified the sensor identification results. This study provided a scientific and novel method for the stability of matcha quality in production.