A novel ZIF-8 mediated nanocomposite colorimetric sensor array for rapid identification of matcha grades, validated by density functional theory

Abstract

The rapid and intelligent assessment of matcha grades remains crucial in quality control. This study proposed a colorimetric sensor array (CSA) mediated with zeolitic imidazolate framework-8 (ZIF-8) to identify matcha grades. The ZIF-8 mediated CSA was innovatively developed by encapsulating the pH indicator and metal porphyrin within the ZIF-8 structure, improving their stability and functionality. ZIF-8 mediated CSA exhibited significantly increased sensitivity towards matcha samples driven by the preconcentration of volatile organic compounds (VOCs) facilitated by ZIF-8. As a result, the response signal values of the CSA increased by 1.13–4.75 times after ZIF-8 mediation. Subsequently, qualitative models for identifying matcha grades were established using K-Nearest Neighbor and Artificial Neural Network (ANN). The ANN model showed the higher discrimination accuracy. Compared with common CSA, the ANN model based on ZIF-8 mediated CSA achieved a better identification rate of 95 %, and recognition accuracy was improved by 7.5 % in the model. These results indicated that the ZIF-8 mediated CSA with a porous structure demonstrated enhanced specificity in capturing VOCs. Ultimately, the density functional theory has confirmed that the ZIF-8-mediated CSA exhibits high selectivity towards matcha's characteristic VOCs. These results highlight the potential of this novel CSA for the rapid identification and grading of matcha.