Exploring the colorimetric sensing mechanism of metal porphyrin on the degree of roasting from the perspective of density functional theory.

Abstract

Aroma quality is a key indicator of the degree of roasting of large-leaf yellow tea (LYT). In this study, a colorimetric sensing array (CSA) composed of tetraphenylporphyrin (TPP) was designed for the rapid quantitative detection of key volatile organic compounds (VOCs) in LYT. First, the responses of the CSA system were compared under three environmental conditions. The response intensity of the TPPs to the VOCs was analysed using density functional theory (DFT). Finally, on the basis of the DFT calculations, a streamlined CSA sensor incorporating a least-squares support vector machine model was designed for the quantitative detection of 2-ethyl-3,5-dimethylpyridazine, 2,5-dimethylpyrazine, benzaldehyde, dihydro-2-methyl-3-furanone, linalool, and trans-β-ionone at levels ranging from 0.005 to 5 ppm. The predictive coefficients and relative predictive deviations of the quantitative model ranged from 0.82 to 0.93 and 1.75 to 2.65, respectively. This study provides a theoretical basis for the construction of CSA and a novel perspective on the monitoring of tea processing and quality.