Multiple carbon quantum dots-enhanced excitation-emission matrix fluorescence strategy for accurate vintage discrimination of Anhua dark tea

Accurate discrimination of dark tea vintages is crucial for quality assurance and market value assessment. In this study, a multiple carbon quantum dots-enhanced excitation-emission matrix (MCQDs-EEM) fluorescence strategy was developed for the rapid identification of Anhua dark tea from different production years. Three functional carbon quantum dots with complementary responses to pH, tea polyphenols, and amino acids were combined into an integrated fluorescent chemical sensor, generating enhanced and information-rich fluorescence fingerprints upon interaction with tea infusions. The MCQDs-EEM dataset was decomposed using the Alternating Trilinear Decomposition (ATLD) algorithm, which revealed four chemically significant components that showed differences in Anhua dark tea from thirteen different years. Based on the MCQDs-EEM data, partial least squares discriminant analysis (PLS-DA) and k -nearest neighbors ( k- NN) models were constructed. The PLS-DA model achieved 100% classification accuracy for both the training and test sets, while the k -NN model attained accuracies of 96.59% and 100%, respectively. These results demonstrate clear superiority over the traditional fluorescence strategy based on carbon quantum dots (CQDs-FL), which relies on the splicing of three distinct emission spectra. The MCQDs-EEM strategy not only simplifies the implementation process but also demonstrates higher accuracy, which can be regarded as an effective tool for tea vintage authentication and quality control.