ATLD-UMAP: A novel strategy applied to 3D fluorescence data for the characterization and classification of Chinese dark tea

In the work, a novel strategy was developed to characterize and classify Chinese dark tea based on alternating trilinear decomposition combined with uniform manifold approximation and projection (ATLD-UMAP). Firstly, nine active components as targeted analytes were selected to construct qualitative and quantitative models to investigate the performance of ATLD-UMAP analysis of 3D fluorescence data. Spiked sample sets demonstrated that satisfactory results were returned with their average recoveries ranging from (91.4 ± 9.7)% to (109.3 ± 1.5)%. Subsequently, ATLD-UMAP was employed to analyze the contents of analytes in 124 real dark tea samples, revealing the presence of eight active components other than tryptophan with their average contents ranging from 0.22 mg·g −1 to 57.69 mg·g −1 . Finally, ATLD-UMAP was applied to discriminate tea samples produced from different geographical origins and processing depths, and compared with the targeted-based feature component analysis (ATLD-FCA) and the nontargeted-based principal component analysis (ATLD-PCA). The research findings demonstrated that ATLD-UMAP achieved satisfactory classification results (100.0 % accuracy, sensitivity and specificity) in simultaneously identifying dark tea's geographical origin and processing depth. In contrast, ATLD-FCA and ATLD-PCA failed to distinguish them accurately with accuracies of only 68.5 % and 64.8 %, respectively. In a word, ATLD-UMAP provides a new method for rapid authenticity identification in the dark tea industry.