Multimodal Transformer Fusion of PTR‐ToF‐MS Volatiles and Targeted Non‐Volatile Metabolites Enables Data‐Driven Grading of Baimudan White Tea
Dandan Zhang, Suk‐Hwan Hong, Xiaojing LiABSTRACT
Rationale
Commercial grading of Baimudan white tea still relies largely on sensory evaluation and would benefit from a rapid, objective analytical approach. This study investigated whether PTR‐ToF‐MS volatile fingerprints combined with targeted non‐volatile metabolite measurements could support data‐driven grading of four official Baimudan grades.
Methods
Headspace volatiles were profiled by proton‐transfer‐reaction time‐of‐flight mass spectrometry (PTR‐ToF‐MS), and non‐volatile metabolites were quantified by HPLC, amino acid analysis, and soluble sugar assay. A total of 120 samples (30 per grade) were divided into a training set ( n = 96) and an independent prediction set ( n = 24). A two‐stream Transformer encoded the volatile and non‐volatile modalities separately and fused them for four‐class classification.
Results
The multimodal model achieved 95.8% accuracy (23/24) and a macro‐F1 score of 0.958 on the independent prediction set, with a single misclassification between adjacent high grades. Repeated stratified five‐fold cross‐validation within the training set showed stable performance. SHAP and attention analyses indicated that high‐grade teas were associated with floral/sweet volatile ions and higher amino acids and soluble sugars, whereas lower grades were associated with greener/woody volatile ions and kaempferol‐related markers.
Conclusions
Within the current dataset, integrating PTR‐ToF‐MS volatile fingerprints with targeted non‐volatile metabolites provides an objective and chemically interpretable approach for instrument‐assisted grading of Baimudan white tea.