Predicting and Co-Optimizing the Taste and Aroma of Green Tea During Spreading Using the TabPFN Model

To investigate how spreading conditions affect green tea taste and aroma and to develop a generalizable prediction model from small data for process optimization, this study integrated SEM, non-targeted dual-omics, and TabPFN to systematically analyze Echa No. 10 spreading. A central composite design was used. Dehydration-induced mechanical stress altered cell membrane permeability, driving non-volatile taste compound transformation and volatile aroma release. Two chemical-sensory proxies, relative polyphenol-to-amino acid ratio (R-PAR) and floral intensity index (FII), were established using ultra-high performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). A prediction model was built with these indicators and TabPFN. Multi-objective optimization yielded optimum conditions: initial moisture 76.8%, temperature 26.2 °C, relative humidity 61.5%, air speed 0.85 m/s, achieving R-PAR 0.465 and FII 125.70. Compared with response surface methodology (RSM), partial least squares regression (PLSR), and support vector regression (SVR), TabPFN showed prediction R² of 0.81 and 0.77, showing favorable applicability and predictive capability on small-sample data. This study validates TabPFN's suitability for small-sample tea processing modeling, quantifies the mapping between spreading and key taste/aroma metabolism, and provides a methodological foundation for digital precision and intelligent optimization in green tea production.