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.