An AI-Assisted Workflow for Tea Product Discrimination and Moisture Prediction Using Portable NIR Spectroscopy

Portable near-infrared (NIR) spectrometers enable rapid tea analysis, but the heterogeneity of tea leaves can reduce spectral representativeness and modelling reliability. This study evaluated the influence of sampling configurations on tea product discrimination and moisture prediction using two portable NIR spectrometers and developed a domain-specific NIR-analysis skill to support the analytical workflow. Ten tea products were analysed in ground and unground states using the NIR-R210 and NeoSpectra Scanner. Sixteen preprocessing methods were evaluated with partial least squares and support vector machine models. Classification and regression models were selected using stratified four-fold and leave-one-product-out cross-validation, respectively. Within the investigated product and batch range, the NeoSpectra Scanner achieved 100% classification accuracy for both sample states in validation, whereas the NIR-R210 achieved 100% and 92.5%, respectively. Support vector regression achieved the best moisture-prediction validation performance for unground NeoSpectra Scanner samples (R2 = 0.95; RMSEP = 0.330%). External validation showed decreased performance, particularly for unground NIR-R210 samples (classification accuracy, 80.0%; R2 = 0.68; RMSEP = 0.797%). Grinding reduced spectral variability, particularly for the NIR-R210, while the NeoSpectra Scanner’s rotating sampling configuration may have contributed to more representative measurements of intact leaves. These findings highlight the importance of representative sampling for heterogeneous tea materials.