Physics-Guided Residual Learning with Conditional Modulation for Quality Monitoring of Kiwifruit Juice During Pasteurization

Accurate prediction of quality indicators during juice pasteurization is challenged by the intricate coupling between temperature-dependent reaction kinetics and the limited availability of labeled data under laboratory-controlled pasteurization conditions. Here, we present a physics-guided residual learning framework that fuses near-infrared spectroscopy with electronic nose signals for dynamic quality monitoring. A first-order volatilization–saturation kinetic equation is embedded as a physical prior (PIRL), anchoring the prediction to known degradation behavior while reserving the residual learner for data-driven compensation of unmodeled deviations. To further incorporate process control information, a Feature-wise Linear Modulation-inspired conditional modulation (FiLM–ICM) mechanism is deployed, enabling temperature and time to act not as passive covariates but as active modulators that adaptively rescale spectral and olfactory feature responses. Validated on a kiwifruit juice pasteurization dataset under leave-one-trajectory-out cross-validation, the PIRL–FiLM framework consistently outperforms conventional chemometric baselines, achieving R2 improvements up to 0.030 and RPD gains exceeding 0.230. This hybrid strategy demonstrates that physical knowledge and data-driven learning are not competing but complementary, offering a robust and interpretable paradigm for process analytical technology under small-sample constraints. This framework offers a practical solution for quality monitoring in scenarios where large-scale labeled datasets are unavailable, such as in laboratory-scale process development and small-batch production.