FTIR ATR Fingerprinting Coupled with Hybrid Physics-Informed Neural Networks for Olive Oil Quality Prediction

Introduction Balancing olive oil quality with resource efficiency remains challenging. This study developed a Deep Hybrid Physics-Informed Neural Network (HPINN) integrating FTIR-ATR spectra, agronomic inputs, and biochemical pathway constraints to improve prediction accuracy and interpretability. Methods A two-year (2023–2024) field experiment in Meknes, Morocco, used a 3 × 3 irrigation–fertilization factorial design with three biological replicates, yielding 54 oil samples and 324 FTIR-ATR spectra. A 1D-CNN extracted spectral features, while fully connected layers predicted Total Phenolic Content (TPC), Total Flavonoid Content (TFC), DPPH antioxidant capacity, and carotenoids. Phenylpropanoid pathway relationships were incorporated as loss constraints. SHAP and Monte Carlo dropout were used for interpretation and uncertainty quantification. Validation used Leave-One-Treatment-Out Cross-Validation (LOTO-CV) and a 2023-to-2024 temporal holdout. Results LOTO-CV produced a mean Spearman ρ 2 of 0.826 across traits, compared with 0.754 for XGBoost and 0.683 for PLSR. Physics-informed regularization reduced model entropy by 37.2% and improved interannual generalizability. Ablation confirmed contributions from both CNN spectral features and biochemical constraints. SHAP identified FTIR-ATR bands consistent with relevant biochemical variation. Discussion Biochemical constraints promoted metabolically plausible predictions while retaining predictive performance, supporting their value over purely data-driven models where mechanistic knowledge is available. Conclusion Pareto optimization identified 65% ETc irrigation × 72% NPK fertilization, achieving 94% of maximum composite quality at 22% lower resource cost. HPINN provides an interpretable framework for precision olive management, with potential application to other perennial crops.