A Controlled Proof-of-Concept Study for Quantitative Estimation of Syrup Addition in Honey Using RGB Histogram Descriptors and Explainable Machine Learning

This controlled proof-of-concept study evaluated whether interpretable RGB histogram descriptors extracted from smartphone images can be used to estimate nominal syrup addition within a single experimentally prepared honey-adulterant series. Eleven concentration-specific physical mixtures containing 0–50% syrup in 5% increments were prepared, and each mixture was represented by ten technical replicate images acquired under fixed white-light and camera settings while the sample vessel was rotated. Whole-image descriptors and a secondary patch-based representation were analyzed using partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost). Nested leave-one-concentration-out validation kept all technical replicate images originating from each concentration-specific preparation within the same fold, thereby preventing replicate leakage between training and test data. Performance metrics were calculated from the eleven outer concentration-level predictions. The primary analysis allowed fold-specific selection from the complete pool of 102 RGB descriptors; PLSR performed best (R2nested-LOCO= 0.980, RMSEnested-LOCO = 2.22 percentage points, and MAEnested-LOCO = 1.96 percentage points), followed by SVR (RMSEnested-LOCO = 2.74 percentage points). Secondary restricted-feature analyses separately evaluated the G-channel descriptor family, the three mean RGB intensities, and G_mean alone. A performance-weighted cross-model SHAP analysis showed that location and percentile descriptors accounted for 67.7% of the total consensus importance score, with G_p95 ranked first. The G-channel descriptor family consistently outperformed the three RGB means, while G_mean provided a strong but less informative reference. Whole-image descriptors outperformed patch-based representation for four of five algorithms in the matched comparison. These results demonstrate the feasibility of quantitative syrup-addition estimation within the investigated controlled series rather than general honey-adulteration detection. Future studies should include independently prepared samples, broader honey–adulterant combinations, and evaluation of transferability across imaging devices and acquisition sessions.