CatBoost-Optimized Hyperspectral Modeling for Accurate Prediction of Wood Dyeing Formulations

This study proposes a CatBoost-enhanced hyperspectral modeling approach for accurate prediction of wood dyeing formulations. Using Pinus sylvestris var. mongolica veneer as the substrate, 306 samples with gradient dye concentrations were prepared, and their reflectance spectra (400–700 nm) were acquired. After noise reduction and sensitive band selection (400–450 nm, 550–600 nm, and 600–650 nm), spectral descriptors were extracted as model inputs. The CatBoost algorithm, optimized via k-fold cross-validation and grid search, outperformed XGBoost, random forest, and SVR in prediction accuracy, achieving MSE = 0.00271 and MAE = 0.0349. Scanning electron microscopy (SEM) revealed the correlation between dye particle distribution and spectral response, validating the model’s physical basis. This approach enables intelligent dye formulation control in industrial wood processing, reducing color deviation (ΔE < 1.75) and dye waste by approximately 25%.