Raman Spectroscopy of Protein–Polysaccharide Conjugates: A Comparative Study of Tree-Based Ensemble Models

Proteins with additives, especially in small quantities, are of great interest as a subject of study. Machine learning approaches implemented on Raman spectroscopy data could provide an insight into the chemical structures of such mixtures or conjugates. Although decision tree models could be powerful in solving either classification or regression tasks and could provide accessible predictions, they are prone to overfitting. Ensemble models that implement several decision trees could overcome the determined problem. Five different model types are discussed: RandomForest, GradientBoosting, AdaBoost, Voting, and Stacking. Raman spectroscopy data of whey protein isolates (5 wt.%) with different amounts of hyaluronic acid (0, 0.1, 0.25, and 0.5 wt.%) were used as datasets. In order to generalize the results of the study, WPI samples from three different manufacturers were used. Optimization established that ensembles of 200 decision trees with a maximum depth of four were optimal. The Stacking algorithm, which used RandomForest, GradientBoosting, and AdaBoost as base models with either LogisticRegressor (classification task) or RidgeCV (regression task), was found to be the most efficient in finding differences between the whey protein isolate and its conjugates with hyaluronic acid: specificity of 68.7% and sensitivity of 95.4% (classification task); R2 = 0.764 with mean absolute error of 0.068 (regression task). According to the feature importance plots, the Raman bands that were most influential in predicting the results were 1003 cm−1 (phenylalanine, ring breath), 1125 cm−1 (rocking of NH3+), 1206 cm−1 (C–C stretching), 1240 cm−1 (amide III (β-sheet), N–H in-plane bend, C–N stretch), and 1399 cm−1 (aspartic and glutamic acids, C=O stretch of COO–). The findings of this study may contribute to the development of novel methods for quality control and analysis of complex multicomponent systems in various industrial settings. In particular, the ensemble approach can be adapted for monitoring in food processing or as a screening tool in pharmaceutical formulation development.