Determination of total protein and sugar content in soy-based beverages using variable selection methods applied to ATR-FTIR spectroscopy

Abstract

This study explores the potential of using ATR-FTIR coupled with partial least squares (PLS) and random forest (RF) for predicting total protein (TP), total sugar (TS), reducing sugars (RS), and non-reducing sugars (NRS) in soy-based beverages (SBBs). Employing variable selection techniques such as interval PLS (iPLS), synergy interval PLS (siPLS), uninformative variable elimination (UVE), ordered predictors selection (OPS), bat algorithm (BA), genetic algorithm (GA), and particle swarm optimization (PSO). The OPS-PLS emerges as the optimal model for TS prediction, yielding an RMSEP of 0.114 wt% and R2p of 0.934. GA-PLS excels in TP, RS, and NRS prediction, achieving RMSEP values of 0.024 wt%, 0.273 wt%, and 0.339 wt%, with corresponding R2p values of 0.880, 0.847, and 0.450. In RF modeling, GA-RF is identified as the superior model for all properties. These findings underscore the potential of regression models and ATR-FTIR as effective and environmentally friendly tools for analyzing SBB composition.