Quantitative analysis of reducing sugars in solid-state fermentation based on NIR combined with GA-AdaBoost

Abstract

This study focuses primarily on the real-time monitoring of reducing sugar levels throughout solid-state ethanol fermentation. A predictive model for accurate quantification of reducing sugar levels will be formulated through a comprehensive analysis of near-infrared spectra across different fermentation stages. Building a predictive model using Adaptive Boosting (AdaBoost), The spectral data was pre-processed using the Standard Normal Variate (SNV) method, followed by feature band selection using the Genetic Algorithm (GA), resulting in the identification of 181 characteristic wavelengths. The resulting GA-AdaBoost model has excellent performance metrics: (Formula presented.) of 0.989, RMSEC of 0.393, (Formula presented.) of 0.978, RMSEP of 0.640, RPD of 6.651 and RER of 0.142. Comparative evaluations against Competitive Adaptive Reweighted Sampling (CARS), Successive Projection Algorithm (SPA), Uninformative Variable Elimination (UVE) and Principal Component Analysis (PCA) consistently underscore the superior efficacy of the GA-AdaBoost model. These results robustly confirm its reliability in accurately quantifying reducing sugars in solid-state fermentation processes.