Machine Learning-Based Terahertz Spectroscopy for Starch Concentration Prediction in Biofilms

Food preservation and safety require advanced detection methods to ensure transparency in supply chains. Terahertz (THz) spectroscopy has emerged as a powerful, noninvasive tool for material characterization. This study explores the integration of THz spectroscopy and machine learning for accurately quantifying maize starch adulteration in bioplastics derived from potato starch. Bioplastic samples with varying concentrations of maize starch were prepared, molded into three different thicknesses, and subjected to a two-stage drying process, resulting in 81 samples (27 treatments with three replicates each). The spectral profiles at THz (0.5 to 2 THz) were recorded and analyzed using three regression models: support vector regression, partial least squares regression, and multiple linear regression. The models were evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), and the Residual Predictive Deviation (RPD). The results showed R² values ranging from 0.7283 to 0.9495, RMSE between 0.0594 and 0.1393, and RPD values from 1.8753 to 4.4479, demonstrating strong predictive performance. These findings highlight the potential of THz spectroscopy and machine learning in the noninvasive detection of starch adulterants in bioplastics, paving the way for future research to enhance model robustness and applicability.