Bioplastic Thickness Estimation Using Terahertz Time-Domain Spectroscopy and Machine Learning

In the sustainable packaging industry, multiple parameters require regulation to achieve a high-quality final product that meets contemporary demands. In bioplastic manufacturing, the control of the film thickness is critical because it in-fluences the mechanical properties and other key characteristics. Terahertz time-domain spectroscopy (THz-TDS) has emerged as a promising technology for the non-invasive characterization of polymeric materials. The present study evaluates the integration of THz-TDS with chemometric techniques and machine learning models to predict the thickness of bioplastic samples fabricated from potato and maize starch. Three distinct thickness levels were produced by solution casting, and a spectral analysis was performed in the range of 0.5 to 1.2 THz. Four regression models were developed, including partial least squares regression, support vector regression, binary regression tree, and a feedforward neural network. The performance of the model was assessed using the coefficient of determination (R2), root mean square error (RMSE) and the ratio of performance to deviation (RPD). R2 values ranged from 0.8379 to 0.9757, the RMSE values ranged from 0.1259 to 0.3368, and the RPD values ranged from 2.4399 to 6.8106. These findings underscore the potential of THz-TDS and machine learning for non-invasive analysis of thin polymeric films and lay the groundwork for future research aimed at enhancing reliability and functionality.