Time series hyperspectral chemical imaging (HCI) for investigation of spectral variations associated with water and plasticizers in casein based biopolymers

Abstract

This present work aims to provide new insights into the spectral variations associated with water and plasticizers in the protein matrix of casein based biopolymers by conducting time series hyperspectral chemical imaging (HCI). Near infrared (NIR) hyperspectral images (880–1720 nm) of sodium caseinate film were acquired at different periods during film forming process. Second derivative spectra revealed a red shift (from lower to higher wavelength) in the region of 1410–1492 nm during drying, indicating that hydrogen bond distribution converted from free toward associated hydroxyl groups. The introduction of plasticizers resulted in a strong hydrogen bonding matrix in the caseinate polymeric film as evidenced by analysis of second derivative spectra and curve fitting results. Moisture content (MC) of film samples were predicted with coefficient of determination in prediction (RPred2) of 0.96 and 0.97, and root mean square error of prediction (RMSEP) of 0.067 and 0.056 by conducting partial least squares (PLS) and support vector machine (SVM), respectively. SVM model was further used to visualize water distribution within the film samples over drying time and the time-evolution of water migration into a dried film. A novel time series spectral angle mapper (SAM) was also proposed to elucidate water penetration pattern in film material. Time series principal components analysis (PCA) and K-means analysis exhibited temporal variation of film samples during drying steps of film formation. The results clearly demonstrated the occurrence of strong hydrogen-bonded aggregates in the film samples, which was more likely due to the congregation of redundant plasticizers. The overall results confirmed that time series NIR HCI is a powerful and versatile tool to investigate water and plasticizer interactions in the protein-based polymeric film.