Non-invasive characterization of color variation in plant-based meat burgers using portable hyperspectral imaging device and multivariate image analysis

Abstract

The excessive color variation in plant-based meat burgers (PB) during storage, arising from the interplay of multiple ingredients, suggests the acute need for real-time color monitoring techniques. This study reports the use of a smart-portable hyperspectral imaging device in the visible-near-infrared coupled with multivariate data and image analysis for the prediction of color in PB samples of different formulations during storage. Principal Components Analysis (PCA) and Partial Least Squares Regression (PLSR) were applied to explore the spectral features and predict the CIE - L*, a*, b*. PCA on spectral data showed successful spatial separation of PB samples based on color stability. The PLSR models in the full spectral range possessed good prediction accuracies for L* (R2P = 0.88, RMSEP = 0.94), a* (R2P = 0.98, RMSEP = 0.15), b* (R2P = 0.92, RMSEP = 0.31). Prediction capabilities of simplified PLSR models were improved for a* (R2P = 0.99, RMSEP = 0.09) with B-iPLS, b* (R2P = 0.95, RMSEP = 0.23) with B-iPLS. Pixel-by-pixel prediction maps unveiled the pattern of color deterioration concerning individual base ingredients in the PB samples. The results unravel the feasibility of developing a real-time multi-spectral technique for quantification and visualization of color evolution in plant-based meat analogs.