Prediction and visualisation of S-ovalbumin content in egg whites using hyperspectral images

Abstract

This study proposed a method using hyper-spectral imaging technology in determining eggs’ quality in term of freshness from a biochemical perspective by estimating the S-ovalbumin content. This method has the potential in assessing eggs’ quality rapidly and non-destructively. Hyper-spectral image of egg was captured using a hyper-spectral imaging system and regression model was built to estimate the S-ovalbumin content. The successive projections algorithm (SPA) was used to select significant wavebands followed by building a partial least squares regression (PLSR) model and a multiple linear regression (MLR) model. The MLR model could predict S-ovalbumin content better than PLSR model with a higher correlation coefficient (0.922) and lower root mean square error (0.086) of the calibration set, a higher correlation coefficient (0.911) and lower root mean square error (0.119) of the validation set, and a higher residual predictive deviation (2.348). The regression equation from the MLR model was used to compute each pixel of the image in the validation set and visualisation of S-ovalbumin content distribution in the egg was obtained using pseudo-color image. The findings implied that the proposed hyper-spectral imaging system with the regression model developed has the potential in determining and visualising the eggs’ quality.

Keywords

• chicken eggs
• hyper-spectral imaging
• s-ovalbumin content
• successive projections algorithm
• visualisation