Hyperspectral Imaging for Prediction and Visualization of Water Content and Springiness as Indicators of the Gel Quality of Preserved Eggs

In this study, hyperspectral imaging was used to visualize the water content and springiness of preserved egg gels and to predict different quality grades. First, the hyperspectral information of qualified and high quality preserved eggs was collected, and their water content and springiness were measured. Then, the original spectral data were transformed by Savitzky-Golay (S-G), first derivative (FD) or Savitzky-Golay and first derivative (S-G-FD) to analyze their correlation with water content and springiness values. We identified and excluded outliers by Monte Carlo-partial least squares (MCPLS), and partitioned the sample sets by sample set partitioning based on joint X-Y distance (SPXY). The characteristic wavelengths were selected using successive projection algorithm (SPA) and the uninformative variable elimination (UVE) method, and a multiple stepwise regression model (MSR) was established to predict the water content and springiness of preserved eggs. The results showed that UVE-MSR was the optimal model for predicting water content. Its determination coefficient and root-mean-square error (RMSE) were 0.882 and 0.583, respectively, and its relative percent deviation (RPD) was 2.1. The optimal model for predicting springiness was SPA-MSR, whose determination coefficient and RMSE were 0.903 and 0.348, respectively, and whose RPD was 2.2. Then, the models were used to calculate the water content and springiness for each pixel in the hyperspectral image, and a visual distribution map was generated for the visual detection of the water content and springiness of preserved eggs. Finally, the competitive adaptive weight sampling method was used to select the characteristic wavelengths, and a back propagation (BP) neural network model was established for quality prediction. The total recognition accuracy of the model was 98.3%.