皮蛋凝胶品质含水率和弹性的高光谱预测及其可视化

采用高光谱成像技术对出缸期皮蛋凝胶品质的含水率和弹性进行可视化检测与不同品质预测.首先,采集合格、优质皮蛋的高光谱信息,对比测定其含水率和弹性,对皮蛋原始光谱数据进行卷积平滑、一阶导数、卷积平滑和一阶导数变换,分析不同预处理光谱数据与含水率和弹性数值的相关性;采用蒙特卡罗偏最小二乘法分析并剔除异常值,利用光谱-理化值共...

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%.