鸡蛋新鲜度、pH值及黏度的高光谱检测模型

利用高光谱成像仪采集鸡蛋的高光谱透射图像，并利用游标卡尺、pH计、黏度计测定鸡蛋的新鲜度、酸碱度与黏度，用竞争性自适应重加权（competitive adaptive reweighed sampling，CARS）算法与连续投影算法（successive projections algorithm，SPA）选取特征波长分别建立鸡蛋品质与其高光谱特征的简单多元线性回归（multiple linear regression，MLR）模型，并在CARS提取的特征波长基础上用SPA进行了二次波段提取，建立相应的MLR模型，对比一次波长提取与二次波长提取选择的特征波长建立的模型性能。结果表明，CARS与SPA建立的MLR模型的验证集相关系数均在0.9以上。二次特征波段建立MLR模型的验证集相关系数比一次特征波段提取建立的MLR相关系数高，且均方根误差（root mean square error，RMSE）均有所减小，选取的特征波段比单独使用CARS或SPA选取的波段要少。建立的鸡蛋新鲜度、酸碱度（pH值）、黏度MLR模型的相关系数R分别为0.94、0.95、0.95，RMSE分别为6.36、0.17、149。即CARS、SPA提取特征波长可以优化模型，二次特征波段提取能更进一步的优化模型，提高了模型的稳定性，该模型能完好无损预测鸡蛋品质。

Predictive Models for the Detection of Egg Freshness,Acidity and Viscosity Using Hyper-Spectral Imaging

In this study, hyper-spectral data of eggs were collected using a hyper-spectral imager, and their freshness, pH and viscosity were also measured with a vernier caliper, a pH meter and a viscometer, respectively. Characteristic wavelengths were selected using competitive adaptive reweighed sampling (CARS) and successive projections algorithm (SPA), respectively, for constructing a simple multiple linear regression model (MLR) between egg quality and hyper-spectral features. Moreover, another MLR model was also established based on secondary SPA extraction of the characteristic wavelengths selected by CARS. The performances of the three models developed were compared. The results showed that the validation set correlation coefficients of both the CARS model and SPA model were above 0.9, which were lower than that of the CARS-SPA model with a lower root mean square error (RMSE) and fewer characteristic wavelength bands. The correlation coefficients of the MLR model for egg freshness, pH and viscosity were 0.94, 0.95, and 0.95 respectively, and the RMSE were 6.36, 0.17, and 149, respectively. This study indicates that optimized MLP model could be obtained using CARS or SPA to extract characteristic wavelengths and be further optimized by their combined use for non-destructive prediction of egg quality with improved stability.