基于CARS算法的脐橙可溶性固形物近红外在线检测

采用可见/近红外光谱在线检测装置进行赣南脐橙可溶性固形物含量在线检测模型优化研究。样品以5个/s的速度运动，采集可见/近红外漫透射光谱。光谱经过预处理后，分别应用向后区间偏最小二乘法（BiPLS）、遗传算法（GA）和正自适应加权算法（CARS）筛选特征变量，并通过外部验证评价PLS模型预测能力。一阶微分处理后经CARS筛选特征变量建立的PLS模型预测结果最优，预测相关系数和预测均方根误差分别为0.94和0.42%。结果表明CARS算法可有效简化赣南脐橙可溶性固形物可见/近红外光谱在线检测模型并提高模型的预测精度。

On-line NIR Detection Model Optimization of Soluble Solids Content in Navel Orange Based on CARS

In order to improve the predictive precision for on-line determination of soluble solids content (SSC) of Gannan navel orange, the dynamic detecting system was applied to optimize online detection model by visible and near-infrared reflectance spectroscopy. The spectra were obtained at the constant velocity of 5 navel oranges per second. After employing various preprocessing methods, the sensitive spectral regions were analyzed by different variable selection methods, including backward interval partial least-squares (BiPLS), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). The predictive abilities of the models were evaluated by prediction set. The results indicated that the best model was obtained by CARS with the first derivative. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) was 0.94 and 0.42% for SSC respectively. The results showed that the proposed method of CARS could effectively simplify the online detection model of SSC of Gannan navel orange based on visible/near-infrared (Vis/NIR) diffuse transmittance spectroscopy, and enhance the predictive precision. The study can provide a reference for optimizing online detecting system of Gannan navel orange.