基于CARS-SPA的苹果可溶性固形物可见/近红外光谱在线检测

CARS-SPA baesd Visble/near Infraed spectroscopy on-line detection of apple soluble solids content

采用CARS(competitive adaptive reweighted sampling)联合连续投影算法(SPA)方法筛选苹果可见/近红外光谱的特征变量,继而联合多种不同建模方法建立苹果可溶性固形物(SSC)预测模型,并对预测模型进行对比研究。研究结果显示,采用CARS-SPA联合筛选出的31个变量,通过采用PLS建立苹果SSC的可见/近红外光谱在线检测模型性能最稳定,其变量数仅为原始光谱的1.69%,预测集的相关系数和均方根误差分别为0.936和0.351%。研究表明采用CARS-SPA能有效提取苹果SSC的光谱特征变量,能有效简化模型并提高模型精度。

CARS was combined with SPA to select the important variables from the visible/near infrared spectrum of apple, then a variety of different modeling methods was used to develop calibration models for SSC of apple, finally, some comparative studies was done among those models. The analysis results showed that 31 variables which selected by CARS-SPA and PLS could build the most stable on-line detection model of apple soluble solids solids（SSC）,in this prediction model,the number of variables was only 1.69 percent of the orginal spectrum, the correlation coefficient of prediction and root mean square error of prediction were 0.936,0.351% repectively. This study showed CARS-SPA could effectively extract important variables from spectrum of apple SSC,also it could simplify and improve the accuracy of prediction model effectively.