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

作　　者：许文丽[1] 药林桃[2] 孙通[1] 胡田[1] 胡涛[1] 刘木华[1] 

机构地区：[1]江西农业大学生物光电技术及应用重点实验室,江西南昌330045 [2]江西省农业科学院农业工程研究所,江西南昌330200

出　　处：《食品工业科技》2014年第22期61-64,共4页Science and Technology of Food Industry

基　　金：国家自然科学基金项目(31271612);留学人员科技活动项目(2012);江西省教育厅科学研究基金(GJJ13254)

摘　　要：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.

关 键 词：可见/近红外光谱 苹果 CARS-SPA PLS 可溶性固形物 

分 类 号：TS255.1[轻工技术与工程—农产品加工及贮藏工程]