短波近红外光谱结合ν-SVM法快速无损鉴别淀粉种类  

Non-destructive determination of starch category by short-wave near-infrared spectroscopy combined with C-SVM

作　　者：邹婷婷[1] 窦英[2] 王莹[3] 宋焕禄[1] 庞小一[1] 陶菲菲[1] 张秋晨[1] 

机构地区：[1]北京工商大学食品学院,北京100048 [2]天津科技大学理学院化学系,天津300457 [3]吉林省产品质量监督检验院,吉林长春130022

出　　处：《食品与发酵工业》2013年第3期176-178,共3页Food and Fermentation Industries

摘　　要：选用不同厂家的红薯淀粉、马铃薯淀粉和玉米淀粉共112个样品,利用短波近红外光谱技术对淀粉种类进行鉴别。分别采用马氏距离判别法、C-支持向量机(C-SVM)、ν-支持向量机(ν-SVM)建立淀粉种类鉴别的短波近红外光谱模型;并对比多元散射矫正、平滑、一阶微分、二阶微分等多种预处理方法后的建模结果。结果表明:同时使用平滑、多元散射矫正、一阶微分3种预处理方法后,ν-SVM分类模型的效果最佳;训练集交叉验证正确率为100%,测试集正确率也达到100%。该模型快速准确无损的鉴别淀粉种类是可行的。A method of starch category analysis was developed using Short-wave Near-infrared （NIR） spectroscopy. All 112 samples were obtained from different manufacturers of sweet potato starch, potato starch and corn starch. The Short-wave NIR models were established using mahalanobis distance discriminant, C-Support vector machine （C-SVM） and v-Support vec- tor machine （v-SVM）. The various different pretreated methods （multiplicative scatter correction （MSC）, smooth, first-deriva- tion and second-derivation of spectra data were applied. The results indicated that v-SVM got the best results after MSC, smooth and first-derivation pretreatments. The correct ratio of the training set and the testing set is 100% and 100%. The re- suits showed the method for simultaneous, non-destructive analysis in determination of starch category is reliable.

关 键 词：短波近红外光谱技术 淀粉 马氏距离判别 ν-支持向量机(ν-SVM) 定性分析 

分 类 号：TS237[轻工技术与工程—粮食、油脂及植物蛋白工程] O657.33[轻工技术与工程—食品科学与工程]