基于梨枣轻微损伤的可见/近红外光谱判别研究  

Discrimination of Pear Jujubes Based on Visible/near-infrared Spectroscopy

作　　者：薛建新[1] 张淑娟[1] 赵聪慧[1] 

机构地区：[1]山西农业大学工学院,山西太谷030801

出　　处：《农机化研究》2015年第5期212-215,共4页Journal of Agricultural Mechanization Research

基　　金：高等学校博士学科点专项科研基金项目(20101403110003);山西省自然科学基金项目(2012011030-3);国家自然科学基金项目(31271973);山西省研究生优秀创新项目(20133055)

摘　　要：利用可见／近红外光谱技术对梨枣轻微损伤的分类判别建模方法进行研究。分别采用 PLS－LDA （线性）和LS－SVM （非线性）建立判别模型，分析比较不同预处理方式和建模波段对模型精度的影响。结果表明：经9点平滑预处理后的短波近红外（780～1100nm）PLS－LDA模型判别效果最佳，校正集和预测集的正确识别率分别达到97．8％和96．7％。Classification discrimination models of subtle bruise “Lizao” jujubes were studied by using visible/near-infra-red spectroscopy .The least squares-support vector machine （ LS-SVM） and partial least squares-line discriminant anal-ysis（ PLS-LDA） were applied to develop classification models , which based on different spectra pretreatments and differ-ent modeling wavelength bands .The results indicated that the best discrimination model was PLS -LDA model using Savitzky-Golay of 9 in the range of 780~1100nm, for the calibration and prediction set of PLS-LDA model, the dis-crimination rate was 97.8% and 96.7%, respectively.

关 键 词：可见/近红外 梨枣 轻微损伤 光谱 

分 类 号：S123[农业科学—农业基础科学] S665