基于近红外光谱的烤烟油分识别研究  

Research on oil levels identification of flue-cured tobacco based on near infrared spectroscopy

作　　者：付光明 高子婷 杨建新 李怀奇 罗菲 梁一凡 严定伟 韦凤杰[4] 常剑波[2] 姬小明[1] FU Guangming;GAO Ziting;YANG Jianxin;LI Huaiqi;LUO Fei;LIANG Yifan;YAN Dingwei;WEI Fengjie;CHANG Jianbo;JI Xiaoming(College of Tobacco,Henan Agricultural University,Zhengzhou 450046,China;Sanmenxia Branch,Henan Tobacco Company,Sanmenxia 472000,China;Technology Center,China Tobacco Henan Industrial Co.,Ltd.,Zhengzhou 450000,China;Henan Provincial Tobacco Company,Zhengzhou 450018,China)

机构地区：[1]河南农业大学烟草学院,河南郑州450046 [2]河南省烟草公司三门峡市公司,河南三门峡472000 [3]河南中烟工业有限责任公司技术中心,河南郑州450000 [4]河南省烟草公司,河南郑州450018

出　　处：《河南农业大学学报》2024年第4期583-591,共9页Journal of Henan Agricultural University

基　　金：国家自然科学基金项目(32300342);河南省烟草公司三门峡市公司技术创新项目(2022411200200004x)。

摘　　要：【目的】对烤烟油分等级进行科学预测,实现不同油分档次烤烟的快速光谱鉴别。【方法】对代表性植烟县的299份全叶位覆盖的不同油分档次云烟87烟叶样本进行近红外光谱采集,利用一阶导数(D1)、归一化(NOR)、小波变换(WAVE)、标准正态化(SNV)和多元散射校正(MSC)共5种方法对光谱数据预处理后,考察了线性的偏最小二乘判别分析(PLS-DA)和非线性的最小二乘支持向量机(LS-SVM)判别模型的判别效果。【结果】对近红外原始光谱数据进行主成分降维后,所构建的PLS-DA油分档次分类模型训练集的准确率可达100.0%,但测试集仅有79.8%,经过D1、NOR、SNV和MSC预处理后,模型的测试集准确率分别提高到了85.9%、90.0%、83.8%和83.8%;基于对近红外原始光谱数据直接构建的LS-SVM油分档次分类模型的训练集准确率也达100.0%,测试集达到92.9%,经过NOR、WAVE、SNV和MSC预处理后测试集的准确率均提高到了95.0%以上,以MSC预处理的99.0%的准确率最高。【结论】多元散射校正预处理结合LS-SVM法构建的油分档次判别模型效果最好,提高了烤烟油分判定效率。【Objective】In order to scientifically predict the flue-cured tobacco leaf oil levels and achieve rapid spectral identification of flue-cured tobacco with different oil levels.【Method】A total of 299 samples of Yunyan 87 tobacco leaves with different oil levels and full leaf position from representative tobacco planting counties were collected by near-infrared spectra.Five methods,including first derivative(D1),normalization(NOR),wavelet transform(WAVE),standard normalization(SNV),and multivariate scattering correction(MSC),were used to preprocess the spectral data.We investigated the discriminative performance of two discriminant models,linear partial least squares discriminant analysis(PLS-DA)and nonlinear least squares support vector machine(LS-SVM).【Result】The accuracy of the PLS-DA oil level classification model training set constructed based on principal component analysis of near-infrared raw spectral data could reach 100.0%,but the test set was only 79.8%.After D1,NOR,SNV,and MSC preprocessing,the accuracy of the model’s test set had been improved to 85.9%,90.0%,83.8%,and 83.8%,respectively.The training set accuracy of the LS-SVM oil classification model based on direct construction of near-infrared raw spectral data also reached 100.0%,and the test set reached 92.9%.After NOR,WAVE,SNV,and MSC preprocessing,the accuracy of the test set was improved to over 95.0%,with the highest accuracy of 99.0%achieved by MSC preprocessing.【Conclusion】The oil levels discrimination model constructed by combining multiple scattering correction preprocessing with LS-SVM method has the best effect,improving the efficiency of flue-cured tobacco leaf oil levels determination.

关 键 词：烤烟 油分 近红外光谱 判别模型 最小二乘支持向量机 

分 类 号：S572[农业科学—烟草工业] TS411[农业科学—作物学]