| 基于DPLS和LS—SVM的梨品种近红外光谱识别 |
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| 引用本文： | 刘雪梅,章海亮.基于DPLS和LS—SVM的梨品种近红外光谱识别[J].农业机械学报,2012,43(9):160-164. |
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| 作者姓名： | 刘雪梅 章海亮 |
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| 作者单位： | 华东交通大学土木建筑学院,南昌,330013 |
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| 基金项目： | 江西省科技支撑项目(2010BNB01200) |
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| 摘 要： | 为了实现不同品种梨的快速光谱鉴别,采用主成分分析法(PCA)对光谱数据进行聚类分析,得到3种不同品种梨的特征差异,主成分分析表明,以所有建模样本主成分PC1和PC2做出的得分图,对不同种类梨具有很好的聚类作用。利用主成分分析得到的载荷图可以得到对于梨品种敏感的特征波段,用特征波段图谱作为输入建立偏最小二乘判别(DPLS)模型和最小二乘支持向量机(LS-SVM)模型。3个品种梨各70个共210个分别建立偏最小二乘判别(DPLS)模型和最小二乘支持向量机(LS-SVM)模型。对未知的24个样本进行预测,LS-SVM模型品种识别准确率达到100%,DPLS模型的校正及验证结果与实际分类变量的相关系数均大于0.980,交叉验证均方根误差(RMSECV)和预测均方根误差(RMSEP)都小于0.100,品种识别率为100%。表明提出的方法具有很好的分类和鉴别作用,提供了梨的品种快速鉴别分析方法。
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| 关 键 词： | 梨 近红外光谱 品种识别 主成分分析 偏最小二乘判别 最小二乘支持向量机 |
Identification of Varieties of Pear Using Near Infrared Spectra Based on DPLS and LS-SVM Model |
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| Liu Xuemei and Zhang Hailiang.Identification of Varieties of Pear Using Near Infrared Spectra Based on DPLS and LS-SVM Model[J].Transactions of the Chinese Society of Agricultural Machinery,2012,43(9):160-164. |
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| Authors: | Liu Xuemei and Zhang Hailiang |
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| Institution: | East China Jiaotong University;East China Jiaotong University |
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| Abstract: | In order to realize the rapid identification of different varieties of pears,principal component analysis(PCA) on the spectral data clustering analysis was used on three different varieties of pears to find the characteristic differences.The principal component analysis showed that the main composition PC1 and PC2 for all the modeling samples score diagrams had very good clustering effect to the different types of pears.Load diagram that got by using principal component analysis can obtain the variety sensitive characteristic wavelengths from pears,and with the characteristic band spectrum as input to build partial least-squares discriminant(DPLS) and least squares support vector machine(LS-SVM) models.Seventy pears of three varieties with 210 in total were used to build DPLS and LS-SVM models respectively.The unknown 24 samples were predicted by the models,the recognition accuracy rate of the LS-SVM model reached to 100%.The calibration and verification results of the DPLS model and the actual classification variables of the correlation coefficient was greater than 0.980.Cross validation root mean square error(RMSECV) and root mean square error of prediction(RMSEP) were less than 0.100.The varieties recognition rate was 100%.The proposed rapid identification method has good classification effects. |
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| Keywords: | Pear Near infrared spectral Varieties identification Principal component analysis DPLS LS-SVM |
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