摘要
为考察支持向量机回归(SVR)在烟草近红外光谱(NIRS)分析中应用的可行性,采用偏最小二乘回归(PLS)、多元线性回归(MLR)、误差反向传播人工神经网络(BP-ANN)和SVR对187份烟草样品的NIR漫反射光谱及其淀粉含量的化学测定数据进行处理,建立了烟草中淀粉含量NIRS定标模型,并采用留一法交叉验证(LOOCV)和独立样本集对模型进行了内部和外部验证。结果表明,SVR模型的预测能力比BP-ANN、PLS和MLR模型略好。因此,可将SVR引入到烟草淀粉含量的NIR分析中。
In order to test the applicability of support vector regression （SVR） in near-infrared （NIR） spectral analysis of tobacco, NIR calibration model for starch in tobacco were developed by processing NIR spectra and chemically determined starch content data of 187 tobacco samples with SVR, partial least square regression （PLS） , muhiplicative linear regression （MLR） and error back propagation artificial neural network （BP-ANN）. The obtained model was tested through internal validation and external validation with leave-one- out cross valida model was sligh applied to NIR tion （LOOCV） and independent sample set. The results showed that the accuracy of SVR tly higher than that of BP-ANN, PLS and MLR models, it implied that SVR algorithm could be analysis of starch in tobacco.
出处
《烟草科技》
EI
CAS
北大核心
2009年第10期41-44,49,共5页
Tobacco Science & Technology
关键词
烟草
近红外光谱
支持向量回归
机器学习
Tobacco
Near-infrared spectroscopy
Support vector regression
Machine learning
