摘要
应用近红外光谱技术实现除草剂胁迫下油菜叶片中脯氨酸含量的检测。对248个经过除草剂丙酯草醚处理后的油菜叶片,经过烘干、磨碎后进行光谱扫描。经过Savitzky-Golay平滑、变量标准化(SNV)、二阶求导预处理后,应用偏最小二乘法(PLS)建立脯氨酸含量的预测模型,同时提取有效特征变量作为神经网络(BPNN)和最小二乘-支持向量机(LS-SVM)的输入值,并建立相应的模型。用186个样本建模,62个样本预测。结果表明,最小二乘-支持向量机能够获得最优的预测效果,预测的相关系数(r)、预测标准差(RMSEP)和偏差分别为0.995,0.041和0.000。说明应用近红外光谱技术结合最小二乘-支持向量机能够定量获得油菜叶片中脯氨酸的含量。
Near infrared（NIR） spectroscopy was applied for the fast determination of proline in oilseed rape leavers. The oilseed rape leaves were treated by herbicide,248 samples were collected for NIR spectral scanning within the wavelength region of 1100～2500 nm.Smoothing way of Savitzky-Golay with 7 segments,standard normal variate （SNV） and second derivative were used as preprocessing methods of spectral data before the calibration stage.Partial least squares（PLS） analysis was applied as calibration method as well as a way to extract the new eigenvectors which could be used to represent the most useful information of original spectra and compress the spectral dimensionality. The selected new eigenvectors were used as the input data matrix of back propagation neural network（BPNN） and least squares-support vector machine（LS-SVM） to develop the BPNN and LS-SVM models.The calibration set was composed of 186 samples,whereas 62 samples in the validation set.The results indicated that LS-SVM model achieved the best prediction performance,and LS-SVM model outperformed PLS and BPNN models.The correlation coefficients（r）,root mean square error of prediction（RMSEP） and bias by LS-SVM model were 0.995,0.041 and 0.000,respectively.The overall results demonstrated that NIR spectroscopy combined with LS-SVM model could be successfully applied for the determination of proline in oilseed rape leaves treated by herbicide.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2010年第4期1192-1196,共5页
Acta Optica Sinica
基金
国家863计划(2007AA10Z210
2006AA10Z234)
国家自然科学基金(30671213)
浙江省自然科学基金重点项目(Z3090295)
中央高校基本科研究业务费专项资金
浙江省研究生创新科研项目(YK2008014)资助课题
关键词
光谱学
近红外光谱
偏最小二乘法
反向传播神经网络
最小二乘-支持向量机
spectroscopy
near infrared spectroscopy
partial least squares analysis
back propagation neural network
least squares-support vector machine
