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Determination of Total Amino Acids in Oilseed Rape Leaves Using Near Infrared Spectroscopy and Chemometrics

机译:近红外光谱和化学计量学测定油菜叶片中的总氨基酸

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Near infrared spectroscopy combined with chemometrics was investigated to determine the total amino acids (TAA) in oilseed rape leaves. The samples in calibration, validation and prediction set were 80, 40 and 30, respectively. Different spectral preprocessing were compared, and three calibration methods were employed including partial least squares (PLS), multiple linear regression (MLR) and least squares-support vector machine (LS-SVM). The performance evaluation standards were determination coefficients (R2) and root mean square error (RMSE). Successive projections algorithm (SPA) was applied as variable selection method. The optimal model was achieved by SPA-LS-SVM using 13 relevant wavelengths with R2 = 0.9830 and RMSEP = 0.3964. The LS-SVM outperformed PLS and SPA-MLR models. The results indicated that near infrared spectroscopy was successfully applied for the determination of TAA in oilseed rape leaves. This detection method could be used for the on field monitoring of growing status and other physiological parameters of oilseed rape.
机译:研究了近红外光谱与化学计量学相结合的方法,以确定油菜籽叶中的总氨基酸(TAA)。校准,验证和预测集中的样本分别为80、40和30。比较了不同的光谱预处理,并采用了三种校准方法,包括偏最小二乘(PLS),多元线性回归(MLR)和最小二乘支持向量机(LS-SVM)。性能评价标准为测定系数(R 2 )和均方根误差(RMSE)。采用连续投影算法(SPA)作为变量选择方法。通过SPA-LS-SVM使用13个相关波长,R 2 = 0.9830和RMSEP = 0.3964来获得最佳模型。 LS-SVM的性能优于PLS和SPA-MLR模型。结果表明,近红外光谱法已成功地用于测定油菜油菜叶片中的TAA。该检测方法可用于油菜生长状况及其他生理参数的现场监测。

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