基于可见/近红外反射光谱的梨树叶片钾含量的快速测定研究

利用可见/近红外反射光谱定量分析技术对梨树鲜叶钾素含量进行快速测定研究。对150个梨树叶片样本进行光谱扫描,其中120个做建模集,30个做验证集。通过对样品的可见/近红外光谱进行多种预处理,并建立钾素预测模型,探讨了可见/近红外光谱数据预处理对预测精度的影响。结果表明,通过原始光谱与S-G(3)平滑相结合的预处理方法,用17个主成分建立的偏最小二乘法模型最好,其交叉验证集和预测集模型的决定系数(R2)分别为0.722 7和0.679 1,交叉验证均方根误差(RMSECV)为1.171,预测的平均相对误差为6.81%,能高效、快速地预测梨树叶片钾素含量,为梨树钾素快速测定提供了新的手段。

Fast Determination of Potassium Content in Fresh Pear Leaves Using Visible/Near Infrared Spectroscopy Technique

The objective of this paper was to study the potential of visible/near infrared reflectance spectroscopy (Vis/NIR) for nondestructive determination of potassium (K) content in fresh pear leaves with an ASD FieldSpec 3 spectrometer. All the samples were divided randomly into two groups, one with 120 samples as the calibration set, and the other with 30 samples as the validation set. Different spectra wave bands, spectra preprocessing in different ways and different spectra styles were used in the prediction model of K content. The results showed that, after the preprocessing of original spectral with the whole wave band plus S-G (3) smoothing, the prediction model calibrated with 17 principal component factors had the best performance by the partial least squares (PLS) regression, in which the determination coefficient (R2) of calibration and cross-validation was 0.722 7 and 0.679 1, respectively, and the root mean square error of cross-validation (RMSECV) was 1.171. Test of the best PLS model with 30 samples in the validation set showed that the predicted average relative error was 6.81%. Therefore, it could be concluded that Vis/NIR has a huge potential for the determination of total potassium content in fresh pear leaves in a rapid and nondestructive way.