银杏叶总黄酮含量近红外光谱检测的特征谱区筛选

通过区间偏最小二乘法(iPLS)谱区筛选方法、反向区间偏最小二乘法(biPLS)谱区筛选方法和联合区间偏最小二乘法(siPLS)谱区筛选方法优化光谱特征区间,建立黄酮含量分析模型,并与波数范围为4 000～8 000 cm-1的全光谱偏最小二乘(PLS)模型进行比较。结果表明,采用siPLS谱区筛选方法将全光谱均匀划分21个子区间,选择两个子区间(7、12区间)联合时,建立的siPLS谱区筛选模型预测效果最佳,其交互验证均方根误差和预测均方根误差分别为2.950 0和3.000,校正集和预测集相关系数分别为0.938 4和0.943 7。因此采用siPLS谱区筛选方法可以有效选择光谱特征区域,提高建模预测能力,实现银杏叶总黄酮含量的快速检测。

Selection of Wavelength Regions to Determine Flavonoids Content in Ginkgo Leaves by FT-NIR Spectroscopy

In order to improve the detecting accuracy rating and stability of total flavonoids content in ginkgo leaves by near infrared spectroscopy technique, a precision model was established by selecting efficient spectral regions combined with different partial least squares (PLS) selecting wavelength regions methods. Three improved partial least squares (PLS) methods, including interval partial least squares (iPLS) selecting wavelength regions method, backward interval partial least squares (biPLS) selecting wavelength regions method and synergy interval partial least squares (siPLS) selecting wavelength regions method were used to find the most informative ranges and build models with better predictive flavonoids content in ginkgo leaves at first. And then the models were compared with PLS model which was developed on the whole wavelength range 4000~8000cm-1. Results showed that the models built by the three improved PLS methods had higher predictive ability than that of PLS method. The optimal model was the one that obtained by siPLS selecting wavelength regions method and it separated the whole spectra into 21 intervals and combined two intervals including interval 7 and interval 12, the RMSECV and RMSEP were 2.9500 and 3.000, calibration and the prediction correlation coefficient were 0.9384 and 0.9437. The conclusion is siPLS method can accurately and rapidly predict flavonoids content in ginkgo leaves.