实测甘草冠层光谱定量估算甘草酸和甘草苷含量

甘草酸(GA)和甘草苷(LQ)是甘草的两个主要的活性成分, 常用作评估甘草的质量主要指标。首次尝试应用实测甘草冠层的可见-短波红外(Vis-SWIR)高光谱数据定量估算甘草中的GA和LQ含量，利用高效液相色谱方法(HPLC)分别测定甘草中GA和LQ含量作为参考值，通过结合一阶导数预处理和运用Wilk’lambda 逐步回归法选择特征波长等光谱预处理方法，在选择9个最优波段基础上建立偏最小二乘(PLS)回归预测模型，甘草GA和LQ的回归精度R2分别为0.953和0.932，校正集的均方根误差(RMSEC)分别为0.31和0.22, 预测精度R2分别为0.875和0.883，验证集的均方根误差(RMSEP)分别为0.39和0.27。结果显示，用光谱预测模型获得甘草GA和LQ含量预测与HPLC方法获得的甘草GA和LQ含量实测之间具有较高的相关性，说明Vis-SWIR技术从遥感数据中来确定GA和LQ含量的可行性。为野外利用外机载和/或星载高光谱传感器对甘草质量遥感监测提供理论依据。

Quantitative Estimation of Glycyrrhizic Acid and Liquiritin Contents Using In-Situ Canopy Spectroscopy

The present study is the first to attempt to apply the in situ hyperspectral data of G. uralensis canopy in visible-shortwave infrared region (Vis-SWIR) to estimate quantification of GA and LQ contents of glycyrrhiza uralensis. After first derivative preprocessing and feature bands selection by Wilks’ lambda stepwise method , partial least squares(PLS) regression with high performance liquid chromatography (HPLC) as reference was constructed to predict the value of GA and LQ contents, respectively. With the nine selected bands and PLS regression model, GA regression accuracy of R2 is 0.953, root mean square errors of calibration set (RMSEC) is 0.31, prediction accuracy R2 is 0.875 and root mean square errors of validation set (RMSEP) is 0.39; LQ regression accuracy of R2 is 0.932, RMSEC is 0.22, prediction accuracy R2 is 0.883 and RMSEP is 0.27; The results showed that our methods provided acceptable results and implied the ability of determining GA and LQ contents from remotely sensed data. It is recommended that an advanced study be conducted in field condition using airborne and/or spaceborne hyperspectral sensors.