高光谱图像技术检测枇杷叶片三萜酸含量及叶面分布

枇杷叶富含三萜酸类化合物,具有较高的药用价值。本研究首先建立枇杷叶高光谱信号与三萜酸含量的对应关系,然后利用高光谱图像包含每个像素点的光谱信号这一独特优势,检测枇杷叶片的三萜酸分布。通过联合区间偏最小二乘法(si PLS)建立三萜酸含量分析模型,结果表明,采用si PLS将全光谱均匀划分11个子区间,选择1、5、6、7联合,主因子数为4 h,建立的si PLS谱区筛选模型预测效果最佳,其交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为3.392 mg/g和3.731 mg/g,校正集和预测集相关系数分别为0.8449和0.8223。根据si PLS模型计算叶片上所有像素点处的三萜酸含量,并通过伪彩色方法描述叶片中三萜酸含量的分布。研究结果表明,利用高光谱图像技术分析枇杷叶片三萜酸含量及叶面分布是可行的。

Measurement of Triterpenic Acid Content and Its Distribution in Loquat Leaves Based on the Hyperspectral Imaging Technique

Triterpenic acids are abundant in Loquat leaves, which present a relatively high medicinal value. The relative relationship between hyperspectral signal and triterpenic acid content was established and the distribution of triterpenic acids in loquat leaves was measured using hyperspectral images, as they contain the spectral signal for each pixel. Synergy interval partial least squares (siPLS) was used to establish an analytical model of triterpenic acid content in loquat leaves. The results showed that the optimal model was obtained by the siPLS spectral region screening method, which separated the whole spectrum into 11 intervals and combined four intervals, including intervals 1, 5, 6, and 7, and had a factor number of 4. The model achieved a correlation coefficient of 0.8449 and root mean square error of cross validation (RMSECV) of 3.392 mg/g in the calibration set and a correlation coefficient of 0.8223 and root mean square error of prediction (RMSEP) of 3.73 mg/g in the prediction set. Every pixel in the hyperspectral image was computed and the triterpenic acid content of each pixel was obtained according to the siPLS model, followed by the creation of a pseudo-color map to describe the distribution of triterpenic acids in loquat leaves. Overall, the results sufficiently demonstrate that it is feasible to employ hyperspectral imaging technology to measure the content of triterpenic acid and its distribution in loquat leaves.