Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible‐near infrared hyperspectral imaging
Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible‐near infrared hyperspectral imaging
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DOI：
10.1111/jfpe.13897
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发表时间：
2021-10
影响因子：
3
通讯作者：
Xin Zhou;Jun Sun;Yuechun Zhang;Yan Tian;Kunshan Yao;Min Xu
中科院分区：
文献类型：
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作者：
Xin Zhou;Jun Sun;Yuechun Zhang;Yan Tian;Kunshan Yao;Min Xu
为了有效检测Cd在生菜叶片中的分布，提出一种小波支持向量机回归（WSVR）建模方法，并将其用于生菜叶片中Cd含量的预测。此外，本文还通过扫描电镜和透射电镜观察了不同浓度Cd胁迫下生菜叶片的内外组织结构。利用高光谱成像系统采集了不同Cd浓度胁迫下的生菜叶片样品，采用不同的预处理算法结合降维算法对光谱特征波长进行选择，并基于光谱特征数据建立了支持向量回归（SVR）和支持向量回归（WSVR）模型。结果表明，随着Cd浓度的增加，生菜叶片的内外组织结构发生了显著变化。对生菜中Cd含量预测效果最好的WSVR模型参数为Rp 2 = 0.8843，RMSEP = 0.1292 mg/kg。最后，通过对高光谱图像各像元的光谱特征预测，将生菜叶片中Cd含量在预测图上可视化。实际应用通过无损检测实现作物叶片中重金属分布的可视化表达具有重要意义。为了论证维斯-NIR高光谱成像技术检测生菜叶片镉（Cd）含量的可行性，采用扫描电镜和透射电镜对不同浓度Cd胁迫下生菜叶片的内外组织结构进行了观察。提出了小波支持向量机回归建模方法，并将其应用于生菜叶片镉含量的预测。证明了维斯-近红外高光谱成像技术是实现作物叶片镉含量分布可视化的一种可行有效的方法。
In order to effectively detect the distribution of Cd in lettuce leaves, this article proposes a wavelet support vector machine regression (WSVR) modeling method and uses it in the prediction of cadmium (Cd) content in lettuce leaves. In addition, this article also observed the internal and external tissue structure of lettuce leaves under different concentrations of Cd stress by scanning electron microscopy and transmission electron microscopy. Moreover, the lettuce leaf samples under different Cd concentration stresses were acquired by the hyperspectral imaging system, different preprocessing algorithms combined with dimensionality reduction algorithms were used to select the spectral characteristic wavelengths, and SVR and WSVR models were established based on the spectral characteristic data. The results showed that with the increase of Cd concentration, the internal and external tissue structure of lettuce leaves changed significantly. The WSVR model parameters for the best prediction of Cd concentration in lettuce wereRp2of 0.8843, RMSEP of 0.1292 mg/kg. Finally, the Cd contents in lettuce leaves were visualized on the prediction maps by predicted spectral features on each hyperspectral image pixel.Practical ApplicationsIt is of great significance to realize the visual expression of the distribution of heavy metals in crop leaves through nondestructive testing. In order to demonstrate the feasibility of Vis‐NIR hyperspectral imaging technology for the detection of cadmium (Cd) content in lettuce leaves, scanning electron microscopy and transmission electron microscopy were used to observe the internal and external tissue structure of lettuce leaves under different concentrations of Cd. Wavelet support vector machine regression modeling method was proposed and used to predict the Cd content in lettuce leaves. It is proved that the Vis‐NIR hyperspectral imaging technology is a feasible and effective method to realize the visualization of Cd content distribution in crop leaves.