Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging

Sun Jun; Zhou Xin; Wu Xiaohong; Lu Bing; Dai Chunxia; Shen Jifeng

doi:10.1016/j.saa.2018.12.051

Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging

Spectrochim Acta A Mol Biomol Spectrosc. 2019 Apr 5:212:215-221. doi: 10.1016/j.saa.2018.12.051. Epub 2018 Dec 29.

Authors

Sun Jun¹, Zhou Xin², Wu Xiaohong¹, Lu Bing¹, Dai Chunxia¹, Shen Jifeng¹

Affiliations

¹ School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
² School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China. Electronic address: 862218958@qq.com.

PMID: 30641361
DOI: 10.1016/j.saa.2018.12.051

Abstract

The reliability and validity of Vis-NIR hyperspectral imaging were investigated for the determination of heavy metal content in tomato leaves under different cadmium stress. Besides, a method involving wavelet transform and least square support vector machine regression (WT-LSSVR) is proposed to select the optimal wavelength and establish the detection model. The Vis-NIR hyperspectral images of 405 tomato leaf samples were obtained and the whole region of tomato leaf sample spectral data was collected and preprocessed. In addition, WT-LSSVR is used to select optimal wavelength and establish the detection model using db4 and db6 as wavelet basis function, respectively. Furthermore, the best prediction performances for detecting cadmium (Cd) content in tomato leaves was obtained by second derivative (2nd Der) pre-processing method, with R_c² of 0.9437, RMSEC of 0.0988 mg/kg, R_p² of 0.8937, RMSEP of 0.2331 mg/kg, R_cv² of 0.9357, RMSECV of 0.1455 mg/kg, RPD of 3.081 and bias of 0.00863 using db6 (daubechies 6) as wavelet basis function with wavelet fourth layer decomposition. The results of this study indicated that WT-LSSVR can effectively select the optimal wavelength and Vis-NIR hyperspectral imaging has great potential for detecting heavy metal content in tomato leaves under different cadmium stresses.

Keywords: Cadmium; Heavy metal; Tomato leaf; Visible/near infrared hyperspectral imaging; WT-LSSVR.

MeSH terms

Cadmium / analysis*
Least-Squares Analysis
Plant Leaves / chemistry*
Principal Component Analysis
Reproducibility of Results
Solanum lycopersicum / chemistry*
Spectroscopy, Near-Infrared / methods*
Support Vector Machine*
Wavelet Analysis*

Substances

Cadmium