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标题：Quantitative analysis of cadmium in rice roots based on LIBS and chemometrics methods
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作者：Wei Wang ; Wenwen Kong ; Tingting Shen 等
期刊名称：Environmental Sciences Europe
印刷版ISSN：2190-4715
出版年度：2021
卷号：33
期号：1
页码：1
DOI：10.1186/s12302-021-00480-4
出版社：BioMed Central
摘要：Abstract Background Excessive cadmium can damage cell structure, inhibit enzyme activity, and affect metabolic process, thus, leading to decline of rice yield and quality. Root is an important organ of crops, the detection of cadmium in root is essential for limitation of cadmium in rice grains. Results In this study, laser-induced breakdown spectroscopy (LIBS) was applied for cadmium quantitative analysis. Pretreatment methods, including median absolute deviation, wavelet transform, area normalization, were used to improve spectral stability. Scanning electron microscope and energy-dispersive X-ray spectrometer (SEM/EDS) was first used to analyze ablation pit surface characteristics and the results showed significant positive correlation with spectral lines of Cd II 214.44, Cd II 226.50 and Cd I 228.80 nm. Univariable models of spectral lines showed that three Cd spectral lines have good prediction for cadmium. Fitting methods including linear, logarithmic, and polynomial were used to propose characteristic input variables, and univariable models based on variable of polynomial fitting of I 214.44 nm have achieved the best effect ( R p = 0.9821 and RMSEP = 31.1 mg/kg). Besides, partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were used for multivariate analysis. Compared with univariate analysis, ELM model based on the full spectrum ( R p = 0.9896 and RMSEP = 26.0 mg/kg) had more advantages for cadmium detection. Conclusion Compared with traditional methods (150 min), the quantitative detection method based on LIBS technology (less than 5 min) greatly reduces the detection time of heavy metals. The results showed that LIBS has proved to be a reliable method for quantitative detection of cadmium in rice roots. The research can provide theoretical support for timely detection of heavy metals in crop and food production.
其他摘要：Abstract Background Excessive cadmium can damage cell structure, inhibit enzyme activity, and affect metabolic process, thus, leading to decline of rice yield and quality. Root is an important organ of crops, the detection of cadmium in root is essential for limitation of cadmium in rice grains. Results In this study, laser-induced breakdown spectroscopy (LIBS) was applied for cadmium quantitative analysis. Pretreatment methods, including median absolute deviation, wavelet transform, area normalization, were used to improve spectral stability. Scanning electron microscope and energy-dispersive X-ray spectrometer (SEM/EDS) was first used to analyze ablation pit surface characteristics and the results showed significant positive correlation with spectral lines of Cd II 214.44, Cd II 226.50 and Cd I 228.80 nm. Univariable models of spectral lines showed that three Cd spectral lines have good prediction for cadmium. Fitting methods including linear, logarithmic, and polynomial were used to propose characteristic input variables, and univariable models based on variable of polynomial fitting of I 214.44 nm have achieved the best effect ( R p = 0.9821 and RMSEP = 31.1 mg/kg). Besides, partial least squares regression (PLSR), least squares support vector machine (LS-SVM) and extreme learning machine (ELM) were used for multivariate analysis. Compared with univariate analysis, ELM model based on the full spectrum ( R p = 0.9896 and RMSEP = 26.0 mg/kg) had more advantages for cadmium detection. Conclusion Compared with traditional methods (150 min), the quantitative detection method based on LIBS technology (less than 5 min) greatly reduces the detection time of heavy metals. The results showed that LIBS has proved to be a reliable method for quantitative detection of cadmium in rice roots. The research can provide theoretical support for timely detection of heavy metals in crop and food production.
其他关键词：Rice root, Cadmium, Laser-induced breakdown spectroscopy, Fitting methods, Extreme learning machine