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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 I214.44 nm have achieved the best effect (Rp = 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 (Rp = 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.

The presence of cadmium in rice stems is a limiting factor that restricts its function as biomass. In order to prevent potential risks of heavy metals in rice straws, this study introduced a fast detection method of cadmium in rice stems based on laser induced breakdown spectroscopy (LIBS) and chemometrics. The wavelet transform (WT), area normalization and median absolute deviation (MAD) were used to preprocess raw spectra to improve spectral stability. Principal component analysis (PCA) was used for cluster analysis. The classification models were established to distinguish cadmium stress degree of stems, of which extreme learning machine (ELM) had the best effect, with 91.11% of calibration accuracy and 93.33% of prediction accuracy. In addition, multivariate models were established for quantitative detection of cadmium. It can be found that ELM model had the best prediction effects with prediction correlation coefficient of 0.995. The results show that LIBS provides an effective method for detection of cadmium in rice stems. The combination of LIBS technology and chemometrics can quickly detect the presence of cadmium in rice stems, and accurately realize qualitative and quantitative analysis of cadmium, which could be of great significance to promote the development of new energy industry.

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