Quantitative analysis of cadmium in rice roots based on LIBS and chemometrics methods

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 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.

Exploring the Potential of Portable Spectroscopic Techniques for the Biochemical Characterization of Roots in Shallow Landslides

“…LIBS has been applied for sorting wood waste [40] and, recently, for classifying wood species [41]. Some studies of the application of LIBS on plant roots can be found in the literature focused mainly on the characterization of elemental composition [42][43][44][45][46]. However, to the best of our knowledge, there are no studies in the literature trying to relate the biochemical structure and composition of roots with shallow landslide occurrence.…”

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

“…Decision tree (DT), support vector regression (SVR) and radial basis function neural network (RBFNN) have a strong ability to deal with nonlinear issues. These methods combined with spectral technology have achieved good results in detection of crop diseases [37], determination of crop origins [38,39] and prediction of physiological indexes [40,41]. It confirms the superiority of machine learning in mining the relationship between variables.…”

Laser-Induced Breakdown Spectroscopy (LIBS) for Trace Element Detection: A Review

“…In another paper, Guo et al [125] demonstrated that LIBS with PLSR modeling has better prediction performance and lower LoD (1079 ppm) than Raman and FT-IR with PLSR. In a similar study, Wang et al [127] applied LIBS and the chemometrics method to determine the cadmium content in rice roots. Tian et al [128] used the LIBS system with machine learning to evaluate the phosphorous concentration in seafood, and the LoD was estimated to be around 370 ppm for normalized data.…”

“…To enhance accuracy in the detection of heavy metals in mulberry leaves, Yang et al [150] proposed a novel analysis framework consisting of a self-organizing map (SOM), successive projection algorithm (SPA), and uninformative variable elimination (UVE). LIBS was combined with chemometrics for quick and accurate quantitative analysis of heavy metal Cd in rice stems [151] and roots [127]. Table 4 summarizes the different works related to detecting nutrients and toxic elements in plants by LIBS.…”