Abstract
The study investigated some new developed variable indices and chemometrics for the fast detection of cadmium (Cd) in tobacco root samples by laser-induced breakdown spectroscopy. The variables selection methods of interval partial least squares (iPLS), backward interval partial least squares (BiPLS), and successive projections algorithm (SPA) were used to locate the optimal Cd emission line for univariate analysis and to select the maximal relevant variables for multivariate analysis. iPLS and BiPLS located 10 Cd emission lines to establish univariate analysis models. Univariate analysis model based on Cd I (508.58 nm) performed best with the coefficient of determination of prediction (Rp2) of 0.9426 and root mean square error of prediction (RMSEP) of 1.060 mg g−1. We developed two new variable indices to remove negative effects for Cd content prediction, including Index1 = (I508.58 + I361.05)/2 × I466.23 and Index2 = I508.58/I466.23 based on Cd emission lines at 508.58, 361.05, and 466.23 nm. Univariate model based on Index2 obtained better result (Rp2 of 0.9502 and RMSEP of 0.988 mg g−1) than univariate analysis based on the best Cd emission line at 508.58 nm. PLS and support vector machines (SVM) were adopted and compared for multivariate analysis. The results of multivariate analysis outperformed univariate analysis and the best quantitative model was achieved by the iPLS-SVM model (Rc2 of 0.9820, RMSECV of 0.214 mg g−1, Rp2 of 0.9759, and RMSEP of 0.712 mg g−1) using the maximal relevant variables in the range of 474–526 nm. The results indicated that LIBS coupled with new developed variable index and chemometrics could provide a feasible, effective, and economical approach for fast detecting Cd in tobacco roots.
Highlights
Cadmium (Cd) is a nonessential and toxic heavy metal for plants, animals, and humans
The specific objectives of this research were (1) to choose suitable variables selection method from interval partial least squares (iPLS), backward interval partial least squares (BiPLS), iPLS-successive projections algorithm (SPA), and BiPLS-SPA, and to obtain outstanding Cd emission lines and the most relevant variables for reference Cd values in tobacco roots; (2) to execute univariate analysis by the selected Cd emission lines and put forward new index based on Cd emission lines to elevate univariate analysis capacity; (3) to compare the prediction performance of partial least squares (PLS) and support vector machines (SVM) multivariate analysis models based on the selected variables and to find out the optimal variables and the best quantitative models for fast and valid detection of Cd content in tobacco roots
Based on the Kurucz database and National Institute of Standards and Technology (NIST) Atomic Spectra Database (ASD), some same strong emission lines were observed in all five tobacco root groups such as C (247.86 nm), Mg (279.55, 279.80, 383.82, 516.73 nm), Ca (373.69, 397.37, 82, 422.67, 558.90, 849.80, 866.21, 643.91, 644.98 nm), Cu (324.75, 327.40 nm), CN (393.37, 396.85 nm), Na (589.00, 589.59 nm), and K (766.49, 769.90 nm)
Summary
Cadmium (Cd) is a nonessential and toxic heavy metal for plants, animals, and humans. The accumulation of Cd may lead to the decrease of yield, affect the quality of plants and endanger human health through the food chain. Root and root hair are almost interspersed in all spaces of soil-plant system and absorb Cd from the polluted soil solution in plant-environment system (Yang et al, 2013). There is no doubt that roots are the usual primary scene exposing to heavy metal in living environments. Rapid monitoring Cd accumulation in tobacco root is conducive for the detection and supervision of tobacco and land heavy metal pollution timely. There is no precedent for fast detection of Cd accumulation on related tobacco research yet
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