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 (Rp 2) 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 = (I 508.58 + I 361.05)/2 × I 466.23 and Index2 = I 508.58/I 466.23 based on Cd emission lines at 508.58, 361.05, and 466.23 nm. Univariate model based on Index2 obtained better result (Rp 2 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 (Rc 2 of 0.9820, RMSECV of 0.214 mg g-1, Rp 2 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.
Keywords: cadmium; interval partial least squares; laser-induced breakdown spectroscopy; multivariate analysis; tobacco root; variable index.