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Discriminate copper contamination of fresh tobacco leaf based on laser induced breakdown spectroscopy

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2016 ASABE Annual International Meeting  162460884.(doi:10.13031/aim.20162460884)
Authors:   Kunlin Song, Fei Liu, Wenwen Kong, Jiyu Peng, Lanhan Ye, Yong He
Keywords:   Tobacco, copper, heavy metal, laser-induced breakdown spectroscopy, chemometrics.

Abstract. Copper is a essential trace element for tobacco. But excessive copper content damages the quality of cigarette and harms the health of smokers. In order to discriminate copper contamination of tobacco plants, a method for identifying fresh tobacco leaf under copper contamination stress was developed by laser-induced breakdown spectroscopy (LIBS) combining with chemometrics methods. 50 tobacco plants were averagely divided into 5 groups. The plants belonging to group 2, 3, 4, 5 were supplied with nutrient solution and copper sulfate solution at different copper ion concentrations of 25 mg L-1, 50 mg L-1, 75 mg L-1, 100 mg L-1, respectively. As reference, the 10 plants of group 1 were only supplied with nutrient solution. After 45 days, 3 leaves from each tabacco plant were selected for LIBS measurements. Principle component analysis (PCA) was used to determine important variables. 18 wavelength variables corresponding to C, N, O, S, Fe, Mg, Ca were seclected. The models for discriminating fresh tobacco leaves under copper contamination stress were built by linear discriminant analysis (LDA) and support vector machine (SVM). The models on the selected wavelength variables performed well. The classification accuracies of LDA model and SVM model were 72% and 82%, respectively. The result indicated the feasibility of copper contamination determination of fresh tobacco leaves using LIBS. This research introduced a new fast heavy metal contamination detection method for fresh plant. It is potential for future portable and online detection instruments or sensors for plant growing monitoring.

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