A strategy to significantly improve the classification accuracy of LIBS data: application for the determination of heavy metals in <i>Tegillarca granosa</i>

Yangli XU (徐仰丽); Liuwei MENG (孟留伟); Xiaojing CHEN (陈孝敬); Xi CHEN (陈熙); Laijin SU (苏来金); Leiming YUAN (袁雷明); Wen SHI (石文); Guangzao HUANG (黄光造)

Plasma Science and Technology > 2021 > 23(8): 85503-085503.

Yangli XU (徐仰丽), Liuwei MENG (孟留伟), Xiaojing CHEN (陈孝敬), Xi CHEN (陈熙), Laijin SU (苏来金), Leiming YUAN (袁雷明), Wen SHI (石文), Guangzao HUANG (黄光造). A strategy to significantly improve the classification accuracy of LIBS data: application for the determination of heavy metals in Tegillarca granosa[J]. Plasma Science and Technology, 2021, 23(8): 85503-085503.

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A strategy to significantly improve the classification accuracy of LIBS data: application for the determination of heavy metals in Tegillarca granosa

Funds: This work was supported by the Natural Science Foundation of Zhejiang Province (No. LY21C200001); National Natural Science Foundation of China (No. 31571920); Wenzhou Science and Technology Project (No. N20160004) and Wenzhou Basic Public Welfare Project (No. N20190017).

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Abstract

Abstract

Tegillarca granosa, as a popular seafood among consumers, is easily susceptible to pollution from heavy metals. Thus, it is essential to develop a rapid detection method for Tegillarca granosa. For this issue, five categories of Tegillarca granosa samples consisting of a healthy group; Zn, Pb, and Cd polluted groups; and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry (LIBS) and the newly proposed linear regression classification-sum of rank difference (LRC-SRD) algorithm. As the comparison models, least regression classification (LRC), support vector machine (SVM), and k-nearest neighbor (KNN) and linear discriminant analysis were also utilized. Satisfactory accuracy (0.93) was obtained by LRC-SRD model and which performs better than other models. This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.

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