Next Article in Journal
A Dual-Polymer Fiber Fizeau Interferometer for Simultaneous Measurement of Relative Humidity and Temperature
Next Article in Special Issue
Surface Plasmon Resonance Sensing of Biorecognition Interactions within the Tumor Suppressor p53 Network
Previous Article in Journal
Combining a Disturbance Observer with Triple-Loop Control Based on MEMS Accelerometers for Line-of-Sight Stabilization
Previous Article in Special Issue
Interaction between Diethyldithiocarbamate and Cu(II) on Gold in Non-Cyanide Wastewater
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2655; https://doi.org/10.3390/s17112655

Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa

1
Department of Automation, Xiamen University, Xiamen 361005, China
2
Innovation Center for Cell Signaling Network, Xiamen University, Xiamen 361102, China
3
College of Physics and Electronic Engineering Information, Wenzhou University, Wenzhou 325035, China
4
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
5
LifeFoundry, Inc., Champaign, IL 61820, USA
6
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Authors to whom correspondence should be addressed.
Received: 14 September 2017 / Revised: 5 November 2017 / Accepted: 9 November 2017 / Published: 17 November 2017
(This article belongs to the Special Issue Surface Plasmon Resonance Sensing)
View Full-Text   |   Download PDF [1136 KB, uploaded 17 November 2017]   |  

Abstract

Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of Tegillarca granosa could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments. View Full-Text
Keywords: toxic heavy metal; laser-induced breakdown spectroscopy (LIBS); Tegillarca granosa; discrimination analysis; wavelet transform algorithm (WTA) toxic heavy metal; laser-induced breakdown spectroscopy (LIBS); Tegillarca granosa; discrimination analysis; wavelet transform algorithm (WTA)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ji, G.; Ye, P.; Shi, Y.; Yuan, L.; Chen, X.; Yuan, M.; Zhu, D.; Chen, X.; Hu, X.; Jiang, J. Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa. Sensors 2017, 17, 2655.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top