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

Ji, Guoli; Ye, Pengchao; Shi, Yijiang; Yuan, Lei; Chen, Xiaojing; Yuan, Mingshun; Zhu, Dehua; Hu, Xinyu; Jiang, Jing

doi:10.3390/s17112655

Cited by 21 publications

(9 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These difficulties brought substantial challenges to the recognition algorithm in classification recognition. For specific data descriptions, please refer to Reference [23].…”

Section: Datasetsmentioning

confidence: 99%

A Sparse Classification Based on a Linear Regression Method for Spectral Recognition

Yuan

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This study introduces a spectral-recognition method based on sparse representation. The proposed method, the linear regression sparse classification (LRSC) algorithm, uses different classes of training samples to linearly represent the prediction samples and to further classify them according to residuals in a linear regression model. Two kinds of spectral data with completely different physical properties were used in this study. These included infrared spectral data and laser-induced breakdown spectral (LIBS) data for Tegillarca granosa samples polluted by heavy metals. LRSC algorithm was employed to recognize the two classes of data, and the results were compared with common spectral-recognition algorithms, such as partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), artificial neural network (ANN), random forest (RF), and support vector machine (SVM), in terms of recognition rate and parameter stability. The results show that LRSC algorithm is not only simple and convenient, but it also has a high recognition rate.

show abstract

“…These difficulties brought substantial challenges to the recognition algorithm in classification recognition. For specific data descriptions, please refer to Reference [23].…”

Section: Datasetsmentioning

confidence: 99%

A Sparse Classification Based on a Linear Regression Method for Spectral Recognition

Yuan

et al. 2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…), compared to EDS (or EDX) technique [23,24]. Currently, owing to the rapid development of this technique, the use of LIBS is widespread in the theoretical and experimental research of many fields, such as those of mineral products, archaeology, biomedicine and aerospace exploration [25][26][27][28][29]. In particular, LIBS is currently the only feasible technique in fields that require remote elemental analysis [26].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, owing to the rapid development of this technique, the use of LIBS is widespread in the theoretical and experimental research of many fields, such as those of mineral products, archaeology, biomedicine and aerospace exploration [25][26][27][28][29]. In particular, LIBS is currently the only feasible technique in fields that require remote elemental analysis [26]. We have [30,31] evaluated the feasibility of using LIBS to achieve rapid, accurate, online monitoring of the ageing performance of silicone rubber and to determine the components (C, O, Fe and Si) that are closely related to the ageing state of silicone rubber.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Pollution in High Voltage Insulators via Laser-Induced Breakdown Spectroscopy

Wang¹,

Lü²,

Wang³

et al. 2020

Molecules

View full text Add to dashboard Cite

Surface pollution deposition in a high voltage surface can reduce the surface flashover voltage, which is considered to be a serious accident in the transmission of electric power for the high conductivity of pollution in wet weather, such as rain or fog. Accordingly, a rapid and accurate online pollution detection method is of great importance for monitoring the safe status of transmission lines. Usually, to detect the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD), the pollution should be collected when power cut off and bring back to lab, time-consuming, low accuracy and unable to meet the online detection. Laser-induced breakdown spectroscopy (LIBS) shows the highest potential for achieving online pollution detection, but its application in high voltage electrical engineering has only just begun to be examined. In this study, a LIBS method for quantitatively detecting the compositions of pollutions on the insulators was investigated, and the spectral characteristics of a natural pollution sample were examined. The energy spectra and LIBS analysis results were compared. LIBS was shown to detect pollution elements that were not detected by conventional energy spectroscopy and had an improved capacity to determine pollution composition. Furthermore, the effects of parameters, such as laser energy intensity and delay time, were investigated for artificial pollutions. Increasing the laser energy intensity and selecting a suitable delay time could enhance the precision and relative spectral intensities of the elements. Additionally, reducing the particle size and increasing the density achieved the same results.

show abstract

“…There are numerous fields where LIBS analysers are used [ 12 ], such as basic scrap metal analysis [ 13 ], classification of alloys [ 14 ], or mapping of geological cores [ 15 ]. Additional uses include sophisticated applications such as adulteration detection in milk [ 16 ], discrimination of heavy-metal-contamination in seafood [ 17 ], analysis of pathological tissues [ 18 ], or precision agriculture [ 19 ]. They are even used with the most demanding space missions [ 20 ] and biohazard detection [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor Factorisation

Wójcik

Brinkmann

Zdunek

et al. 2020

Sensors

View full text Add to dashboard Cite

Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.

show abstract

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

Cited by 21 publications

References 50 publications

A Sparse Classification Based on a Linear Regression Method for Spectral Recognition

A Sparse Classification Based on a Linear Regression Method for Spectral Recognition

Analysis of Pollution in High Voltage Insulators via Laser-Induced Breakdown Spectroscopy

Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor Factorisation

Contact Info

Product

Resources

About