Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF)

Sheng, Liwen; Zhang, Tianlong; Niu, Guanghui; Wang, Kang; Tang, Hongsheng; Duan, Yixiang; Li, Hua

doi:10.1039/c4ja00352g

Cited by 91 publications

(27 citation statements)

References 33 publications

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“…To obtain an excellent classification performance, two factors of regularization parameter γ and the RBF kernel function parameter σ 2 in LS-SVM classifier have to be optimized. The parameter γ could determine the tradeoff between maximizing the model performance and minimizing model complexity, and the σ 2 was the bandwidth and implicitly defined the nonlinear mapping from input space to some high-dimensional feature space 55 56 57 . Based on LS-SVM model, its classified results of predication set (60 spectra) are summarized in a confusion matrix presented in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Zhao

Liu

et al. 2016

Sci Rep

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The aim of this work was to analyze the variety of soil by laser-induced breakdown spectroscopy (LIBS) coupled with chemometrics methods. 6 certified reference materials (CRMs) of soil samples were selected and their LIBS spectra were captured. Characteristic emission lines of main elements were identified based on the LIBS curves and corresponding contents. From the identified emission lines, LIBS spectra in 7 lines with high signal-to-noise ratio (SNR) were chosen for further analysis. Principal component analysis (PCA) was carried out using the LIBS spectra at 7 selected lines and an obvious cluster of 6 soils was observed. Soft independent modeling of class analogy (SIMCA) and least-squares support vector machine (LS-SVM) were introduced to establish discriminant models for classifying the 6 types of soils, and they offered the correct discrimination rates of 90% and 100%, respectively. Receiver operating characteristic (ROC) curve was used to evaluate the performance of models and the results demonstrated that the LS-SVM model was promising. Lastly, 8 types of soils from different places were gathered to conduct the same experiments for verifying the selected 7 emission lines and LS-SVM model. The research revealed that LIBS technology coupled with chemometrics could conduct the variety discrimination of soil.

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Section: Resultsmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Zhao

Liu

et al. 2016

Sci Rep

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“…The univariate methods mainly include single linear regression [83], internal standard [84], and external standard [85]. Commonly used multivariate methods include partial least squares [86], artificial neural network [87,88], partial least squares regression [89], least squares support vector machines [90,91], and random forest [92].…”

Section: Mathematical Methodsmentioning

confidence: 99%

Improving the Detection Sensitivity for Laser-Induced Breakdown Spectroscopy: A Review

Dong

2020

Front. Phys.

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Laser-induced breakdown spectroscopy (LIBS) is fast, on-line, causes little sample damage, and can be applied in remote field locations. In recent years, LIBS has been widely used in many fields of scientific research for element detection. Further application of LIBS is limited by the strong matrix effect, poor repeatability, and relatively weak detection sensitivity. The detection sensitivity is an important factor and needs to be improved for LIBS detection of minor or trace elements in samples. A variety of methods have been developed to improve detection sensitivity of LIBS. In this invited review paper, we discuss improvements in the LIBS detection sensitivity achieved with physical enhancement methods, chemical enhancement methods, mathematical methods, and combinations of multiple methods. We discuss the enhancement mechanisms, sensitivity improvements, configurations, and effects of key factors for various methods. The advantages, disadvantages, and real-time capabilities of these methods are reviewed. Finally, new trends and future perspectives for LIBS as an efficient analytical tool are discussed.

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“…The fundamental idea of supervised pattern recognition is that samples with a known class as a training set are used to construct a training model and then the class or grade of an unknown sample is predicted by the training model. The common supervised pattern recognition methods mainly include partial least squares discriminate analysis (PLS‐DA), soft independent modeling of class analog (SIMCA), K‐nearest neighbor (KNN), SVM, artificial neural networks (ANN), and random forest (RF) …”

Section: Qualitative Analysismentioning

confidence: 99%

“…Remus and Dunsin compared RF with PLS‐DA to classify and recognize 5 different materials (4 rock samples and an ink sample), and RF showed better classification efficiency (100%). Sheng et al used SVM and RF methods to discriminate and classify 10 types of iron ore samples; although both the SVM and RF models provided acceptably accurate predictions, RF provided better classification predictions. At the same time, Zhang et al proposed LIBS integrated with RF to identify and discriminate 9 steel grades, and its generation ability was evaluated by the out‐of‐bag estimation and 5‐fold CV.…”

Section: Qualitative Analysismentioning

confidence: 99%

Chemometrics in laser‐induced breakdown spectroscopy

Zhang

Tang

2018

Journal of Chemometrics

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Laser-induced breakdown spectroscopy (LIBS) is a new type of elemental analytical technology with the advantages of real-time, online, and noncontact as well as enabling the simultaneous analysis of multiple elements. It has become a frontier analytical technique in spectral analysis. However, the issue of how to improve the accuracy of qualitative and quantitative analyses by extracting useful information from a large amount of complex LIBS data remains the main problem for the LIBS technique. Chemometrics is a chemical subdiscipline of multi-interdisciplinary methods; it offers advantages in data processing, signal analysis, and pattern recognition. It can solve some complicated problems that are difficult for traditional chemical methods. In this paper, we reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012-2016). KEYWORDSchemometrics, data preprocessing, laser-induced breakdown spectroscopy, qualitative analysis, quantitative analysis | INTRODUCTIONAs one of the most promising analytical tools in the 21st century, laser-induced breakdown spectroscopy (LIBS) is a novel analytical technique for materials and elements; it is based on atomic emission spectroscopy and a laser as an excitation source. 1-4 In LIBS, a low-energy pulse laser (typically tens to hundreds of millijoules per pulse) generated from a laser device is reflected by a plane mirror and focused by a plano-convex lens onto the sample surface; a laser-induced plasma with high temperature is then generated. The light with different frequencies is radiated during the plasma cooling process, and it is collected by an optical fiber coupled to a spectrometer. The qualitative and quantitative analyses by the LIBS technique can be performed using the wavelength and integrated intensity of the spectral line. A schematic diagram of the LIBS instrument is presented in Figure 1. It consists of Nd:YAG laser, optical part, X-Y-Z manual micrometric stage, optical fiber, spectrometer, Charge coupled device (CCD) camera, and computer.The LIBS technology has benefited from the invention of the ruby crystal laser 5 and was first reported by Brech and Cross. 6 It has become a research focus in the spectral field with the development of laser and optical detection techniques. Compared with conventional analytical tools, LIBS demonstrates some substantial advantages with regards to being rapid, real-time, on-site, and nondestructive while allowing remote detection without complicated sample preparation procedures as well as simultaneous multicomponent analysis. 7-9 Because of these advantages, LIBS technology has been widely used in process analysis and in the control of industrial manufacturing, food supervision, environmental monitoring, historical relic identification, and space exploration. [10][11][12][13] The National Aeronautics and

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Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF)

Abstract: Laser-induced breakdown spectroscopy combined with the random forest (RF) algorithm was proposed for the classification of ten iron ore samples.

Cited by 91 publications

References 33 publications

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Laser-Induced Breakdown Spectroscopy Coupled with Multivariate Chemometrics for Variety Discrimination of Soil

Improving the Detection Sensitivity for Laser-Induced Breakdown Spectroscopy: A Review

Chemometrics in laser‐induced breakdown spectroscopy

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