“…Thus, ensemble averaging cannot eliminate the effect of signal variation because ensemble averaging works based on the emission intensities strictly following the normal distribution. 30 Figure 1.The fluctuation of the intensity of C 247.87 nm from 400 repeated measurements on the surface of one sample (C4): (a) frequency histogram of spectral line intensity, (b) normal distribution Q–Q plot of spectral line intensity.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, ensemble averaging cannot eliminate the effect of signal variation because ensemble averaging works based on the emission intensities strictly following the normal distribution. 30 In order to investigate the variation of spectral signals collected at different locations on the surface of one coal sample, the spectrum collected from the first shot laser ablating each measurement location in this work was used to characterize the spectral intensity of each measurement location. The level of spectral signal variation was evaluated by the value of relative standard deviation (RSD).…”
Section: Signal Variation In Libs Measurementmentioning
confidence: 99%
“…Ensemble averaging can reduce the effect of spectral noise when the LIBS spectral intensities obtained from repeated measurement strictly obey the normal distribution. 30 In fact, the spectral intensities only obey an approximate normal distribution. Thus, ensemble averaging cannot eliminate the effect of spectral noise.…”
The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposed a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed lower RSD value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method.
“…Thus, ensemble averaging cannot eliminate the effect of signal variation because ensemble averaging works based on the emission intensities strictly following the normal distribution. 30 Figure 1.The fluctuation of the intensity of C 247.87 nm from 400 repeated measurements on the surface of one sample (C4): (a) frequency histogram of spectral line intensity, (b) normal distribution Q–Q plot of spectral line intensity.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, ensemble averaging cannot eliminate the effect of signal variation because ensemble averaging works based on the emission intensities strictly following the normal distribution. 30 In order to investigate the variation of spectral signals collected at different locations on the surface of one coal sample, the spectrum collected from the first shot laser ablating each measurement location in this work was used to characterize the spectral intensity of each measurement location. The level of spectral signal variation was evaluated by the value of relative standard deviation (RSD).…”
Section: Signal Variation In Libs Measurementmentioning
confidence: 99%
“…Ensemble averaging can reduce the effect of spectral noise when the LIBS spectral intensities obtained from repeated measurement strictly obey the normal distribution. 30 In fact, the spectral intensities only obey an approximate normal distribution. Thus, ensemble averaging cannot eliminate the effect of spectral noise.…”
The accuracy and precision of laser-induced breakdown spectroscopy (LIBS) quantitative analysis are significantly limited by the spectral noise. Normalization and ensemble averaging of multiple spectra were often used to preprocess spectra. However, these methods cannot completely remove the spectral noise. Data uncertainty due to the irremovable spectral noise will affect LIBS quantitative analysis. Therefore, this paper proposed a method using data uncertainty to improve the performance of LIBS quantitative analysis. The proposed method uses several spectra to characterize each sample to preserve some data uncertainty in the calibration data matrix. Thus, the data uncertainty is used to optimize the calibration model for improving the toleration to the spectral signal variation. As a result, the optimized calibration model had better accuracy and robustness than the calibration model trained by conventional method. The best root mean square error of prediction (RMSEP) of the ash content of coal was 1.152% for the optimized calibration model, while that for the conventional calibration model was 1.718%. The optimized calibration model also showed lower RSD value of repeated predictions. Moreover, the calibration model for predicting the ash content in biomass was also optimized by the proposed method. The optimized calibration model outperformed the conventional calibration model again, which demonstrated the extensive applicability of the proposed method.
“…The improved weighting function exhibited better comprehensive performance in model robustness and convergence speed compared with the 4 known weighting functions. Yang et al proposed a new LIBS quantitative analysis method based on analytical line adaptive selection and the relevance vector machine (RVM) regression model. RVM has been successfully applied in machine learning as a sparse probabilistic model based on limited samples.…”
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
“…As is well known, one main challenge of LIBS is the improvement of the accuracy of quantitative predictions. In recent years, various chemometric methods have been widely applied to LIB spectra to improve the detection accuracy, such as principle components regression (PCR) [33,34], partial least squares (PLS) [35,36], the support vector machine (SVM) [37,38], and the extreme learning machine (ELM) [39,40]. Among these regression models, PLS and SVM are commonly used.…”
Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.
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