A critical review of recent progress in analytical laser-induced breakdown spectroscopy

Galbács, Gábor

doi:10.1007/s00216-015-8855-3

Cited by 173 publications

(83 citation statements)

References 215 publications

(186 reference statements)

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“…1b) considering that randomization was necessary to effectively assess this material. After the mapping, an average spectrum was calculated for each sample following the procedure preconized by Galbács [17].…”

Section: Resultsmentioning

confidence: 99%

Expeditious prediction of fiber content in sugar cane: An analytical possibility with LIBS and chemometrics

Romera

Barsanelli

Pereira

2016

Fuel

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a b s t r a c tThe development of a multivariate model for fiber content prediction in sugar cane has been investigated and can be offered as an alternative to the wet methods of analysis. The importance of fiber in bagasse of sugar cane is directly linked to the production of sugar and alcohol performances and to payment systems. The analytical method investigated here was the Laser-Induced Breakdown Spectroscopy (LIBS) combined with a Partial Least Squares (PLS) chemometric tool that can achieve the best model to predict fiber content in sugar cane. A total of 6287 spectra of raw samples of sugar cane bagasse was evaluated. The optimum conditions of operation of the equipment for the acquisition of the most sensitive LIBS signals were tested through Design of Experiments (DOE).

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Section: Resultsmentioning

confidence: 99%

Expeditious prediction of fiber content in sugar cane: An analytical possibility with LIBS and chemometrics

Romera

Barsanelli

Pereira

2016

Fuel

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“…A Q-switch pulsed laser (Vlite 200, Beamtech, Beijing, China) was used to ablate samples with the maximal energy of 300 mJ @1064 nm, pulse duration of 8 ns. Since great absorption might occur within fluid medium specimens in the near infrared region, it might be unsuitable to use infrared laser for fresh leaves20. In this case, the laser was operated at the second harmonics wavelength with 60 mJ pulse energy and 1 Hz repetition rate.…”

Section: Methodsmentioning

confidence: 99%

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

Peng

Song

Zhu

et al. 2017

Sci Rep

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Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves.

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“…1,2 During the ablation process, a great variety of data are obtained in a short interval of time due to the high analytical frequency when compared with other spectroanalytical techniques. This plasma provides information about the chemical composition of the sample.…”

Section: Introductionmentioning

confidence: 99%

Direct chemical inspection of eye shadow and lipstick solid samples using laser-induced breakdown spectroscopy (LIBS) and chemometrics: proposition of classification models

2016

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22mg kg -1 , 29 were correctly predicted and 1 sample was incorrectly predicted.Only 2 samples were considered as not belonging to any of the classes, and thus the model presented satisfactory results. In the case of PLS-DA, the average accuracy was 77%, and 3 latent variables were selected for each class. The KNN and SIMCA chemometric strategies presented good results for the two classes; however, the KNN model showed better accuracy (98% on average). Table 3 shows the results for KNN, SIMCA and PLS-DA and some figures of merit (%) for the proposed models (accuracy, sensitivity, false alarm rate and specificity). 42As can be noted in Table 3, the accuracy for KNN and SIMCA ranged from 96 to 98%. This parameter is related to the number of correct predictions. The average sensitivity for KNN was 97%. This parameter is related to the samples that belong to a certain class and were predicted to belong to the other class. The sensitivity for class 1 (below) in KNN, for example, was 94%, which means that one sample from class 2 (above) was predicted as class 1. On the other hand, the sensitivity for class 2 was 100%, which means that no sample from class 2 was predicted as class 1.The average sensitive was 97% ((94+100)/2). Specificity is the inverse of sensitivity, and false alarm rate for class 2, for example, is the number of samples from class 2 predicted as belonging to class 1 divided by the total

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A critical review of recent progress in analytical laser-induced breakdown spectroscopy

Cited by 173 publications

References 215 publications

Expeditious prediction of fiber content in sugar cane: An analytical possibility with LIBS and chemometrics

Expeditious prediction of fiber content in sugar cane: An analytical possibility with LIBS and chemometrics

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

Direct chemical inspection of eye shadow and lipstick solid samples using laser-induced breakdown spectroscopy (LIBS) and chemometrics: proposition of classification models

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