Classification of LIBS protein spectra using support vector machines and adaptive local hyperplanes

Vance, Tia; Reljin, Nataša; Lazarević, Aleksandar; Pokrajac, Dragoljub; Kecman, Vojislav; Melikechi, Noureddine; Marcano, Arı́stides; Markushin, Yuri; McDaniel, Samantha

doi:10.1109/ijcnn.2010.5596575

Cited by 13 publications

(22 citation statements)

References 11 publications

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“…Thus, PCA could be used to compress the original dataset into a reduced number of factors before introducing them into the model of classification. As an example, PCA was used prior to HCA [50] and PCA was also applied to compress the LIBS data related to different classes of proteins, prior to their treatment by SVM [62]. One can conclude that the results of classification strongly depend on the choice of the predictors.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Then the average spectrum for each sample was calculated, the data were compressed by wavelet compression and finally three techniques of variable selection were applied, namely genetic, successive projections, and stepwise algorithms [55]. Furthermore, principal component analysis (PCA) has also been used to compress the initial data before their introduction into a model of cluster analysis [50], and PCA was also used to compress the LIBS data of different types of proteins before their introduction into a SVM model [62].…”

Section: Influence Of the Predictorsmentioning

confidence: 99%

“…30 Table 3 Abbreviation Full description References R² Correlation coefficient [62,66] Q² Prediction ability [66] RSD (%) Relative standard deviation [19,36] RMSE Root mean square error [19,70] RE(%) Relative error [19,36] LOD Limit of detection [68] LOQ Limit of quantification [68] Uncertainty (limits of confidence) [62] …”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Good practices in LIBS analysis: Review and advices

Haddad

Canioni

Bousquet

2014

Spectrochimica Acta Part B: Atomic Spectroscopy

274

109

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International audienceThis paper presents a review on the analytical results obtained by laser-induced breakdown spectroscopy (LIBS). In the first part, results on identification and classification of samples are presented including the risk of misclas-sification, and in the second part, results on concentration measurement based on calibration are accompanied with significant figures of merit including the concept of accuracy. Both univariate and multivariate approaches are discussed with special emphasis on the methodology, the way of presenting the results and the assessment of the methods. Finally, good practices are proposed for both classification and concentration measurement

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Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Influence Of the Predictorsmentioning

confidence: 99%

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Good practices in LIBS analysis: Review and advices

Haddad

Canioni

Bousquet

2014

Spectrochimica Acta Part B: Atomic Spectroscopy

274

109

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“…Principal component analysis (PCA) is a popular feature extraction technique to identify important features and reduce dimensionality to perform the classification task. PCA projects highly dimensional data (eg, >10 000) onto a lower dimension using a linear transformation . Typically, not all attributes or dimensions are useful for learning algorithms, and there is a threshold number of attributes above which the performance of the classifier can degrade .…”

Section: Introductionmentioning

confidence: 99%

“…PCA projects highly dimensional data (eg, >10 000) onto a lower dimension using a linear transformation. 13,14 Typically, not all attributes or dimensions are useful for learning algorithms, and there is a threshold number of attributes above which the performance of the classifier can degrade. 15 The wavelet transform technique can also be used as a dimensionality reduction technique before classification techniques are applied to the LIBS signals.…”

mentioning

confidence: 99%

Classification and statistical analysis of hydrothermal seafloor rocks measured underwater using laser‐induced breakdown spectroscopy

Yelameli

Thornton

Takahashi

et al. 2018

Journal of Chemometrics

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This study investigates the use of statistical methods for the classification of laser‐induced breakdown spectroscopy (LIBS) measurements of water‐immersed rocks with respect to their labels and geological groups. The analysis is performed on deep‐sea hydrothermal deposit rocks. These rocks are categorized on the basis of the relative ratio of Zn‐Pb‐Cu on a ternary diagram. The proposed method demonstrates that the accurate classification of rocks with respect to their labels and geological group from the LIBS data can be successfully achieved by combining principal component analysis (PCA), which reduces the dimensionality of the data, with classification algorithms such as the support vector machine (SVM), k‐nearest neighbor search (KNN), and artificial neural network (ANN) methods. The performance of the classification algorithms depends on the size of the dataset. Additionally, removing the linear trend from the data enhances the performance of the classification in terms of sensitivity. The best classification performance concerning the rock label is obtained using an SVM linear kernel algorithm with 95% sensitivity. The best classification performance concerning the geological group is obtained using the SVM method with 98% accuracy. The one‐sided Wilcoxon signed rank test is applied to the classification results in the rock label and group cases, and the results indicate that the SVM algorithm has statistical significance over the other algorithms while classifying the rock labels and rock group.

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