Abstract

The correct classification of pathogenic bacteria is significant for clinical diagnosis and treatment. Compared with the use of whole spectral data, using feature lines as the inputs of the classification model can improve the correct classification rate (CCR) and reduce the analyzing time. In order to select feature lines, we need to investigate the contribution to the CCR of each spectral line. In this paper, two algorithms, important weights based on principal component analysis (IW-PCA) and random forests (RF), were proposed to evaluate the importance of spectra lines. The laser-induced plasma spectra (LIBS) of six common clinical pathogenic bacteria species were measured and a support vector machine (SVM) classifier was used to classify the LIBS of bacteria species. In the proposed IW-PCA algorithm, the product of the loading of each line and the variance of the corresponding principal component were calculated. The maximum product of each line calculated from the first three PCs was used to represent the line’s importance weight. In the RF algorithm, the Gini index reduction value of each line was considered as the line’s importance weight. The experimental results demonstrated that the lines with high importance were more suitable for classification and can be chosen as feature lines. The optimal number of feature lines used in the SVM classifier can be determined by comparing the CCRs with a different number of feature lines. Importance weights evaluated by RF are more suitable for extracting feature lines using LIBS combined with an SVM classification mechanism than those evaluated by IW-PCA. Furthermore, the two methods mutually verified the importance of selected lines and the lines evaluated important by both IW-PCA and RF contributed more to the CCR.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

2017 (7)

E. Teran-Hinojosa, H. Sobral, C. Sánchez-Pérez, A. Pérez-García, N. Alemán-García, and J. Hernández-Ruiz, “Differentiation of fibrotic liver tissue using laser-induced breakdown spectroscopy,” Biomed. Opt. Express 8(8), 3816–3827 (2017).
[Crossref] [PubMed]

I. Ahmed, R. Ahmed, J. Yang, A. W. L. Law, Y. Zhang, and C. Lau, “Elemental analysis of the thyroid by laser induced breakdown spectroscopy,” Biomed. Opt. Express 8(11), 4865–4871 (2017).
[Crossref] [PubMed]

L. Váradi, J. L. Luo, D. E. Hibbs, J. D. Perry, R. J. Anderson, S. Orenga, and P. W. Groundwater, “Methods for the detection and identification of pathogenic bacteria: past, present, and future,” Chem. Soc. Rev. 46(16), 4818–4832 (2017).
[Crossref] [PubMed]

C. Brady, D. Arnold, J. McDonald, and S. Denman, “Taxonomy and identification of bacteria associated with acute oak decline,” World J. Microbiol. Biotechnol. 33(7), 143 (2017).
[Crossref] [PubMed]

V. Chalansonnet, C. Mercier, S. Orenga, and C. Gilbert, “Identification of Enterococcus faecalis enzymes with azoreductases and/or nitroreductase activity,” BMC Microbiol. 17(1), 126 (2017).
[Crossref] [PubMed]

D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

K. Wang, P. Guo, and A. Luo, “A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery,” Mon. Not. R. Astron. Soc. 465(4), 4311–4324 (2017).
[Crossref]

2016 (4)

E. Vors, K. Tchepidjian, and J. B. Sirven, “Evaluation and optimization of the robustness of a multivariate analysis methodology for identification of alloys by laser induced breakdown spectroscopy,” Spectrochim. Acta B At. Spectrosc. 117, 16–22 (2016).
[Crossref]

Q. Q. Wang, L. A. He, Y. Zhao, Z. Peng, and L. Liu, “Study of cluster analysis used in explosives classification with laser-induced breakdown spectroscopy,” Laser Phys. 26(6), 065605 (2016).
[Crossref]

L. He, Q. Q. Wang, Y. Zhao, L. Liu, and Z. Peng, “StudyonClusterAnalysisUsedwithLaser-InducedBreakdownSpectroscopy,” Plasma Sci. Technol. 18(6), 647–653 (2016).
[Crossref]

C. D. Doern, “The Confounding Role of Antimicrobial Stewardship Programs in Understanding the Impact of Technology on Patient Care,” J. Clin. Microbiol. 54(10), 2420–2423 (2016).
[Crossref] [PubMed]

2015 (5)

Z. Wang, F. Dong, and W. Zhou, “A Rising Force for the World-Wide Development of Laser-Induced Breakdown Spectroscopy,” Plasma Sci. Technol. 17(8), 617–620 (2015).
[Crossref]

S. Pahlow, S. Meisel, D. Cialla-May, K. Weber, P. Rösch, and J. Popp, “Isolation and identification of bacteria by means of Raman spectroscopy,” Adv. Drug Deliv. Rev. 89, 105–120 (2015).
[Crossref] [PubMed]

A. K. Myakalwar, N. Spegazzini, C. Zhang, S. K. Anubham, R. R. Dasari, I. Barman, and M. K. Gundawar, “Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection,” Sci. Rep. 5, 13169 (2015).

L. Sheng, T. Zhang, G. Niu, K. Wang, H. Tang, Y. Duan, and H. Li, “Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF),” J. Anal. At. Spectrom. 30(2), 453–458 (2015).
[Crossref]

T. Zhang, S. Wu, J. Dong, J. Wei, K. Wang, H. Tang, X. Yang, and H. Li, “Quantitative and classification analysis of slag samples by Laser-induced breakdown spectroscopy(LIBS) coupled with support vector machine(SVM) and partial least square(PLS) methods,” J. Anal. At. Spectrom. 30(2), 368–374 (2015).
[Crossref]

2014 (1)

J. L. Nagel, A. M. Huang, A. Kunapuli, T. N. Gandhi, L. L. Washer, J. Lassiter, T. Patel, and D. W. Newton, “Impact of Antimicrobial Stewardship Intervention on Coagulase-Negative Staphylococcus Blood Cultures in Conjunction with Rapid Diagnostic Testing,” J. Clin. Microbiol. 52(8), 2849–2854 (2014).
[Crossref] [PubMed]

2013 (2)

P. S. Hiremath, P. Bannigidad, and S. S. Yelgond, “An Improved Automated Method for Identification of Bacterial Cell Morphological Characteristics,” IJATCSE 2, 11–16 (2013).

Z. Haider, Y. Munajat, R. K. R. Ibrahim, and M. Rashid, “Identification of materials through SVM classification of their LIBS spectra,” Jurnal. Teknologi. 62(3), 123–127 (2013).
[Crossref]

2012 (4)

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. Kumar Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84(6), 2686–2694 (2012).
[Crossref] [PubMed]

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemometr. 26(5), 143–149 (2012).
[Crossref]

J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
[Crossref] [PubMed]

H. Zokaeifar, J. L. Balcázar, M. S. Kamarudin, K. Sijam, A. Arshad, and C. R. Saad, “Selection and identification of non-pathogenic bacteria isolated from fermented pickles with antagonistic properties against two shrimp pathogens,” J. Antibiot.  65(6), 289–294 (2012).
[Crossref] [PubMed]

2011 (2)

D. Marcos-Martinez, J. A. Ayala, R. C. Izquierdo-Hornillos, F. J. de Villena, and J. O. Caceres, “Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks,” Talanta 84(3), 730–737 (2011).
[Crossref] [PubMed]

A. Verikas, A. Gelzinis, and M. Bacauskiene, “Mining data with random forests: A survey and results of new tests,” Pattern Recognit. 44(2), 330–349 (2011).
[Crossref]

2010 (6)

S. J. Rehse, Q. I. Mohaidat, and S. Palchaudhuri, “Towards the clinical application of laser-induced breakdown spectroscopy for rapid pathogen diagnosis: the effect of mixed cultures and sample dilution on bacterial identification,” Appl. Opt. 49(13), C27–C35 (2010).
[Crossref]

R. A. Multari, D. A. Cremers, J. M. Dupre, and J. E. Gustafson, “The use of laser-induced breakdown spectroscopy for distinguishing between bacterial pathogen species and strains,” Appl. Spectrosc. 64(7), 750–759 (2010).
[Crossref] [PubMed]

H. Abdi and L. J. Williams, “Principal component analysis,” WIREs Comp. Stats. 2, 433–459 (2010).

E. Tognoni, G. Cristoforetti, S. Legnaioli, and V. Palleschi, “Calibration-Free Laser-Induced Breakdown Spectroscopy: State of the art,” Spectrochim. Acta B At. Spectrasc. 65, 1–14 (2010).

K. A. Bauer, J. E. West, J. M. Balada-Llasat, P. Pancholi, K. B. Stevenson, and D. A. Goff, “An antimicrobial stewardship program’s impact with rapid polymerase chain reaction methicillin-resistant Staphylococcus aureus/S. aureus blood culture test in patients with S. aureus bacteremia,” Clin. Infect. Dis. 51(9), 1074–1080 (2010).
[Crossref] [PubMed]

J. S. P. Joseph Capriotti, “Bacterial Resistance: Causes and Consequences,” Ophthalmol. Management 10, 10 (2010).

2009 (1)

R. S. Harmon, J. Remus, N. J. Mcmillan, C. Mcmanus, L. Collins, J. L. G. Jr, F. C. Delucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24(6), 1125–1141 (2009).
[Crossref]

2008 (1)

M. Cizman, “Experiences in prevention and control of antibiotic resistance in Slovenia,” Eurosurveillance: European Communicable Disease Bulletin 13, 19038 (2008).

2006 (1)

R. Díaz-Uriarte and S. Alvarez de Andrés, “Gene selection and classification of microarray data using random forest,” BMC Bioinformatics 7(1), 3 (2006).
[Crossref] [PubMed]

2004 (1)

A. M. Alvarez, “Integrated approaches for detection of plant pathogenic bacteria and diagnosis of bacterial diseases,” Annu. Rev. Phytopathol. 42(1), 339–366 (2004).
[Crossref] [PubMed]

2000 (1)

T. G. Dietterich, “An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization,” Mach. Learn. 40(2), 139–157 (2000).
[Crossref]

1999 (1)

R. Dietrich, M. Opper, and H. Sompolinsky, “Statistical mechanics of support vector networks,” Phys. Rev. Lett. 82(14), 2975–2978 (1999).
[Crossref]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20(3), 273–297 (1995).
[Crossref]

Abdi, H.

H. Abdi and L. J. Williams, “Principal component analysis,” WIREs Comp. Stats. 2, 433–459 (2010).

Abujamel, T.

J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
[Crossref] [PubMed]

Ahmed, I.

Ahmed, R.

Alemán-García, N.

Alvarez, A. M.

A. M. Alvarez, “Integrated approaches for detection of plant pathogenic bacteria and diagnosis of bacterial diseases,” Annu. Rev. Phytopathol. 42(1), 339–366 (2004).
[Crossref] [PubMed]

Alvarez de Andrés, S.

R. Díaz-Uriarte and S. Alvarez de Andrés, “Gene selection and classification of microarray data using random forest,” BMC Bioinformatics 7(1), 3 (2006).
[Crossref] [PubMed]

Anderson, R. J.

L. Váradi, J. L. Luo, D. E. Hibbs, J. D. Perry, R. J. Anderson, S. Orenga, and P. W. Groundwater, “Methods for the detection and identification of pathogenic bacteria: past, present, and future,” Chem. Soc. Rev. 46(16), 4818–4832 (2017).
[Crossref] [PubMed]

Anubham, S. K.

A. K. Myakalwar, N. Spegazzini, C. Zhang, S. K. Anubham, R. R. Dasari, I. Barman, and M. K. Gundawar, “Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection,” Sci. Rep. 5, 13169 (2015).

Arnold, D.

C. Brady, D. Arnold, J. McDonald, and S. Denman, “Taxonomy and identification of bacteria associated with acute oak decline,” World J. Microbiol. Biotechnol. 33(7), 143 (2017).
[Crossref] [PubMed]

Arshad, A.

H. Zokaeifar, J. L. Balcázar, M. S. Kamarudin, K. Sijam, A. Arshad, and C. R. Saad, “Selection and identification of non-pathogenic bacteria isolated from fermented pickles with antagonistic properties against two shrimp pathogens,” J. Antibiot.  65(6), 289–294 (2012).
[Crossref] [PubMed]

Arthur, J. C.

J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
[Crossref] [PubMed]

Ayala, J. A.

D. Marcos-Martinez, J. A. Ayala, R. C. Izquierdo-Hornillos, F. J. de Villena, and J. O. Caceres, “Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks,” Talanta 84(3), 730–737 (2011).
[Crossref] [PubMed]

Bacauskiene, M.

A. Verikas, A. Gelzinis, and M. Bacauskiene, “Mining data with random forests: A survey and results of new tests,” Pattern Recognit. 44(2), 330–349 (2011).
[Crossref]

Balada-Llasat, J. M.

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A. K. Myakalwar, N. Spegazzini, C. Zhang, S. K. Anubham, R. R. Dasari, I. Barman, and M. K. Gundawar, “Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection,” Sci. Rep. 5, 13169 (2015).

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L. Sheng, T. Zhang, G. Niu, K. Wang, H. Tang, Y. Duan, and H. Li, “Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF),” J. Anal. At. Spectrom. 30(2), 453–458 (2015).
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Fan, T. J.

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J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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K. A. Bauer, J. E. West, J. M. Balada-Llasat, P. Pancholi, K. B. Stevenson, and D. A. Goff, “An antimicrobial stewardship program’s impact with rapid polymerase chain reaction methicillin-resistant Staphylococcus aureus/S. aureus blood culture test in patients with S. aureus bacteremia,” Clin. Infect. Dis. 51(9), 1074–1080 (2010).
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A. K. Myakalwar, N. Spegazzini, C. Zhang, S. K. Anubham, R. R. Dasari, I. Barman, and M. K. Gundawar, “Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection,” Sci. Rep. 5, 13169 (2015).

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K. Wang, P. Guo, and A. Luo, “A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery,” Mon. Not. R. Astron. Soc. 465(4), 4311–4324 (2017).
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Haider, Z.

Z. Haider, Y. Munajat, R. K. R. Ibrahim, and M. Rashid, “Identification of materials through SVM classification of their LIBS spectra,” Jurnal. Teknologi. 62(3), 123–127 (2013).
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Hannig, J.

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemometr. 26(5), 143–149 (2012).
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J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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R. S. Harmon, J. Remus, N. J. Mcmillan, C. Mcmanus, L. Collins, J. L. G. Jr, F. C. Delucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24(6), 1125–1141 (2009).
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He, L. A.

Q. Q. Wang, L. A. He, Y. Zhao, Z. Peng, and L. Liu, “Study of cluster analysis used in explosives classification with laser-induced breakdown spectroscopy,” Laser Phys. 26(6), 065605 (2016).
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Hibbs, D. E.

L. Váradi, J. L. Luo, D. E. Hibbs, J. D. Perry, R. J. Anderson, S. Orenga, and P. W. Groundwater, “Methods for the detection and identification of pathogenic bacteria: past, present, and future,” Chem. Soc. Rev. 46(16), 4818–4832 (2017).
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P. S. Hiremath, P. Bannigidad, and S. S. Yelgond, “An Improved Automated Method for Identification of Bacterial Cell Morphological Characteristics,” IJATCSE 2, 11–16 (2013).

Huang, A. M.

J. L. Nagel, A. M. Huang, A. Kunapuli, T. N. Gandhi, L. L. Washer, J. Lassiter, T. Patel, and D. W. Newton, “Impact of Antimicrobial Stewardship Intervention on Coagulase-Negative Staphylococcus Blood Cultures in Conjunction with Rapid Diagnostic Testing,” J. Clin. Microbiol. 52(8), 2849–2854 (2014).
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Z. Haider, Y. Munajat, R. K. R. Ibrahim, and M. Rashid, “Identification of materials through SVM classification of their LIBS spectra,” Jurnal. Teknologi. 62(3), 123–127 (2013).
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D. Marcos-Martinez, J. A. Ayala, R. C. Izquierdo-Hornillos, F. J. de Villena, and J. O. Caceres, “Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks,” Talanta 84(3), 730–737 (2011).
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J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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R. S. Harmon, J. Remus, N. J. Mcmillan, C. Mcmanus, L. Collins, J. L. G. Jr, F. C. Delucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24(6), 1125–1141 (2009).
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Kaiser, J.

D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

Kamarudin, M. S.

H. Zokaeifar, J. L. Balcázar, M. S. Kamarudin, K. Sijam, A. Arshad, and C. R. Saad, “Selection and identification of non-pathogenic bacteria isolated from fermented pickles with antagonistic properties against two shrimp pathogens,” J. Antibiot.  65(6), 289–294 (2012).
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Keku, T. O.

J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

Kumar Gundawar, M.

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. Kumar Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84(6), 2686–2694 (2012).
[Crossref] [PubMed]

Kunapuli, A.

J. L. Nagel, A. M. Huang, A. Kunapuli, T. N. Gandhi, L. L. Washer, J. Lassiter, T. Patel, and D. W. Newton, “Impact of Antimicrobial Stewardship Intervention on Coagulase-Negative Staphylococcus Blood Cultures in Conjunction with Rapid Diagnostic Testing,” J. Clin. Microbiol. 52(8), 2849–2854 (2014).
[Crossref] [PubMed]

Lassiter, J.

J. L. Nagel, A. M. Huang, A. Kunapuli, T. N. Gandhi, L. L. Washer, J. Lassiter, T. Patel, and D. W. Newton, “Impact of Antimicrobial Stewardship Intervention on Coagulase-Negative Staphylococcus Blood Cultures in Conjunction with Rapid Diagnostic Testing,” J. Clin. Microbiol. 52(8), 2849–2854 (2014).
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Lau, C.

Law, A. W. L.

Lazarevic, A.

D. Pokrajac, T. Vance, A. Lazarević, A. Marcano, Y. Markushin, N. Melikechi, and N. Reljin, “Performance of multilayer perceptrons for classification of LIBS protein spectra,” inSymposium on Neural Network Applications in Electrical Engineering (IEEE, 2010), pp. 171–174.
[Crossref]

Legnaioli, S.

E. Tognoni, G. Cristoforetti, S. Legnaioli, and V. Palleschi, “Calibration-Free Laser-Induced Breakdown Spectroscopy: State of the art,” Spectrochim. Acta B At. Spectrasc. 65, 1–14 (2010).

Li, H.

T. Zhang, S. Wu, J. Dong, J. Wei, K. Wang, H. Tang, X. Yang, and H. Li, “Quantitative and classification analysis of slag samples by Laser-induced breakdown spectroscopy(LIBS) coupled with support vector machine(SVM) and partial least square(PLS) methods,” J. Anal. At. Spectrom. 30(2), 368–374 (2015).
[Crossref]

L. Sheng, T. Zhang, G. Niu, K. Wang, H. Tang, Y. Duan, and H. Li, “Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF),” J. Anal. At. Spectrom. 30(2), 453–458 (2015).
[Crossref]

Li, W.

W. Li, J. Du, and B. Yi, “Study on classification for vegetation spectral feature extraction method based on decision tree algorithm,” inInternational Conference on Image Analysis and Signal Processing (IEEE, 2011), pp. 665–669.

Li, X.

Liu, A.

Liu, L.

Q. Q. Wang, L. A. He, Y. Zhao, Z. Peng, and L. Liu, “Study of cluster analysis used in explosives classification with laser-induced breakdown spectroscopy,” Laser Phys. 26(6), 065605 (2016).
[Crossref]

L. He, Q. Q. Wang, Y. Zhao, L. Liu, and Z. Peng, “StudyonClusterAnalysisUsedwithLaser-InducedBreakdownSpectroscopy,” Plasma Sci. Technol. 18(6), 647–653 (2016).
[Crossref]

Luo, A.

K. Wang, P. Guo, and A. Luo, “A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery,” Mon. Not. R. Astron. Soc. 465(4), 4311–4324 (2017).
[Crossref]

Luo, J. L.

L. Váradi, J. L. Luo, D. E. Hibbs, J. D. Perry, R. J. Anderson, S. Orenga, and P. W. Groundwater, “Methods for the detection and identification of pathogenic bacteria: past, present, and future,” Chem. Soc. Rev. 46(16), 4818–4832 (2017).
[Crossref] [PubMed]

Marcano, A.

D. Pokrajac, T. Vance, A. Lazarević, A. Marcano, Y. Markushin, N. Melikechi, and N. Reljin, “Performance of multilayer perceptrons for classification of LIBS protein spectra,” inSymposium on Neural Network Applications in Electrical Engineering (IEEE, 2010), pp. 171–174.
[Crossref]

Marcos-Martinez, D.

D. Marcos-Martinez, J. A. Ayala, R. C. Izquierdo-Hornillos, F. J. de Villena, and J. O. Caceres, “Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks,” Talanta 84(3), 730–737 (2011).
[Crossref] [PubMed]

Markushin, Y.

D. Pokrajac, T. Vance, A. Lazarević, A. Marcano, Y. Markushin, N. Melikechi, and N. Reljin, “Performance of multilayer perceptrons for classification of LIBS protein spectra,” inSymposium on Neural Network Applications in Electrical Engineering (IEEE, 2010), pp. 171–174.
[Crossref]

Mazura, M.

D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

McDonald, J.

C. Brady, D. Arnold, J. McDonald, and S. Denman, “Taxonomy and identification of bacteria associated with acute oak decline,” World J. Microbiol. Biotechnol. 33(7), 143 (2017).
[Crossref] [PubMed]

Mcmanus, C.

R. S. Harmon, J. Remus, N. J. Mcmillan, C. Mcmanus, L. Collins, J. L. G. Jr, F. C. Delucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24(6), 1125–1141 (2009).
[Crossref]

Mcmillan, N. J.

R. S. Harmon, J. Remus, N. J. Mcmillan, C. Mcmanus, L. Collins, J. L. G. Jr, F. C. Delucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24(6), 1125–1141 (2009).
[Crossref]

Meisel, S.

S. Pahlow, S. Meisel, D. Cialla-May, K. Weber, P. Rösch, and J. Popp, “Isolation and identification of bacteria by means of Raman spectroscopy,” Adv. Drug Deliv. Rev. 89, 105–120 (2015).
[Crossref] [PubMed]

Melikechi, N.

D. Pokrajac, T. Vance, A. Lazarević, A. Marcano, Y. Markushin, N. Melikechi, and N. Reljin, “Performance of multilayer perceptrons for classification of LIBS protein spectra,” inSymposium on Neural Network Applications in Electrical Engineering (IEEE, 2010), pp. 171–174.
[Crossref]

Mercier, C.

V. Chalansonnet, C. Mercier, S. Orenga, and C. Gilbert, “Identification of Enterococcus faecalis enzymes with azoreductases and/or nitroreductase activity,” BMC Microbiol. 17(1), 126 (2017).
[Crossref] [PubMed]

Miziolek, A. W.

R. S. Harmon, J. Remus, N. J. Mcmillan, C. Mcmanus, L. Collins, J. L. G. Jr, F. C. Delucia, and A. W. Miziolek, “LIBS analysis of geomaterials: Geochemical fingerprinting for the rapid analysis and discrimination of minerals,” Appl. Geochem. 24(6), 1125–1141 (2009).
[Crossref]

Mohaidat, Q. I.

Mühlbauer, M.

J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
[Crossref] [PubMed]

Multari, R. A.

Munajat, Y.

Z. Haider, Y. Munajat, R. K. R. Ibrahim, and M. Rashid, “Identification of materials through SVM classification of their LIBS spectra,” Jurnal. Teknologi. 62(3), 123–127 (2013).
[Crossref]

Myakalwar, A. K.

A. K. Myakalwar, N. Spegazzini, C. Zhang, S. K. Anubham, R. R. Dasari, I. Barman, and M. K. Gundawar, “Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection,” Sci. Rep. 5, 13169 (2015).

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. Kumar Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84(6), 2686–2694 (2012).
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J. L. Nagel, A. M. Huang, A. Kunapuli, T. N. Gandhi, L. L. Washer, J. Lassiter, T. Patel, and D. W. Newton, “Impact of Antimicrobial Stewardship Intervention on Coagulase-Negative Staphylococcus Blood Cultures in Conjunction with Rapid Diagnostic Testing,” J. Clin. Microbiol. 52(8), 2849–2854 (2014).
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Niu, G.

L. Sheng, T. Zhang, G. Niu, K. Wang, H. Tang, Y. Duan, and H. Li, “Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF),” J. Anal. At. Spectrom. 30(2), 453–458 (2015).
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D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

Novotný, K.

D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

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R. Dietrich, M. Opper, and H. Sompolinsky, “Statistical mechanics of support vector networks,” Phys. Rev. Lett. 82(14), 2975–2978 (1999).
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V. Chalansonnet, C. Mercier, S. Orenga, and C. Gilbert, “Identification of Enterococcus faecalis enzymes with azoreductases and/or nitroreductase activity,” BMC Microbiol. 17(1), 126 (2017).
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J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemometr. 26(5), 143–149 (2012).
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Palleschi, V.

E. Tognoni, G. Cristoforetti, S. Legnaioli, and V. Palleschi, “Calibration-Free Laser-Induced Breakdown Spectroscopy: State of the art,” Spectrochim. Acta B At. Spectrasc. 65, 1–14 (2010).

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K. A. Bauer, J. E. West, J. M. Balada-Llasat, P. Pancholi, K. B. Stevenson, and D. A. Goff, “An antimicrobial stewardship program’s impact with rapid polymerase chain reaction methicillin-resistant Staphylococcus aureus/S. aureus blood culture test in patients with S. aureus bacteremia,” Clin. Infect. Dis. 51(9), 1074–1080 (2010).
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J. L. Nagel, A. M. Huang, A. Kunapuli, T. N. Gandhi, L. L. Washer, J. Lassiter, T. Patel, and D. W. Newton, “Impact of Antimicrobial Stewardship Intervention on Coagulase-Negative Staphylococcus Blood Cultures in Conjunction with Rapid Diagnostic Testing,” J. Clin. Microbiol. 52(8), 2849–2854 (2014).
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Perry, J. D.

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D. Pokrajac, T. Vance, A. Lazarević, A. Marcano, Y. Markushin, N. Melikechi, and N. Reljin, “Performance of multilayer perceptrons for classification of LIBS protein spectra,” inSymposium on Neural Network Applications in Electrical Engineering (IEEE, 2010), pp. 171–174.
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S. Pahlow, S. Meisel, D. Cialla-May, K. Weber, P. Rösch, and J. Popp, “Isolation and identification of bacteria by means of Raman spectroscopy,” Adv. Drug Deliv. Rev. 89, 105–120 (2015).
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D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

Prochazka, D.

D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

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D. Prochazka, M. Mazura, O. Samek, K. Rebrošová, P. Pořízka, J. Klus, P. Prochazková, J. Novotný, K. Novotný, and J. Kaiser, “Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria,” Spectrochim. Acta B At. Spectrosc. 139, 6 (2017).

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Rehse, S. J.

Reljin, N.

D. Pokrajac, T. Vance, A. Lazarević, A. Marcano, Y. Markushin, N. Melikechi, and N. Reljin, “Performance of multilayer perceptrons for classification of LIBS protein spectra,” inSymposium on Neural Network Applications in Electrical Engineering (IEEE, 2010), pp. 171–174.
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J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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Rösch, P.

S. Pahlow, S. Meisel, D. Cialla-May, K. Weber, P. Rösch, and J. Popp, “Isolation and identification of bacteria by means of Raman spectroscopy,” Adv. Drug Deliv. Rev. 89, 105–120 (2015).
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H. Zokaeifar, J. L. Balcázar, M. S. Kamarudin, K. Sijam, A. Arshad, and C. R. Saad, “Selection and identification of non-pathogenic bacteria isolated from fermented pickles with antagonistic properties against two shrimp pathogens,” J. Antibiot.  65(6), 289–294 (2012).
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Sheng, L.

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E. Vors, K. Tchepidjian, and J. B. Sirven, “Evaluation and optimization of the robustness of a multivariate analysis methodology for identification of alloys by laser induced breakdown spectroscopy,” Spectrochim. Acta B At. Spectrosc. 117, 16–22 (2016).
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J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemometr. 26(5), 143–149 (2012).
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K. A. Bauer, J. E. West, J. M. Balada-Llasat, P. Pancholi, K. B. Stevenson, and D. A. Goff, “An antimicrobial stewardship program’s impact with rapid polymerase chain reaction methicillin-resistant Staphylococcus aureus/S. aureus blood culture test in patients with S. aureus bacteremia,” Clin. Infect. Dis. 51(9), 1074–1080 (2010).
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Tewari, S. P.

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J. C. Arthur, E. Perez-Chanona, M. Mühlbauer, S. Tomkovich, J. M. Uronis, T. J. Fan, B. J. Campbell, T. Abujamel, B. Dogan, A. B. Rogers, J. M. Rhodes, A. Stintzi, K. W. Simpson, J. J. Hansen, T. O. Keku, A. A. Fodor, and C. Jobin, “Intestinal inflammation targets cancer-inducing activity of the microbiota,” Science 338(6103), 120–123 (2012).
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Adv. Drug Deliv. Rev. (1)

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Anal. Chem. (1)

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Chem. Soc. Rev. (1)

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Figures (7)

Fig. 1
Fig. 1 Schematics of the LIBS experimental setup.
Fig. 2
Fig. 2 LIBS spectra of 6 kinds of bacteria after preprocessing and the empty slides.
Fig. 3
Fig. 3 The variance described by each principal component.
Fig. 4
Fig. 4 Importance weights and related elements of the most important 20 lines evaluated by IW-PCA.
Fig. 5
Fig. 5 Importance weights and related elements of the most important 20 lines evaluated by Random Forests.
Fig. 6
Fig. 6 CCR according to the number of feature lines (Extracted by IW-PCA and RF, respectively) used in SVM model.
Fig. 7
Fig. 7 CCR according to the number of feature lines (Extracted by both IW-PCA and RF) used in SVM model.

Tables (5)

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Table 1 The 85 selected lines and corresponding elements

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Table 2 The most important 20 feature lines evaluated by IW-PCA.

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Table 3 The most important 20 feature lines evaluated by Random Forests.

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Table 4 Comparing of differences between the most important 20 lines evaluated by IW-PCA and Random Forests.

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Table 5 CCRs of SVM using feature lines extracted by IW-PCA and RF.

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

lim N (1 1 N ) N = 1 e 0.368
Gini(D)= k=1 K p k (1 p k )=1 k=1 K p k 2
Gini(D)=1 k=1 K ( | C k | | D | ) 2
Gini(D,A)= | D 1 | | D | Gini( D 1 )+ | D 2 | | D | Gini( D 2 )
Δ j =Gini(D)Gini(D,A)
wx+b=0
γ= y i (w x i +b) w

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