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Journal ArticleDOI

A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification

TL;DR: The partial least squares discriminant analysis was more effective at distinguishing between highly similar spectra from closely related bacterial genera and may be the preferred multivariate technique in future species-level or strain-level classifications.
About: This article is published in Spectrochimica Acta Part B: Atomic Spectroscopy.The article was published on 2013-09-01 and is currently open access. It has received 38 citations till now. The article focuses on the topics: Discriminant function analysis & Partial least squares regression.

Summary (2 min read)

1. Introduction

  • Since the initial demonstrations of bacterial identification with laser-induced breakdown spectroscopy (LIBS) in 2003, significant progress has been made in the use of multivariate chemometric analyses to classify unknown bacterial LIBS spectra.[1-4].
  • Over the last five years the authors and others have demonstrated a sensitive and specific identification of live bacterial biospecimens utilizing a discriminant function analysis (DFA) to classify LIBS spectra.[5-8].
  • The intensities of strong specific elemental atomic emission lines normalized by the total observed spectral power have been utilized as independent variables in this multivariate analysis. [9].
  • And this is an ongoing area of investigation.
  • Model performance was quantified by calculating truth tables (and the resulting sensitivity and specificity) from the external validation tests.

2.1. Experimental Setup

  • The LIBS apparatus used to obtain the bacterial spectra, as well as their bacterial sample preparation and mounting protocols, have been described at length elsewhere.
  • Five spectra were acquired at each sampling location, thus twenty-five laser pulses were used to obtain this spectrum.
  • The bacteria were chosen to represent a fairly wide taxonomic range.
  • The 32 distinct experiments that were performed yielded the 32 data sets shown in column three of Table 1.
  • No data “outliers” were omitted from their data sets and efforts were made to maximize the number of spectra from any one bacterial deposition rather than to standardize the number of spectra taken.

2.2 Models for Chemometric Analysis (Lines, RM1, and RM2)

  • The three independent variable models that were tested are referred to here as the “lines” model, ratio model one (RM1), and ratio model two (RM2).
  • The lines model was the simplest of the three, having been used in all their previous work.
  • This approach has been utilized with success by Gottfried et al. to discriminate LIBS spectra obtained from explosives residues.
  • The first thirteen variables were merely the intensities of the thirteen strong emission lines used in the lines model (indicated by an asterisk).
  • It was decided that when the dimensionality of the original data was not reduced significantly then the benefits of performing a down-selection were reduced and the more appropriate model would be to use the entire spectrum.

2.3 Chemometric Analysis Techniques

  • Two multivariate chemometric analysis techniques were compared for discrimination between different bacterial genera based on the LIBS emission spectra.
  • This is known as external validation, because each spectrum was tested against a library where no other spectra acquired at the same time or under the same conditions were present.
  • PLS-DA takes a set of independent variables as determined by their models and constructs latent variables to maximize the variance between the two groups.
  • The identity of unknown spectra was then predicted based on this discrimination line in the pre-compiled library.
  • All unknown samples were classified in a PLS-DA test specific for each genus, and if the test group was classified as belonging to the “no group” for each model, it remained unknown and was not classified as belonging to any genus.

3. Results and Discussion

  • In each of the DFA results, four discriminant functions (DF1 through DF4) were constructed to determine the classification of each spectrum.
  • The “unknown” bacterial spectra are represented by the “x” symbols and 34 of 34 unknown spectra were correctly classified as Mycobacterium, even though the model contained no other spectra from strain TA.
  • An investigation of the PLS-DA was conducted to compare the number of LV’s and the corresponding rates of true positives and true negatives.

4. Discussion

  • A comparison of the DFA performed with the three different models consisting of lines, RM1, and RM2 showed that RM2 yielded the overall highest true positive and true negative rates with true positive rates of 95%, 54%, 95%, and 88% for the four genera and true negative rates of 91%, 99%, 99%, and 99%.
  • The sensitivity and specificity were obtained by averaging the results from the 31 tests and the standard deviation is reported as the uncertainty.
  • This was merely a result of there being only two representative Staphylococci data sets to include in the analysis, as can be seen in Table 1, with one of these data sets being among the earliest experiments performed in the construction of the spectral library.
  • Therefore both analyses can perform both functions, if necessary.
  • It may therefore be true that a DFA is more effective in genus-level discrimination on bacterial specimens with a wide range of potential identities, but discrimination at the species- or strain-level once the genus is accurately identified may require the use of PLS-DA.

5. Conclusion

  • The authors have shown that a sensitive and specific genus level classification of LIBS spectra from live bacterial specimens can be performed with a DFA or a PLS-DA using several different independent variable models.
  • All results were obtained using external-validation tests.
  • The number of latent variables required for efficient classification using this model was investigated, and chosen to be 20 in all subsequent tests.
  • More precise identification at the species-level or strain-level may be subsequently performed with a PLS-DA, which demonstrated improved performance at discriminating highly similar spectra.
  • It is likely 18 that computational processing power would easily allow such a verification, as the classification of one unknown spectrum against a pre-compiled library model is performed rapidly by both techniques.

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Citations
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Journal ArticleDOI
TL;DR: This review describes and compares the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures, and presents examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world.
Abstract: Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure.

314 citations


Cites methods from "A comparison of multivariate analys..."

  • ...Such model can be used to provide predictions for a new (‘unknown’) object based on the values of measured variables in that object (Putnam et al. 2013)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the authors highlight the work of many LIBS researchers who have developed, adapted, and improved upon sample preparation techniques for various specimen types in order to improve the quality of the analytical data that LIBS can produce in a large number of research domains.

161 citations

Journal ArticleDOI
TL;DR: This work critically assess and elaborate on the approaches to utilize PCA in LIBS data processing, and derives some implications and suggests advice in data preprocessing, visualization, dimensionality reduction, model building, classification, quantification and non-conventional multivariate mapping.

143 citations

Journal ArticleDOI
TL;DR: This review attempts to give a critical overview of the diverse progress of the field, focusing on the results of the last five years, of laser-induced breakdown spectroscopy.
Abstract: Laser-induced breakdown spectroscopy (LIBS) has become an established analytical atomic spectrometry technique and is valued for its very compelling set of advantageous analytical and technical characteristics. It is a rapid, versatile, non-contact technique, which is capable of providing qualitative and quantitative analytical information for practically any sample, in a virtually non-destructive way, without any substantial sample preparation. The instrumentation is simple, robust, compact, and even enables remote analysis. This review attempts to give a critical overview of the diverse progress of the field, focusing on the results of the last five years. The advancement of LIBS instrumentation and data evaluation is discussed in detail and selected results of some prominent applications are also described.

140 citations


Cites background from "A comparison of multivariate analys..."

  • ...Successful discrimination of pathogenic from non-pathogenic bacteria has been achieved, including some multi-drug-resistant strains of bacteria including Staphylococcus aureus and other strains causing hospital-acquired infections (HAI) [112, 132]....

    [...]

Journal ArticleDOI
TL;DR: Laser-Induced Breakdown Spectroscopy is in a stage of great vitality as an analytical technique, with new research emerging trends likely to play an important role in the future development of the technique as well as in its penetration in the medical field.

98 citations

References
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Journal ArticleDOI
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

37,124 citations

01 Jan 2009

10,876 citations


"A comparison of multivariate analys..." refers methods in this paper

  • ...DFA is a multivariate analysis technique that uses independent variables (atomic emission intensities) to calculate a dependant variable (bacterial identity) to classify or discriminate between two or more groups [21]....

    [...]

Book
01 Oct 2010
TL;DR: Partial least squares (PLS) was not originally designed as a tool for statistical discrimination as discussed by the authors, but applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role.
Abstract: Partial least squares (PLS) was not originally designed as a tool for statistical discrimination. In spite of this, applied scientists routinely use PLS for classification and there is substantial empirical evidence to suggest that it performs well in that role. The interesting question is: why can a procedure that is principally designed for overdetermined regression problems locate and emphasize group structure? Using PLS in this manner has heurestic support owing to the relationship between PLS and canonical correlation analysis (CCA) and the relationship, in turn, between CCA and linear discriminant analysis (LDA). This paper replaces the heuristics with a formal statistical explanation. As a consequence, it will become clear that PLS is to be preferred over PCA when discrimination is the goal and dimension reduction is needed. Copyright © 2003 John Wiley & Sons, Ltd.

2,067 citations


"A comparison of multivariate analys..." refers background in this paper

  • ...The PLS-DA then calculates a discrimination line (or this can be user-determined) to predict the class of each spectrum based on Bayesian statistics by minimizing the number of false positives and negatives [22]....

    [...]

Journal ArticleDOI
TL;DR: This review discusses the application of laser-induced breakdown spectroscopy (LIBS) to the problem of explosive residue detection and demonstrates the tremendous potential of LIBS for real-time detection of explosives residues at standoff distances.
Abstract: In this review we discuss the application of laser-induced breakdown spectroscopy (LIBS) to the problem of detection of residues of explosives. Research in this area presented in open literature is reviewed. Both laboratory and field-tested standoff LIBS instruments have been used to detect explosive materials. Recent advances in instrumentation and data analysis techniques are discussed, including the use of double-pulse LIBS to reduce air entrainment in the analytical plasma and the application of advanced chemometric techniques such as partial least-squares discriminant analysis to discriminate between residues of explosives and non-explosives on various surfaces. A number of challenges associated with detection of explosives residues using LIBS have been identified, along with their possible solutions. Several groups have investigated methods for improving the sensitivity and selectivity of LIBS for detection of explosives, including the use of femtosecond-pulse lasers, supplemental enhancement of the laser-induced plasma emission, and complementary orthogonal techniques. Despite the associated challenges, researchers have demonstrated the tremendous potential of LIBS for real-time detection of explosives residues at standoff distances.

290 citations

Journal ArticleDOI
TL;DR: LIBS data from the individual laser shots were analyzed by principal-components analysis and were found to contain adequate information to afford discrimination among the different biomaterials.
Abstract: Laser-induced breakdown spectroscopy (LIBS) has been used to study bacterial spores, molds, pollens, and proteins. Biosamples were prepared and deposited onto porous silver substrates. LIBS data from the individual laser shots were analyzed by principal-components analysis and were found to contain adequate information to afford discrimination among the different biomaterials. Additional discrimination within the three bacilli studied appears feasible.

217 citations

Frequently Asked Questions (2)
Q1. What are the contributions in "A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification" ?

In this paper, the authors compared the use of three different down-selected variable models consisting of emission intensities, the sum of observed ∼4 intensities from the elements P, Ca, Mg, Na, C, and complex ratios of those intensities. 

Such a confirmation will need to be investigated in future work.