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

TLDR: 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.

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Table 1 Identities of the 32 data sets used to construct a spectral library composed of 669 bacterial LIBS spectra.

Table 2 The twenty-four independent variables used in ratio model one (RM1).

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Table 5 Truth table results for two multivariate techniques (DFA and PLS-DA) utilized in a genus-level classification of bacterial LIBS spectra.

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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. 

Q2. What have the authors stated for future works in "A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification" ?

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