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.

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Application of multivariate statistical techniques in microbial ecology.

Pathogenesis of acute hepatopancreatic necrosis disease (AHPND) in shrimp.

A fast and reliable detection and identification of microorganisms is crucial in environmental science, for food quality as well as medical diagnosis. In these fields, all types of Raman spectroscopy are gaining more and more importance during the last years. The review provides an extensive overview of recent research, technical expertise, and scientific findings based on Raman spectroscopic detection and identification of microorganisms within the years 2010 and 2015, demonstrating the diverse capability of Raman spectroscopy as a modern analytical tool. Raman spectroscopy distinguishes itself from other currently applied techniques by its easy application at low cost, its high speed of analysis, and its broad information content on both the chemical composition and the structure of biomolecules within the microorganisms. Slight chances in the chemical composition of microorganisms can be monitored by means of Raman spectroscopy and used to differentiate genera, species, or even strains. Detection of pathogens is possible from complex matrices, such as soil, food, and body fluids. Further, spectroscopic studies of host–pathogen interactions are addressed as well as the effect of antibiotics on bacteria. Copyright © 2015 John Wiley & Sons, Ltd.

The application of Raman spectroscopy for the detection and identification of microorganisms

Sample treatment and preparation for laser-induced breakdown spectroscopy

On the utilization of principal component analysis in laser-induced breakdown spectroscopy data analysis, a review

The determination of bacterial identity at the strain level is still a complex and time-consuming endeavor. In this study, visible wavelength spontaneous Raman spectroscopy has been used for the discrimination of four closely related Escherichia coli strains: pathogenic enterohemorrhagic E. coli O157:H7 and non-pathogenic E. coli C, E. coli Hfr K-12, and E. coli HF4714. Raman spectra from 600 to 2000 cm−1 were analyzed with two multivariate chemometric techniques, principal component-discriminant function analysis and partial least squares-discriminant analysis, to determine optimal parameters for the discrimination of pathogenic E. coli from the non-pathogenic strains. Spectral preprocessing techniques such as smoothing with windows of various sizes and differentiation were investigated. The sensitivity and specificity of both techniques was in excess of 95%, determined by external testing of the chemometric models. This study suggests that spontaneous Raman spectroscopy with visible wavelength excitation is potentially useful for the rapid identification and classification of clinically-relevant bacteria at the strain level.

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Sensitive and specific discrimination of pathogenic and nonpathogenic Escherichia coli using Raman spectroscopy-a comparison of two multivariate analysis techniques.

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

http://absimage.aps.org/image/DAMOP13/MWS_DAMOP13-2013-000139.pdf

Russell A. Putnam

Papers

Sensitive and specific discrimination of pathogenic and nonpathogenic Escherichia coli using Raman spectroscopy-a comparison of two multivariate analysis techniques.

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

Recent advances in the use of laser-induced breakdown spectroscopy (LIBS) as a rapid point-of-care pathogen diagnostic