Combining near infrared spectroscopy and multivariate analysis as a tool to differentiate different strains of Saccharomyces cerevisiae: a metabolomic study

Abstract

Near-infrared (NIR) spectroscopy has gained wide acceptance within the food and agriculture industries as a rapid analytical tool. NIR spectroscopy offers the advantage of rapid, non-destructive analysis and routine operation is simple and opens the possibility of using spectra to obtain the 'fingerprint' of a sample. The aim of this study was to explore the potential of combining visible (VIS) and nearinfrared (NIR) spectroscopy, together with multivariate analysis, in establishing the function of genes, by investigating the metabolic profiles produced by Saccharomyces cerevisiae deletion strains sourced from the EUROSCARF yeast collection. Spectra (400-2500 nm) were acquired with a FOSS NIRSystems6500 (Foss NIRSystems), in transmittance mode. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used in order to visualize graphically the relative differences and similarities of yeast deletion strains. VIS and NIR spectroscopy showed great promise as a screening tool for both discriminating between yeast strains and grouping strains with deletions in genes that disturb similar metabolic pathways. These results indicate that the methods may be useful in defining the function of genes that produce no obvious phenotype.

Comparison of partial least squares‐discriminant analysis and soft independent modeling of class analogy methods for classification of Saccharomyces cerevisiae cells based on mid‐infrared spectroscopy

“…Similar results were obtained in studies performed by Holmes et al (2002), involving metabolomic analysis to distinguish between classes expected to show metabolic or genetic differences, for example, controls versus dosed, healthy versus diseased, male versus female, based on their biofluid, or tissue 1 H NMR spectra 31 . Studies performed previously showed the success of infrared spectroscopy associated to SIMCA methods in terms of microbiological classification, as well as suitable discrimination of deletion strains from the wild‐type laboratory strain 32–34 . The SIMCA algorithm showed to be more appropriate for this study, since it is suitable to work with a reduced number of samples per class, and there is no restriction in terms of the number of the measurement variables, which is considered as a significant feature.…”

Development of a gas chromatography/mass spectrometry based metabolomics protocol by means of statistical experimental design

“…Several techniques, including nuclear magnetic resonance spectroscopy (NMR) (Zhang et al 2008), near-infrared spectroscopy (NIR) (Cozzolino et al 2006), gas chromatography (GC) (Fiehn 2008;Jiye et al 2005), liquid chromatography (LC) (Zelena et al 2009), and capillary electrophoresis (CE) (Lapainis et al 2009), with the latter three being coupled to mass spectrometry (MS), have been applied in metabolomics. Out of these techniques, the combination of a chromatographic separation with mass spectrometric detection offers a somewhat higher sensitivity than the pure spectroscopic techniques, although sample preparation generally becomes more complicated.…”

FT-NIR spectroscopy and RP-HPLC combined with multivariate analysis reveals differences in plant cell suspension cultures of Thevetia peruviana treated with salicylic acid and methyl jasmonate

“…MVA is a powerful statistical tool that has been used in qualitative and quantitative studies to extract the required information from the NIR spectra. Conzzolino et al [ 43 ] used NIR spectroscopy combined with MVA in a metabolic study to differentiate strains of Saccharomyces cerevisiae , proving its applicability as a screening tool for both discriminating between yeast strains and grouping strains with deletions in genes that disturb similar metabolic pathways. Combination of chemometrics and visible/NIR spectroscopy was also used for red wine fermentation monitoring in a pilot scale, obtaining a correct classification of the samples, regardless of the variety or time of fermentation [ 44 ].…”