Preliminary evaluation of infrared spectroscopy for the differentiation of Brettanomyces bruxellensis strains isolated from red wines

Oelofse, A.; Malherbe, Sulette; Pretorius, Isak S.; Toit, Maret du

doi:10.1016/j.ijfoodmicro.2010.08.004

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…The statistical model can be almost completely described by means of the first two PCs, which accounted for 99% of the total variance. The PCA clearly shows that YCH can be classified based on genus, which is consistent with previous studies showing that FTIR spectra of yeast cells can be used to discriminate yeasts strains and species (Kümmerle et al ., 1998; Oelofse et al ., 2010; Niu et al ., 2018).…”

Section: Resultsmentioning

confidence: 99%

Use of FT‐NIR and XPS techniques to distinguish cell hull fractions prepared by autolysis or HPH from Saccharomyces cerevisiae and Brettanomyces bruxellensis strains

Bakhos

Skaff

Esvan

et al. 2021

Int J of Food Sci Tech

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Yeast hulls can be used to adsorb undesirable compounds such as volatile phenols that may be present in wine. To understand this adsorption process, the properties of the cell walls and their chemical composition need to be better understood. A study was conducted using four different yeast fractions of either autolysed or high-pressure homogeniser (HPH)-crushed yeast biomasses of Saccharomyces cerevisiae and Brettanomyces bruxellensis. Near-infrared spectroscopy (FT-NIR) coupled with X-ray photoelectron spectroscopy (XPS) was used and analysed by principal component analysis (PCA). The FT-NIR spectral region of Saccharomyces and Brettanomyces statistically analysed by PCA showed a clear discrimination accounting for 76% of the variation in the data for PC1; moreover, yeast hulls prepared from the same strain and subjected to two different treatments were also separated. These methods classify yeast cell hulls (YCH) according to strain, composition and treatment applied. Our results indicate that yeast hulls obtained by autolysis are less rich in proteins than those resulting from HPH treatment due to the high pressure that releases more proteins and exposes them on the surface of the cell wall. The composition of YCH at the extreme surface is similar to that found deeper in the wall.

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Section: Resultsmentioning

confidence: 99%

Use of FT‐NIR and XPS techniques to distinguish cell hull fractions prepared by autolysis or HPH from Saccharomyces cerevisiae and Brettanomyces bruxellensis strains

Bakhos

Skaff

Esvan

et al. 2021

Int J of Food Sci Tech

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show abstract

“…Furthermore, in most of the studies dedicated to the differentiation of microorganisms, only specic regions of the spectra are used because they are characteristic of these species such as the spectral window 900-1200 cm À1 corresponding mainly to the polysaccharides. 14,36 We investigated if using one specic spectral window the differentiation between spectra of FR and LY cells from one medium could be achieved. Once again, it was impossible to statistically discriminate FR from LY cells (results not shown).…”

Section: Pca Processing Of Cells From Different Sampling Methodsmentioning

confidence: 99%

FTIR spectroscopic metabolome analysis of lyophilized and fresh Saccharomyces cerevisiae yeast cells

et al. 2014

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The yeast Saccharomyces cerevisiae is widely used as a biological eukaryotic model and also serves as a production organism in biotechnology. One of the methods used to avoid degradation of the yeast cell content is lyophilization. The use of lyophilized yeast cells has several advantages over fresh ones: samples can be easily transported and/or stored and variations of their metabolomic profiles do not occur during transport or storage. Fourier transform infrared (FTIR) spectroscopy is one of the most emerging approaches in modern biology that permits operation on very small quantities of whole cells without the need for extractions or purifications. This technique is very sensitive and not only allows the discrimination between different cell genotypes but also between different growth conditions. FTIR spectra provide interesting data on the metabolic status of the whole cell. Modern multivariate data processing was applied to analyse live fresh or lyophilized S. cerevisiae cells from different growth media.This study clearly demonstrates that yeast cells coming from an identical biological medium can be used indiscriminately for FTIR analysis whether they are analysed directly as live fresh cells or after lyophilization which is a freeze-drying process. Moreover, FTIR data obtained using lyophilized cells showed less variability.

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“…All of the studies described molecular methods, except one, which used Fourier Transform Infrared Attenuated Total Reflectance (FTIR-ATR) spectroscopy to analyze the molecular composition of cells with mid-infrared radiation for B. bruxellensis strain discrimination. Despite being rapid and not requiring DNA extraction, this method cannot be applied routinely due to the need to standardize the protocol and access a database specific to the target microorganism [17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Genetic Groups within Brettanomyces bruxellensis through Cell Morphology Using a Deep Learning Tool

Lebleux

Denimal

Oliveira

et al. 2021

JoF

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Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly strain-dependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems.

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Preliminary evaluation of infrared spectroscopy for the differentiation of Brettanomyces bruxellensis strains isolated from red wines

Cited by 14 publications

References 32 publications

Use of FT‐NIR and XPS techniques to distinguish cell hull fractions prepared by autolysis or HPH from Saccharomyces cerevisiae and Brettanomyces bruxellensis strains

Use of FT‐NIR and XPS techniques to distinguish cell hull fractions prepared by autolysis or HPH from Saccharomyces cerevisiae and Brettanomyces bruxellensis strains

FTIR spectroscopic metabolome analysis of lyophilized and fresh Saccharomyces cerevisiae yeast cells

Prediction of Genetic Groups within Brettanomyces bruxellensis through Cell Morphology Using a Deep Learning Tool

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