Dot blot and PCR for Brettanomyces bruxellensis detection in red wine

Cecchini, Francesca; Iacumin, Lucilla; Fontanot, Marco; Comuzzo, Piergiorgio; Comi, Giuseppe; Manzano, Marisa

doi:10.1016/j.foodcont.2013.04.013

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…In order to combat B. bruxellensis spoilage, several methods have been described in order to detect these yeast cells, to further control this yeast early on in the fermentation (Stender et al, 2001;Cecchini et al, 2013). With this in mind, other authors report the benefits of this "Brett" character in certain wine styles, as the influence of minimal amounts of these aromas may enhance the complexity as well as impart a mature character on younger wines (Fugelsang, 1997).…”

Section: The Ecological Niches Associated With B Bruxellensismentioning

confidence: 99%

Brettanomyces bruxellensis, a survivalist prepared for the wine apocalypse and other beverages

Smith

Divol

2016

Food Microbiology

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Section: The Ecological Niches Associated With B Bruxellensismentioning

confidence: 99%

Brettanomyces bruxellensis, a survivalist prepared for the wine apocalypse and other beverages

Smith

Divol

2016

Food Microbiology

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“…In addition to PCR and qPCR methods, other approaches targeted B. bruxellensis in wine samples, such as fluorescence in situ hybridization (Stender et al 2001;Röder et al 2007), dot blot hybridization (Cecchini et al 2013) and the use of biosensors (Cecchini et al 2012;Manzano et al 2016). Finally, several spectroscopy methods, such as Raman (Rodriguez et al 2013) and Fourier transform mid-infrared (FTMIR) (Oelofse et al 2010), in combination with chemiometrics, have been devised to identify B. bruxellensis, although no applications in wine have been carried out so far.…”

Section: Methods To Detect B Bruxellensis In Winemakingmentioning

confidence: 99%

Brettanomyces bruxellensis yeasts: impact on wine and winemaking

Agnolucci

Tirelli

Cocolin

et al. 2017

World J Microbiol Biotechnol

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Yeasts belonging to the Brettanomyces/Dekkera genus are non-conventional yeasts, which affect winemaking by causing wine spoilage all over the world. This mini-review focuses on recent results concerning the presence of Brettanomyces bruxellensis throughout the wine processing chain. Here, culture-dependent and independent methods to detect this yeast on grapes and at the very early stage of wine production are encompassed. Chemical, physical and biological tools, devised for the prevention and control of such a detrimental species during winemaking are also presented. Finally, the mini-review identifies future research areas relevant to the improvement of wine safety and sensory profiles.

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“…To date, though more than 2,460 different species are presented in The Ribosomal Database Project stores (Maidak et al, 2000), most microbes in the environment have not been described and accessed for biotechnology. Few viable bacteria can be cultivated on artificial media (Kimura and Nobutada, 2006;Cecchini et al, 2012). Furthermore, traditional microbial culture highly relies on culture conditions and has limitations in the analysis of microbial community composition.…”

Section: Development Of Forensic Microbiologymentioning

confidence: 99%

Trends in forensic microbiology: From classical methods to deep learning

Yuan

Wang

et al. 2023

Front. Microbiol.

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Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

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Dot blot and PCR for Brettanomyces bruxellensis detection in red wine

Cited by 13 publications

References 30 publications

Brettanomyces bruxellensis, a survivalist prepared for the wine apocalypse and other beverages

Brettanomyces bruxellensis, a survivalist prepared for the wine apocalypse and other beverages

Brettanomyces bruxellensis yeasts: impact on wine and winemaking

Trends in forensic microbiology: From classical methods to deep learning

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