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Special Issue "Machine Learning in Fermented Food and Beverages"

A special issue of Fermentation (ISSN 2311-5637). This special issue belongs to the section "Fermentation for Food and Beverages".

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editor

Prof. Dr. Bernard Chen
E-Mail Website
Guest Editor
Department of Computer Science, University of Central Arkansas, Conway, AR, USA
Interests: wineinformatics; coffee-informatics; data science; natural language processing

Special Issue Information

Dear Colleagues,

Data science is the advancement in the combination of data engineering, scientific methods, math, visualization, and statistically based algorithms with a domain of application to make sense of larger quantities of data. Data science has become one of the most popular research areas in the 21st century due to the availability of data from research and the Internet. Within this popular field, there are four major types of machine learning algorithms that provide efficacy: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. All these methods provide useful and distinct information to the domain knowledge with a large amount of data.

Fermentation is a natural metabolic process utilized by humans to produce foodstuffs and beverages for thousands of years. Under the biochemical scope, fermentation is a process of metabolism where an organism converts carbohydrate into alcohol and/or acid. During fermentation, yeast produces a whole range of flavoring compounds utilized by humans to create fermented foods and beverages, such as wine, beer, yoghurt, miso, kimchi, etc. In order to improve the aroma and flavor quality of the fermented products, experiments with different recipes and components need to be carried out and recorded in various types of formats, including numerical, categorical, machine readable, and human language. Novel or hidden knowledge in fermented products has the potential to be discovered by applying machine learning algorithms on a large amount of experimental data.

This Special Issue calls for reviews and original data science research articles that adopt fermented food and beverages as the domain knowledge to discover useful information through machine learning algorithms.

Prof. Dr. Bernard Chen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fermentation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Fermented food and beverages
  • Data science
  • Machine learning
  • Deep learning
  • Aroma and flavor
  • Natural language processing
  • Wine informatics

Published Papers (1 paper)

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Complementing Digital Image Analysis and Laser Distance Meter in Beer Foam Stability Determination
Fermentation 2021, 7(3), 113; https://doi.org/10.3390/fermentation7030113 - 14 Jul 2021
Viewed by 451
The aim of this research is to investigate the possibility of applying a laser distance meter (LDM) as a complementary measurement method to image analysis during beer foam stability monitoring. The basic optical property of foam, i.e., its high reflectivity, is the main [...] Read more.
The aim of this research is to investigate the possibility of applying a laser distance meter (LDM) as a complementary measurement method to image analysis during beer foam stability monitoring. The basic optical property of foam, i.e., its high reflectivity, is the main reason for using LDM. LDM measurements provide relatively precise information on foam height, even in the presence of lacing, and provide information as to when foam is no longer visible on the surface of the beer. Sixteen different commercially available lager beers were subjected to analysis. A camera and LDM display recorded the foam behavior; the LDM display which was placed close to the monitored beer glass. Measurements obtained by the image analysis of videos provided by the visual camera were comparable to those obtained independently by LDM. However, due to lacing, image analysis could not accurately detect foam disappearance. On the other hand, LDM measurements accurately detected the moment of foam disappearance since the measurements would have significantly higher values due to multiple reflections in the glass. Full article
(This article belongs to the Special Issue Machine Learning in Fermented Food and Beverages)
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