Machine Learning in Fermented Food and Beverages
Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 13424
Interests: wineinformatics; data science; natural language processing; bioinformatics
Special Issues, Collections and Topics in MDPI journals
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
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 submissions that pass pre-check are 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 monthly 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 2600 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