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Fermentation
Special Issues
From Traditional to Machine Learning: How Computers Can Improve the...
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Special Issue "From Traditional to Machine Learning: How Computers Can Improve the Quality of Rudimentary Fermented Products and Learn from Reviews"

A special issue of Fermentation (ISSN 2311-5637). This special issue belongs to the section "Fermentation Process Design".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2560

Special Issue Editor

Dr. Bernard Chen

E-Mail Website
Guest Editor
Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA
Interests: wineinformatics; data science; natural language processing; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fermentation is a natural metabolic process that has been used to produce foodstuffs and beverages for thousands of years, in which an organism converts carbohydrate into alcohol and/or acid. During fermentation, yeast produces a range of flavoring compounds that can be utilized to create fermented foods and beverages, such as wine, beer, yoghurt, miso, kimchi, etc. Nowadays, to improve the aroma and flavor of fermented products, experiments with different recipes and industrial quality-control parameters need to be carried out and recorded in various formats, including numerical, categorical, machine readable, and human language. The large amount of experimental data can be utilized as input data for machine learning algorithms to suggest the optimal quality-control setups. Reviews of the final products can be analyzed through computational models to understand the key components that form the quality products.

This Special Issue aims to discover how computers can help us develop high-quality rudimentary fermented products.

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 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.

Keywords

  • machine learning
  • data science
  • wine fermentation
  • fermentation products
  • fermentation management
  • environmental parameters
  • aroma
  • optimal control strategies

Published Papers (2 papers)

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Research

23 pages, 2989 KiB  
Article
Applying Neural Networks in Wineinformatics with the New Computational Wine Wheel
by
Long Le
,
Pedro Navarrete Hurtado
,
Ian Lawrence
,
Qiuyun Tian
and
Bernard Chen
Fermentation 2023, 9(7), 629; https://doi.org/10.3390/fermentation9070629 - 01 Jul 2023
Viewed by 686
Abstract
Wineinformatics involves the application of data science techniques to wine-related datasets generated during the grape growing, wine production, and wine evaluation processes. Its aim is to extract valuable insights that can benefit wine producers, distributors, and consumers. This study highlights the potential of [...] Read more.
Wineinformatics involves the application of data science techniques to wine-related datasets generated during the grape growing, wine production, and wine evaluation processes. Its aim is to extract valuable insights that can benefit wine producers, distributors, and consumers. This study highlights the potential of neural networks as the most effective black-box classification algorithm in wineinformatics for analyzing wine reviews processed by the Computational Wine Wheel (CWW). Additionally, the paper provides a detailed overview of the enhancements made to the CWW and presents a thorough comparison between the latest version and its predecessors. In comparison to the highest accuracy results obtained in the latest research work utilizing an elite Bordeaux dataset, which achieved approximately 75% accuracy for Robert Parker’s reviews and 78% accuracy for the Wine Spectator’s reviews, the combination of neural networks and CWW3.0 consistently yields improved performance. Specifically, this combination achieves an accuracy of 82% for Robert Parker’s reviews and 86% for the Wine Spectator’s reviews on the elite Bordeaux dataset as well as a newly created dataset that contains more than 10,000 wines. The adoption of machine learning algorithms for wine reviews helps researchers understand more about quality wines by analyzing the end product and deconstructing the sensory attributes of the wine; this process is similar to reverse engineering in the context of wine to study and improve the winemaking techniques employed. Full article
(This article belongs to the Special Issue From Traditional to Machine Learning: How Computers Can Improve the Quality of Rudimentary Fermented Products and Learn from Reviews)
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17 pages, 14991 KiB  
Article
A Deep Learning Approach to Optimize Recombinant Protein Production in Escherichia coli Fermentations
by
Domenico Bonanni
,
Mattia Litrico
,
Waqar Ahmed
,
Pietro Morerio
,
Tiziano Cazzorla
,
Elisa Spaccapaniccia
,
Franca Cattani
,
Marcello Allegretti
,
Andrea Rosario Beccari
,
Alessio Del Bue
and
Franck Martin
Fermentation 2023, 9(6), 503; https://doi.org/10.3390/fermentation9060503 - 24 May 2023
Viewed by 1468
Abstract
Fermentation is a widely used process in the biotechnology industry, in which sugar-based substrates are transformed into a new product through chemical reactions carried out by microorganisms. Fermentation yields depend heavily on critical process parameter (CPP) values which need to be finely tuned [...] Read more.
Fermentation is a widely used process in the biotechnology industry, in which sugar-based substrates are transformed into a new product through chemical reactions carried out by microorganisms. Fermentation yields depend heavily on critical process parameter (CPP) values which need to be finely tuned throughout the process; this is usually performed by a biotech production expert relying on empirical rules and personal experience. Although developing a mathematical model to analytically describe how yields depend on CPP values is too challenging because the process involves living organisms, we demonstrate the benefits that can be reaped by using a black-box machine learning (ML) approach based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks to predict real time OD600nm values from fermentation CPP time series. We tested both networks on an E. coli fermentation process (upstream) optimized to obtain inclusion bodies whose purification (downstream) in a later stage will yield a targeted neurotrophin recombinant protein. We achieved root mean squared error (RMSE) and relative error on final yield (REFY) performances which demonstrate that RNN and LSTM are indeed promising approaches for real-time, in-line process yield estimation, paving the way for machine learning-based fermentation process control algorithms. Full article
(This article belongs to the Special Issue From Traditional to Machine Learning: How Computers Can Improve the Quality of Rudimentary Fermented Products and Learn from Reviews)
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