Applications of Computer Science and AI to Fermented Foods and Beverages

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 412

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Department of Computer Science and Engineering, University of Central Arkansas, Conway, AR 72034, USA
Interests: wine informatics; data science; natural language processing; bioinformatics
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Special Issue Information

Dear Colleagues,

The application of computer science and artificial intelligence (AI) is now in nearly every area of life. As one of the fastest-growing research fields of the 21st century, computer science and AI have advanced significantly, driven by the vast amounts of data available from research and the Internet.

Fermentation is a traditional metabolic process that humans have used for thousands of years to create various foods and beverages. Biochemically, fermentation involves using microorganisms like yeast to convert carbohydrates into alcohols or acids, producing a range of flavor compounds. These compounds are essential to fermented products such as wine, beer, yogurt, miso, kimchi, and more. To enhance the aroma and flavor of these products, extensive experimentation with different recipes and components is necessary. Additionally, understanding the final fermented products often requires evaluating their physicochemical and sensory characteristics from various sources. These research activities generate data in multiple formats, including numerical, categorical, machine-readable, and natural language. Applying computer science and AI to these diverse datasets has significant potential to uncover new insights and hidden knowledge in fermented products.

This Special Issue invites reviews and original research articles exploring the application of computer science and AI to fermented foods and beverages. We seek contributions that showcase how relevant techniques and algorithms can be leveraged to extract valuable information, improve product quality, and advance our understanding of fermentation processes through computer science and AI.

Dr. Bernard Chen
Guest Editor

Manuscript Submission Information

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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 2100 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

  • fermented food and beverages
  • wine
  • beer
  • aroma and flavor
  • AI
  • data science
  • machine learning
  • deep learning
  • natural language processing
  • wine informatics

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Published Papers (1 paper)

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Research

15 pages, 2618 KiB  
Article
Wineinformatics: Wine Score Prediction with Wine Price and Reviews
by Yuka Nagayoshi and Bernard Chen
Fermentation 2024, 10(12), 598; https://doi.org/10.3390/fermentation10120598 - 23 Nov 2024
Viewed by 229
Abstract
Wineinformatics is a new field that applies data science to wine-related data. The goal of this paper is to determine whether incorporating wine price can improve the accuracy of score prediction. To explore the relationship between wine price and wine score, naive Bayes [...] Read more.
Wineinformatics is a new field that applies data science to wine-related data. The goal of this paper is to determine whether incorporating wine price can improve the accuracy of score prediction. To explore the relationship between wine price and wine score, naive Bayes classifier and support vector machine (SVM) classifier are employed to predict the scores as either equal to or above 90 or below 90. The price values are normalized using four different methods: mean, median, boxplot mean, and boxplot median. To conduct a proper comparison, the original dataset from previous research, which includes a total of 14,349 wine reviews, was preprocessed by filtering all null price values, resulting in 9721 wine reviews. Using this dataset, classifiers, and normalization methods, the models with and without the price feature were compared. SVM classifier with mean normalization method (USD 50.04) achieved the best accuracy of 87.98%, while naive Bayes classifier with boxplot median normalization method (USD 28.00) showed the greatest improvement of 0.99%. From all the results, we concluded that boxplot median normalization (USD 28.00) is the most effective method in this study. These results indicate that incorporating price as an attribute enhances machine learning algorithms’ ability to recognize the correlation between wine reviews and scores. Full article
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