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Applied Sciences
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Applications of Machine Learning in Food Industry
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Applications of Machine Learning in Food Industry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 5054

Special Issue Editors

Dr. Mengxing Li

E-Mail Website
Guest Editor
1. Department of Biological Systems Engineering, The University of Nebraska-Lincoln, 211 Chase Hall, 3605 Fair St, Lincoln, NE 68583, USA
2. Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
Interests: statistical modeling; mathematical optimization; fermentation; process optimization; design of experiments; machine learning; food and bioprocess
Dr. Manping Jia

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
Interests: bioelectronics; materials science; organic light-emitting diodes; materials characterization; nanotechnology; biosensor; microfluidics; biomaterials
Dr. Peihua Ma

E-Mail Website
Guest Editor
Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD 20740, USA
Interests: food science

Special Issue Information

Dear Colleagues,

Articifical intelligence and machine learning are improving the food industry, which is a multidisciplinary research field. Machine learning has exhibited success in all aspects of the food industry, including but not limited to the following: sensory evaluation; food sales prediction and supply chain management; spectroscopic techniques; food production optimization and process control; substainability; and life cycle assessment. This Special Issue will be dedicated to the most recent advances of all aspects of machine learning development and applications in the food industry.

Dr. Mengxing Li
Dr. Manping Jia
Dr. Peihua Ma
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • food industry
  • machine learning
  • process control
  • supply chain management
  • sustainability

Published Papers (2 papers)

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Research

19 pages, 3573 KiB  
Article
Probabilistic Machine Learning for the Authentication of the Protected Designation of Origin of Greek Bottarga from Messolongi: A Generic Methodology to Cope with Very Small Number of Samples
by George Tsirogiannis, Anna-Akrivi Thomatou, Eleni Psarra, Eleni C. Mazarakioti, Katerina Katerinopoulou, Anastasios Zotos, Achilleas Kontogeorgos, Angelos Patakas and Athanasios Ladavos
Appl. Sci. 2022, 12(13), 6335; https://doi.org/10.3390/app12136335 - 22 Jun 2022
Viewed by 1443
Abstract
Consumers are increasingly interested in the geographical origin of foodstuff, as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used [...] Read more.
Consumers are increasingly interested in the geographical origin of foodstuff, as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products like wine, oil, and meat by using large datasets and analysis. On the other hand, only few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products, and even fewer investigate a small number of samples and datasets. Stable isotopes of Carbon (C), Nitrogen (N), and Sulfur (S) analysis of bottarga samples from four different origins were carried out. The first results show that the stable isotopes ratios of C, N, and S could be used to discriminate the Greek PDO Bottarga (Messolongi) from other similar products by using a probabilistic machine learning methodology. That could use limited sample data to fit/estimate their parameters, while, at the same time, being capable of describing accurately the population and discriminate individual samples regarding their origin. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Food Industry)
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24 pages, 1497 KiB  
Article
The Chef’s Choice: System for Allergen and Style Classification in Recipes
by Andreas Roither, Marc Kurz and Erik Sonnleitner
Appl. Sci. 2022, 12(5), 2590; https://doi.org/10.3390/app12052590 - 2 Mar 2022
Cited by 3 | Viewed by 2751
Abstract
Allergens in food items can be dangerous for individuals affected by food allergens. Considering how many different ingredients and food items exists, it is hard to keep track of which food items contain relevant allergens. Food businesses in the EU are required to [...] Read more.
Allergens in food items can be dangerous for individuals affected by food allergens. Considering how many different ingredients and food items exists, it is hard to keep track of which food items contain relevant allergens. Food businesses in the EU are required to label foods with information about the 14 major food allergens defined by the EU legislation. This improves the situation for affected individuals. Nevertheless, more changes are necessary to provide reasonable protection for people with severe allergic reactions. Recipe websites and online content is usually not labelled with allergens. In addition, the 14 main allergen categories consist of a variety of different ingredients that are not always easy to remember. Scanning websites and recipes for specific allergens can consume a fair amount of time if the reader wants to make sure no allergen is missed. In this article, a dataset is processed and used for machine learning to classify cuisine style and allergens. The dataset used contains labelling for the 14 major allergen categories. Furthermore, a system is proposed that informs the user about style and allergens in a recipe with the help of a browser add-on. To measure the performance of the proposed system, a user study is conducted where participants label recipes with food allergens. A comparison between human and system performance as well as the time needed to read and label recipes concludes this article. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Food Industry)
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