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Applied Sciences
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Artificial Neural Network (ANN) Based Prediction System in Foods
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Artificial Neural Network (ANN) Based Prediction System in Foods

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 (20 July 2023) | Viewed by 4928

Special Issue Editor

Dr. Krzysztof Przybył

E-Mail Website
Guest Editor
Department of Dairy and Process Engineering, Food Sciences and Nutrition, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland
Interests: application of artificial intelligence; deep learning and machine learning; high-dimensional data visualization; python programming; database designing; data preprocessing; statistical analysis; optimizing fruit and vegetable drying processes; patterns analyzing; analysis of the morphological structure of raw materials using electron microscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the increased demand for all kinds of food products and consumer expectations has prompted the search for innovative methods supporting the quality assessment, storage and preservation of these products. The last decade has been a time of technological progress and the development and popularity of artificial intelligence, which is a dynamically developing discipline studying the intelligence of machines and using machine and deep vision in the broadly understood aspect of decision problems. Thanks to the development of intelligent machines and intelligent systems, it is possible to use data for structured and interrelated predictions, classification through the use of a supporting image analysis, consideration of the presence of specific objects, matching patterns, information analysis, filtering signals by extracting characteristic features from images and process optimization. Currently, the cooperation of research centers with the industry poses new challenges to science. Considering research problems regarding food with the use of artificial intelligence, it becomes necessary to choose the correct implementation of a given functionality, an adequate analysis of model errors, attempts to solve problems with data purity, as well as the ability to verify results, guaranteeing the appropriate quality of the product. This Special Issue aims to focus on the application of machine and deep learning methods and data analyses and their implementation, especially in the quality, storage and preservation of food. Topics of interest include, but are not limited to:

  • Machine comprehension;
  • Deep learning;
  • Pattern Recognition;
  • Decision making;
  • Self-organisms systems;
  • Computer image analysis in food processing.

Dr. Krzysztof Przybył
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. 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

  • deep learning
  • artificial neural networks (ANN)
  • food processing
  • computer vision

Published Papers (2 papers)

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Research

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10 pages, 1942 KiB  
Communication
Prediction of Influence Transmission by Water Temperature of Fish Intramuscular Metabolites and Intestinal Microbiota Factor Cascade Using Bayesian Networks
by Hideaki Shima, Kenji Sakata and Jun Kikuchi
Appl. Sci. 2023, 13(5), 3198; https://0-doi-org.brum.beds.ac.uk/10.3390/app13053198 - 02 Mar 2023
Viewed by 924
Abstract
Aquaculture is receiving attention as one of the solutions to the global food problem. Therefore, it is essential to clarify the impact of fish and their environment on the stable supply and uniformity of the quality of fish provided as meat. Nuclear magnetic [...] Read more.
Aquaculture is receiving attention as one of the solutions to the global food problem. Therefore, it is essential to clarify the impact of fish and their environment on the stable supply and uniformity of the quality of fish provided as meat. Nuclear magnetic resonance can comprehensively acquire metabolite information in foods nondestructively and is suitable for measuring physical properties for quality control. Moreover, recent advances in machine learning methods and artificial neural network (ANN) analysis have contributed to the analysis of comprehensive information. In this study, we sampled a wide variety of fish from the natural sea and analyzed them using a scheme incorporating ANN. As a result, it was found that anserine, an antioxidant, was found to be reduced in fish muscles, and this destabilized the homeostasis of other metabolites at low water temperature. We also concluded that the fish muscle metabolic state was stabilized in warm water. Furthermore, a relationship between water temperature and the intestinal microbiota of fish was established. In this study, we evaluated the relationship between the metabolic profile changes in fish muscle and external environmental factors and predicted connection strength and order using machine learning and ANN. We conclude that our proposed scheme for estimating the degree and direction of the influence of environmental factors on organisms by using ANN will work. Full article
(This article belongs to the Special Issue Artificial Neural Network (ANN) Based Prediction System in Foods)
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Review

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22 pages, 1454 KiB  
Review
Applications MLP and Other Methods in Artificial Intelligence of Fruit and Vegetable in Convective and Spray Drying
by Krzysztof Przybył and Krzysztof Koszela
Appl. Sci. 2023, 13(5), 2965; https://0-doi-org.brum.beds.ac.uk/10.3390/app13052965 - 25 Feb 2023
Cited by 9 | Viewed by 2645
Abstract
The seasonal nature of fruits and vegetables has an immense impact on the process of seeking methods that allow extending the shelf life in this category of food. It is observed that through continuous technological changes, it is also possible to notice changes [...] Read more.
The seasonal nature of fruits and vegetables has an immense impact on the process of seeking methods that allow extending the shelf life in this category of food. It is observed that through continuous technological changes, it is also possible to notice changes in the methods used to examine and study food and its microbiological aspects. It should be added that a new trend of bioactive ingredient consumption is also on the increase, which translates into numerous attempts that are made to keep the high quality of those products for a longer time. New and modern methods are being sought in this area, where the main aim is to support drying processes and quality control during food processing. This review provides deep insight into the application of artificial intelligence (AI) using a multi-layer perceptron network (MLPN) and other machine learning algorithms to evaluate the effective prediction and classification of the obtained vegetables and fruits during convection as well as spray drying. AI in food drying, especially for entrepreneurs and researchers, can be a huge chance to speed up development, lower production costs, effective quality control and higher production efficiency. Current scientific findings confirm that the selection of appropriate parameters, among others, such as color, shape, texture, sound, initial volume, drying time, air temperature, airflow velocity, area difference, moisture content and final thickness, have an influence on the yield as well as the quality of the obtained dried vegetables and fruits. Moreover, scientific discoveries prove that the technology of drying fruits and vegetables supported by artificial intelligence offers an alternative in process optimization and quality control and, even in an indirect way, can prolong the freshness of food rich in various nutrients. In the future, the main challenge will be the application of artificial intelligence in most production lines in real time in order to control the parameters of the process or control the quality of raw materials obtained in the process of drying. Full article
(This article belongs to the Special Issue Artificial Neural Network (ANN) Based Prediction System in Foods)
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