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Agriculture
Special Issues
Harvesting, Transportation, Processing and Storage of Vegetables and Fruits
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Harvesting, Transportation, Processing and Storage of Vegetables and Fruits

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Product Quality and Safety".

Deadline for manuscript submissions: closed (10 November 2023) | Viewed by 1908

Special Issue Editors

Prof. Dr. Józef Gorzelany

E-Mail Website
Guest Editor
Department of Food and Agriculture Production Engineering, University of Rzeszow, 35-601 Rzeszów, Poland
Interests: food texture; engineering; chemical analyses; mechanical property; cereals, fruits
Special Issues, Collections and Topics in MDPI journals
Dr. Piotr Kuźniar

E-Mail Website
Guest Editor
Department of Agricultural and Food Production Engineering, University of Rzeszow, 35-601 Rzeszów, Poland
Interests: vegetables and fruits; harvest technology; transport technology; post-harvest technology; processing technologies; biotic and abiotic factors; storage and mechanical properties

Special Issue Information

Dear Colleagues,

In today's world, increasing attention is paid to food production and food security, bearing in mind the quality of the harvested material. Important links in the production and management of raw materials (fruit and vegetables) include their transport, processing, storage, preservation and use for the production of high-quality products. Therefore, it is necessary to promote and disseminate the latest research results in the field of the quality of selected plant raw materials and the method of their preservation in order to improve their quality.

This Special Issue specifically focuses on the harvesting processes of selected crops in terms of mechanical properties, losses, damage during harvesting and transport, assessment of the impact of selected factors in the storage process on their mechanical and chemical properties, with particular emphasis on their health-promoting properties, such as their sugar content , vitamin C and polyphenols, and the possibility of using the collected and stored plant materials in the food industry, e.g., as an additive-enriching beer.

Dr. Józef Gorzelany
Dr. Piotr Kuźniar
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. Agriculture 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 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

  • vegetables and fruits
  • harvest technology
  • transport technology
  • post-harvest technology
  • processing technologies
  • biotic and abiotic factors
  • storage
  • food safety
  • mechanical and chemical properties
  • pro-health properties
  • pro-health beer additive

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Published Papers (1 paper)

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Research

18 pages, 3163 KiB  
Article
Evaluation of the Use of Machine Learning to Predict Selected Mechanical Properties of Red Currant Fruit (Ribes rubrum L.) Ozonized during Storage
by Piotr Kuźniar, Katarzyna Pentoś and Józef Gorzelany
Agriculture 2023, 13(11), 2125; https://doi.org/10.3390/agriculture13112125 - 10 Nov 2023
Cited by 2 | Viewed by 1299
Abstract
The study examined selected biometric and mechanical properties of fruits of three varieties of red currant (Ribes rubrum L.) from organic cultivation. The influence of the harvest date of red currant fruits, their storage time, and the use of ozone at a [...] Read more.
The study examined selected biometric and mechanical properties of fruits of three varieties of red currant (Ribes rubrum L.) from organic cultivation. The influence of the harvest date of red currant fruits, their storage time, and the use of ozone at a concentration of 10 ppm for 15 and 30 min on the water content, volume, and density, as well as the destructive force and the apparent modulus of elasticity, were determined. Fruits harvested at harvest maturity were characterized by a much larger volume and lower water content compared to fruits harvested seven days earlier. The ozonation process, regardless of the harvest date, resulted in a reduction in volume, density, and humidity. After 15 days of storage, the fruits of the tested varieties showed a decrease in the average water content from 86.15% to 83.79%. The tests showed a decrease in the destructive force and the apparent modulus of elasticity, the average value of which for fresh fruit was 76.98 ± 21.0 kPa, and after 15 days of storage, it decreased to 56.34 ± 15.96 kPa. The relationships between fruit-related parameters, harvesting, and storage conditions and fruit strength characteristics were modeled with the use of neural networks and support vector machines. These relationships are complex and nonlinear, and therefore, machine learning is usually more relevant than the traditional methods of modeling. For evaluation of the performance of the models, statistical parameters such as the coefficient of correlation (R), root-mean-squared error (RMSE), and generalization ability coefficient (GA) were used. The best models for the prediction of an apparent modulus of elasticity were developed with the use of ANNs. These models can be used in practice because the correlation between expected and predicted values was in the range 0.78–0.82, RMSE was in the range 13.38–14.71, and generalization ability was excellent. A significantly lower accuracy was achieved for models with a destructive force as the output parameter (R ≤ 0.6). Full article
(This article belongs to the Special Issue Harvesting, Transportation, Processing and Storage of Vegetables and Fruits)
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