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Topic Editors

Department Technology of Plant Production and Commodities Science, University of Life Sciences in Lublin, 20-950 Lublin, Poland
Dr. Barbara Krochmal-Marczak
Department of Food Production and Safety, Carpathian State College in Krosno, Dmochowskiego 12 Str., 38-400 Krosno, Poland
Department of Potato Agronomy, Plant Breeding and Acclimatization Institute–National Research Institute, Branch in Jadwisin, 05-140 Serock, Poland
Department of Plant Production Technology and Commodity Science, University of Life Sciences in Lublin, Lublin, Poland

Determinants and Methods of Quality Management in Agriculture and Food Processing

Abstract submission deadline
31 August 2025
Manuscript submission deadline
31 October 2025
Viewed by
2266

Topic Information

Dear Colleagues,

Both the COVID-19 pandemic and the recent war in Ukraine have had a significant impact on the markets of raw materials and food products. In 2020–2021, the pandemic was responsible for record increases in the prices of cereals and cereal products. In 2022, there is an increase in the prices of all raw materials, especially food and energy raw materials. On the other hand, the recent war in Ukraine has severely disrupted the supply of these raw materials and led to historically soaring prices in many commodity markets. In particular, the increase in energy prices over the year was the largest since 1973. Thus, the comprehensive effects of the COVID-19 pandemic and the war in Ukraine have had a profound impact on global commodity markets. The aim of this Topic is to provide a collection of high-quality research papers covering a wide range of topics with a fresh look at the quality of goods in an environment of economic and political uncertainty. We welcome original empirical and theoretical contributions to all aspects of quality determinants in commodity and food markets. We also welcome documents related to the situation and problems of agricultural exchanges. Quality plays a key role in commodity science and is the subject of a wide range of topics related to key areas of economic and social life. It is an interdisciplinary research area and is therefore the subject of research across many scientific disciplines. It is expressed in the way the organization is managed and is an important aspect that determines the success of the organization and at the same time constantly evolves. Currently, quality no longer relates to the extent to which a given product or service meets customer requirements, but also relates to management processes.

Quality management is the deliberate performance of the management function in terms of optimizing the use of resources and other factors, as well as process rationalization, and focuses on obtaining high-quality products and their continuous improvement. Therefore, in quality management, the goal should always be to improve the quality of products, aiming to satisfy the expectations and needs of the customer. Quality management is an approach that aims to improve the efficiency and flexibility of an organization in order to meet the requirements, needs and expectations of customers.

Currently, there is an increased interest in quality issues, which results from the development of the market and the desire to increase the competitiveness of enterprises. The continuous scientific and technical developments of the agricultural and processing industries and the need for the constant modification of products, with the aim to increase the efficiency of enterprises, means that more attention is paid to the quality of processes and final products. Striving to improve the quality of production, in turn, puts pressure on the introduction of innovative technologies. This results in an increase in labor productivity, the creation of a culture supporting innovation, modern products, a lower consumption of raw materials, increased pro-ecological effect, better use of the means of production, better satisfaction of the growing needs of customers and an increase in the pace of development of enterprises and the entire economy.

Prof. Dr. Barbara Sawicka
Dr. Barbara Krochmal-Marczak
Dr. Piotr Barbaś
Dr. Dominika Skiba
Topic Editors

Keywords

  • quality assurance and management
  • quality optimization
  • methods of quality testing and assessment
  • methods of detecting defects and adulterations
  • quality assessment of final products
  • quality control of goods
  • factors shaping the quality of goods
  • factors lowering the quality of goods
  • strategic management
  • implementation (implementation) of management
  • modern management

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agriculture
agriculture
3.3 4.9 2011 19.2 Days CHF 2600 Submit
Businesses
businesses
- - 2021 24.7 Days CHF 1000 Submit
Processes
processes
2.8 5.1 2013 14.9 Days CHF 2400 Submit
Standards
standards
- - 2021 30.6 Days CHF 1000 Submit
Sustainability
sustainability
3.3 6.8 2009 19.7 Days CHF 2400 Submit

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Published Papers (1 paper)

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21 pages, 5901 KiB  
Article
A Rapid Identification Method for Cottonseed Varieties Based on Near-Infrared Spectral and Generative Adversarial Networks
by Qingxu Li, Hao Li, Renhao Liu, Xiaofeng Dong, Hongzhou Zhang and Wanhuai Zhou
Agriculture 2024, 14(12), 2177; https://doi.org/10.3390/agriculture14122177 - 29 Nov 2024
Viewed by 430
Abstract
China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. The detection of [...] Read more.
China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. The detection of cottonseed variety information has become a critical issue for the Chinese cotton industry. In this study, we collected near-infrared (NIR) spectral data from six cottonseed varieties and constructed a GAN for cottonseed NIR data (GAN-CNIRD) model to generate additional cottonseed NIR data. The Euclidean distance method was used to label the generated NIR data according to the characteristics of the true NIR data. We then applied Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Normalization algorithms to preprocess the combined dataset of generated and real cottonseed NIR data. Feature wavelengths were extracted using Bootstrap Soft Shrinkage (BOSS) and Competitive Adaptive Reweighted Sampling (CARS) algorithms. Subsequently, we developed Linear Discriminant Analysis (LDA), Random subspace method (RSM), and convolutional neural network (CNN) models to classify the cottonseed varieties. The results showed that for the LDA model, the use of feature wavelengths extracted after Normalization-BOSS processing achieved the best performance with an accuracy of 97.00%. For the RSM model, the use of feature wavelengths extracted after SNV-CARS processing achieved the best performance with an accuracy of 98.00%. For the CNN model, the use of feature wavelengths extracted after MSC-CARS processing achieved the best performance with an accuracy of 100.00%. Data augmentation using GAN-CNIRD-generated cottonseed data improved the accuracy of the three optimal models by 6%, 5%, and 6%, respectively. This study provides a crucial reference for the rapid detection of cottonseed variety information and has significant implications for the standardized management of cottonseed varieties. Full article
Show Figures

Figure 1

Figure 1
<p>Six different cottonseed varieties.</p>
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<p>The NIR data acquisition system for cottonseeds.</p>
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<p>WGAN-GP.</p>
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<p>GAN-CNIRD.</p>
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<p>CNN.</p>
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<p>Loss of GAN-CNIRD.</p>
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<p>Generated and real cottonseed data.</p>
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<p>The preprocessed cottonseed data.</p>
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<p>Selection of cottonseed features using the CARS.</p>
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<p>Feature wavelengths selected by CARS.</p>
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<p>The variation of RMSECV with the number of iterations.</p>
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<p>The weight distribution at the 23rd iteration.</p>
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<p>Feature wavelengths selected by BOSS.</p>
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