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Sustainable Food Processing Safety and Public Health
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Sustainable Food Processing Safety and Public Health

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Food".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 5508

Special Issue Editors

Dr. Ahmed Rady

E-Mail Website
Guest Editor
1. Teagasc Food Research Centre, Doublin, Ireland
2. Food, Water, Waste (FWW) Research Group, University of Nottingham, Nottingham, UK
Interests: food safety; food quality; non-destructive sensing for food quality and safety; postharvest engineering; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Samet Ozturk

E-Mail Website
Guest Editor
Food, Water, Waste (FWW) Research Group, University of Nottingham, Nottingham, UK
Interests: food safety; food quality; food sustainability; thermal and non-thermal food processing; innovative technologies

Special Issue Information

Dear Colleagues,

Food safety has occupied an important portion of the research due to its significant effect on the public health especially with the currently global food trading, in addition to the effect on the reputation of food businesses and the indirect effect on the environment due to the wasted resources (energy, water, soil). Thus, sustainable food processing is key for providing high-quality food products with less impact on the environment. The negative implications of pathogenic microorganisms on public health and economy have constituted a major concern for sustainable food security and the supply chain, which caught the attention of researchers to develop technologies to benefit the food industry as well as the consumers. Innovative technologies such as non-thermal processing and active and smart packaging have been developed by scientists and started to be adopted by the food industry in order to produce safe and high-quality food products with a relatively long shelf life. In the meanwhile, the use of intelligent technologies to reduce food loss and waste at the different stages of the supply chain can alleviate the environmental implications. Therefore, we are delighted to invite you to participate in this Special Issue of Sustainability titled “Sustainable Food Processing Safety and Public Health”, where you can contribute your thoughts and share your research outcomes.

This Special Issue aims to highlight innovative advances toward emerging technologies applied, alone or in combination, to the processing, safety, traceability and sustainability of various foods. The specific topics relevant to this issue include, but are not limited to, traditional and innovative thermal and non-thermal processing technologies, non-invasive controlling methods, food packaging, natural antimicrobial or antifungal compositions, and non-destructive sensors. This Special Issue aims to collect up-to-date original research and review manuscripts focusing on the development of such technologies for enhancing sustainable food safety and public health.

We look forward to receiving your contributions.

Dr. Ahmed Rady
Dr. Samet Ozturk
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. Sustainability 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 safety
  • sustainable food processing
  • thermal and non-thermal processing
  • innovative technologies
  • natural antimicrobials
  • rapid and non-destructive detection sensors
  • food traceability and supply chain
  • food waste
  • novel packaging
  • machine learning

Published Papers (3 papers)

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Research

14 pages, 4863 KiB  
Article
Apricot Stone Classification Using Image Analysis and Machine Learning
by Ewa Ropelewska, Ahmed M. Rady and Nicholas J. Watson
Sustainability 2023, 15(12), 9259; https://doi.org/10.3390/su15129259 - 08 Jun 2023
Cited by 3 | Viewed by 1323
Abstract
Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars [...] Read more.
Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production. Full article
(This article belongs to the Special Issue Sustainable Food Processing Safety and Public Health)
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12 pages, 1446 KiB  
Article
Utilization of FTIR and Machine Learning for Evaluating Gluten-Free Bread Contaminated with Wheat Flour
by Akinbode A. Adedeji, Abuchi Okeke and Ahmed M. Rady
Sustainability 2023, 15(11), 8742; https://doi.org/10.3390/su15118742 - 29 May 2023
Cited by 4 | Viewed by 1960
Abstract
In this study, Fourier-transform infrared (FTIR) spectroscopy coupled with machine learning (ML) approaches were applied to detect and quantify wheat flour (WF) contamination in gluten-free cornbread. Samples of corn flour (CF) were contaminated with WF in the range of 0–10% with a 0.5% [...] Read more.
In this study, Fourier-transform infrared (FTIR) spectroscopy coupled with machine learning (ML) approaches were applied to detect and quantify wheat flour (WF) contamination in gluten-free cornbread. Samples of corn flour (CF) were contaminated with WF in the range of 0–10% with a 0.5% increment. The flour samples were baked into bread using basic bread formulation and ground into a fine particle size for homogeneity, and FTIR spectra of the ground samples were obtained and standardized before modeling. For constructing the classification model, majority voting-based ensemble learning (stack of k-nearest neighbor [KNN], random forest, and support vector classifier) was implemented to detect and quantify WF in the cornbread samples. KNN regressor was determined to be the best predictive model to quantify wheat contaminants based on the majority-vote ensemble. The optimal classification model for the test set showed an F1 score, true positive rate (TPR), and false negative rate (FNR) of 1.0, 1.0, and 0.0, respectively. For the quantification models, the coefficient of determination and root mean square error for the prediction set (R2P and RMSEP) were 0.99 and 0.34, respectively. These results show the feasibility of utilizing FTIR along with supervised learning algorithms for the rapid offline evaluation of wheat flour contamination in gluten-free products. Full article
(This article belongs to the Special Issue Sustainable Food Processing Safety and Public Health)
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14 pages, 2593 KiB  
Article
Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms
by Ewa Ropelewska, Kadir Sabanci, Muhammet Fatih Aslan and Necati Çetin
Sustainability 2023, 15(8), 7011; https://doi.org/10.3390/su15087011 - 21 Apr 2023
Cited by 3 | Viewed by 1637
Abstract
The objective of this study was to evaluate the differences in texture parameters between freeze-dried and fresh carrot slices using image processing and artificial intelligence. Images of fresh and freeze-dried carrot slices were acquired using a digital camera. Texture parameters were extracted from [...] Read more.
The objective of this study was to evaluate the differences in texture parameters between freeze-dried and fresh carrot slices using image processing and artificial intelligence. Images of fresh and freeze-dried carrot slices were acquired using a digital camera. Texture parameters were extracted from slice images converted to individual color channels L, a, b, R, G, B, X, Y, and Z. A total of 1629 texture parameters, 181 for each of these color channels, were obtained. Models for the classification of freeze-dried and fresh carrot slices were created using various machine learning algorithms, based on attributes selected from a combined set of textures extracted from images in all color channels (L, a, b, R, G, B, X, Y, and Z). Using three different feature selection methods (Genetic Search, Ranker, and Best First), the 20 most effective texture parameters were determined for each method. The models with the highest classification accuracy obtained by applying various machine learning algorithms from Trees, Rules, Meta, Lazy, and Functions groups were determined. The classification successes obtained with the parameters selected from all three different feature selection algorithms were compared. Random Forest, Multi-class Classifier, Logistic and SMO machine learning algorithms achieved 100% accuracy in the classification performed with texture features obtained by each feature selection algorithm. Full article
(This article belongs to the Special Issue Sustainable Food Processing Safety and Public Health)
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