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Advanced Methods and Techniques: Ensuring and Improving Food Safety and Quality

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

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1627

Special Issue Editors

Dr. Patrizio Lorusso

E-Mail Website
Guest Editor
Department of Veterinary Medicine, University of Bari Aldo Moro, Provincial Road 62 to Casamassima km 3, 70100 Valenzano, Italy
Interests: viruses; food safety; foodborne patogens; emerging patogens; foodborne viruses;
Dr. Valentina Terio

E-Mail Website
Guest Editor
Department of Veterinary Medicine, University of Bari Aldo Moro, Provincial Road to Casamassima km 3, 70100 Valenzano, Italy
Interests: arcobacter; infectious agent; food microbiology; listeria monocytogenes; food contamination; genome sequencing
Special Issues, Collections and Topics in MDPI journals
Dr. Pandiscia Annamaria

E-Mail Website
Guest Editor Assistant
Department of Veterinary Medicine, University of Bari Aldo Moro, Provincial Road 62 to Casamassima km 3, 70100 Valenzano, Italy
Interests: foodborne viruses; waterborne viruses; enteroviruses; food safety; food microbiology; risk assessment; food hygiene; viability assays; hepatitis A; hepatitis E; norovirus

Special Issue Information

Dear Colleagues,

Ensuring food safety and quality remains a global priority as supply chains become more complex and consumer expectations rise. This Special Issue highlights recent advances in analytical methods, monitoring technologies, and data-driven approaches designed to detect, prevent, and manage risks across the food supply chain. Contributions are expected to explore cutting-edge techniques such as high-resolution mass spectrometry, next-generation sequencing, biosensors, and artificial intelligence, which are reshaping how we assess contaminants, pathogens, and overall product integrity. Emphasis will also be placed on emerging strategies for traceability, real-time monitoring, and the integration of foodomics to support evidence-based decision-making. By showcasing innovative tools and interdisciplinary solutions, this issue aims to support researchers, policymakers, and industry stakeholders in strengthening food safety systems and enhancing product quality from farm to fork.

Dr. Patrizio Lorusso
Dr. Valentina Terio
Guest Editors

Dr. Pandiscia Annamaria
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

  • high-resolution mass spectrometry
  • biosensors and nanosensors for rapid detection
  • metagenomics and food microbiome analysis
  • blockchain-based traceability technologies
  • big data and artificial intelligence in food safety
  • hyperspectral imaging for quality control
  • isothermal amplification techniques (LAMP, RPA) for pathogen detection
  • advanced sampling and extraction methods for emerging contaminants
  • integrated foodomics approaches (proteomics, metabolomics, lipidomics)
  • real-time automated HACCP monitoring systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 3156 KB  
Article
Detecting Escherichia coli on Conventional Food Processing Surfaces Using UV-C Fluorescence Imaging and Deep Learning
by Zafar Iqbal, Thomas F. Burks, Snehit Vaddi, Pappu Kumar Yadav, Quentin Frederick, Satya Aakash Chowdary Obellaneni, Jianwei Qin, Moon Kim, Mark A. Ritenour, Jiuxu Zhang and Fartash Vasefi
Appl. Sci. 2026, 16(2), 968; https://doi.org/10.3390/app16020968 - 17 Jan 2026
Viewed by 639
Abstract
Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. [...] Read more.
Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 105, 107, and 108 colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments. Full article
(This article belongs to the Special Issue Advanced Methods and Techniques: Ensuring and Improving Food Safety and Quality)
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21 pages, 998 KB  
Article
Profiling the Aroma of Grape Spirits for Port Wine Using a Multi-Analytical GC Approach and Sensory Analysis
by Ilda Caldeira, Maria Loureiro, Nuno Martins, Sílvia Lourenço, Maria João Cabrita, Ricardo Silva, Sílvia M. Rocha and Fernando Alves
Appl. Sci. 2026, 16(2), 941; https://doi.org/10.3390/app16020941 - 16 Jan 2026
Viewed by 641
Abstract
Port wine production involves the addition of grape spirit to halt fermentation and retain natural sweetness. This spirit, produced by distilling wine and its by-products, must comply with legal standards, including a mandatory sensory assessment. Because grape spirit influences Port wine’s volatile composition, [...] Read more.
Port wine production involves the addition of grape spirit to halt fermentation and retain natural sweetness. This spirit, produced by distilling wine and its by-products, must comply with legal standards, including a mandatory sensory assessment. Because grape spirit influences Port wine’s volatile composition, this study investigated the odour-active compounds present in several grape spirits intended for fortification. Volatile compounds were extracted by liquid–liquid extraction, concentrated, and analysed using gas chromatography–olfactometry (GC-O) and gas chromatography–mass spectrometry (GC-MS). In GC-O, based on frequency detection, a panel of assessors sniffed the extracts to determine the presence of aroma compounds. The results revealed a wide range of odour-active compounds in grape spirits, belonging to several chemical families such as esters, alcohols, terpenic compounds and acids. These compounds exhibited both pleasant aromas, such as fruity, floral and caramel notes as well as undesirable ones like cheese and foot odour. Most of these compounds originate from the fermentation process and are also found in other unaged distilled beverages, including young Cognac, Calvados and fruit spirits. This research highlights the aromatic complexity of grape spirits and, for the first time, determined the aroma thresholds for 25 of 36 the compounds studied at an ethanol content of 20%. Full article
(This article belongs to the Special Issue Advanced Methods and Techniques: Ensuring and Improving Food Safety and Quality)
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