Food Quality Evaluation Methods Based on Non-Destructive and Intelligent Technologies

Dear Colleagues,

Aim: To provide a platform for publishing recent advances in non-destructive, rapid, and intelligent analytical approaches for evaluating food quality, safety, authenticity, and freshness throughout the food supply chain.

Scope: This Special Issue focuses on innovative methodologies that enable reliable, efficient, and sustainable food quality assessment without compromising sample integrity. It welcomes studies on advanced sensing technologies, spectroscopic and imaging techniques, chemometrics, artificial intelligence, machine learning, and integrated analytical approaches for monitoring food quality and safety in agricultural products, raw materials, processed foods, and functional foods.

Ensuring food quality and safety has become increasingly important in response to growing consumer expectations, stricter regulatory requirements, and the need for sustainable food production. Conventional analytical methods often require labor-intensive sample preparation, are time-consuming, and may involve destructive testing. In contrast, non-destructive analytical technologies, combined with intelligent data-processing tools, offer rapid, accurate, and environmentally friendly alternatives for assessing food quality while preserving sample integrity. Recent advances in spectroscopy, imaging, sensor technologies, chemometrics, artificial intelligence (AI), and machine learning have significantly expanded the capabilities of food quality evaluation, enabling real-time monitoring, authentication, adulteration detection, freshness assessment, and process control across the food supply chain.

We are pleased to invite you to contribute to this Special Issue, titled "Food Quality Evaluation Methods Based on Non-Destructive and Intelligent Technologies," to be published in Foods.

This Special Issue aims to present the latest scientific developments in non-destructive and intelligent technologies for food quality evaluation. It seeks to highlight innovative analytical methodologies, data-driven approaches, and practical applications that improve the assessment of food quality, safety, authenticity, and traceability. The Special Issue is fully aligned with the scope of Foods, promoting interdisciplinary research that bridges food science, analytical chemistry, engineering, sensor technology, and artificial intelligence to address current challenges in modern food systems.

In this Special Issue, original research articles, review articles, and communications are welcome. Research areas may include, but are not limited to, the following:

• Non-destructive techniques for food quality evaluation;
• Spectroscopic methods (NIR, MIR, Raman, fluorescence, hyperspectral and multispectral imaging);
• Computer vision and image analysis for food inspection;
• Electronic nose, electronic tongue, and biosensor technologies;
• Artificial intelligence, machine learning, and deep learning in food quality assessment;
• Chemometric methods and multivariate data analysis;
• Rapid detection of food adulteration, contamination, and authenticity;
• Freshness and shelf-life prediction;
• Smart sensing technologies for food monitoring and process control;
• Integrated analytical platforms for food quality and safety evaluation.

We look forward to receiving your valuable contributions.

Dr. Patryk Pokorski
Dr. Jorge Custodio-Mendoza
Dr. Tomasz Piechowiak
Dr. Marcin A. Kurek
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods 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 2900 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.

• food quality evaluation
• non-destructive analysis
• intelligent technologies
• food safety
• spectroscopy
• machine learning
• artificial intelligence
• chemometrics
• sensor technologies
• food authenticity