Spectroscopy and Intelligent Data Processing for Food Quality, Safety and Authenticity
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 10 August 2026 | Viewed by 334
Editors
Interests: plant phenotyping; quality inspection; spectroscopy and spectral analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: information engineering; spectroscopy; spectral imaging; plant phenotyping; agro-food quality detection; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Spectroscopic technologies (e.g., visible and near-infrared (VIS/NIR) spectroscopy, mid-infrared (MIR) spectroscopy, hyperspectral imaging (HSI), laser-induced breakdown spectroscopy (LIBS) or multi-spectroscopic fusion) stand as indispensable pillars in modern food science, delivering reliable and robust solutions for food quality assessment and accreditation. While these spectroscopic tools excel at capturing rich, multidimensional spectral information related to food composition, structure and safety attributes, it is data-processing methods that play a pivotal role in addressing key challenges such as signal noise, spectral overlap, matrix interference and complex correlations between raw spectral data and target indicators. Therefore, this Special Issue aims to collect high-quality original research, innovative methodologies and practical applications that leverage spectroscopy to drive progress in food quality and composition analysis, food safety and contaminant screening, food authentication and traceability as well as on-site and industrial-scale applications across the entire food-supply chain. We also welcome submissions focusing on the development or optimization of spectroscopic and imaging methods specifically tailored to food-quality assessment and accreditation. Additionally, we value original contributions centered on advanced data-processing strategies such as machine learning, deep learning or artificial intelligence that enhance spectral-data interpretation, automate detection workflows and boost prediction performance.
Dr. Yiying Zhao
Dr. Chu Zhang
Dr. Wei Wang
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.
Keywords
- spectroscopic technique innovations
- chemometrics and AI integration
- food quality and composition analysis
- food safety and contaminant screening
- authentication and traceability
- on-site and industrial-scale applications
- sustainability and circular economy
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