Deep Learning and Chemometrics Combined with Spectroscopic Technologies for Food Quality Assessment
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 15 October 2026 | Viewed by 118
Special Issue Editor
2. High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang, China
Interests: nondestructive detection; hyperspectral imaging technology; spectroscopy; electronic nose; chemometrics; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Food quality and safety are critical to public health, industrial development, and global food security. Traditional food quality assessment methods are often destructive, time-consuming, and costly, failing to meet the demands of rapid, in situ, and large-scale detection in modern food production and circulation. Spectroscopic technologies such as near-infrared, mid-infrared, Raman, and hyperspectral imaging have emerged as powerful tools for non-destructive food analysis, while deep learning and chemometrics have significantly enhanced the accuracy, robustness, and generalization of spectral data interpretation.
This Special Issue aims to gather high-quality original research papers, reviews, and short communications focusing on the integration of deep learning, chemometrics, and spectroscopic technologies for food quality assessment.
Potential topics include, but are not limited to, the following: novel spectral preprocessing strategies combining chemometrics and deep learning; end-to-end spectral modeling based on deep learning or chemometrics; applications in food authenticity, nutritional component quantification, freshness evaluation, defect detection, and safety monitoring; interpretable AI for spectral food analysis; portable spectral devices integrated with deep learning or chemometrics.
Prof. Dr. Xiaohong Wu
Guest Editor
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. 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
- deep learning
- chemometrics
- spectroscopic technology
- food quality assessment
- non-destructive detection
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