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Application of Artificial Intelligence and Machine Learning in Food Analysis
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Application of Artificial Intelligence and Machine Learning in Food Analysis

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1542

Special Issue Editor

Dr. Shuxiang Fan

E-Mail Website
Guest Editor
School of Technology, Beijing Forestry University, Beijing, China
Interests: food safety; food quality; food authenticity; hyperspectral imaging; NIR; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intersection of artificial intelligence (AI) and machine learning (ML) with food science has emerged as a transformative force in addressing global challenges such as food security, sustainability, and safety. As the world faces escalating demands for efficient food production, equitable distribution, and innovative resource management, AI-driven and ML-driven technologies offer unprecedented opportunities for revolutionizing food analysis across the entire supply chain—from cultivation and processing to quality control and consumption. This Special Issue seeks to explore cutting-edge advancements, methodologies, and applications of AI and ML in food science, with a focus on enhancing precision, scalability, and sustainability. This Special Issue invites original research, reviews, and case studies on topics including, but not limited to, the following:

  • AI- and ML-enhanced food quality evaluation and safety assessment.
  • Intelligent sensor networks and real-time monitoring.
  • Integration of remote sensing and big data analytics in food supply chains.
  • Instrumental Analysis of Food Ingredients.

By fostering interdisciplinary dialogue, this Special Issue aims to advance the frontier of AI and ML in food science, ultimately contributing to a more secure and sustainable global food ecosystem. I look forward to your submissions.

Dr. Shuxiang Fan
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 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. 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

  • food analysis
  • food quality
  • food safety
  • food authenticity
  • AI
  • machine learning

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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

21 pages, 4050 KiB  
Article
Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms
by Quancheng Liu, Jun Zhou, Zhaoyi Wu, Didi Ma, Yuxuan Ma, Shuxiang Fan and Lei Yan
Foods 2025, 14(14), 2527; https://doi.org/10.3390/foods14142527 - 18 Jul 2025
Viewed by 364
Abstract
Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra [...] Read more.
Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms—Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)—and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Analysis)
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13 pages, 3556 KiB  
Article
Lipidomic Profiling of Edible Japanese Sea Urchins by LC–MS
by Sahana Amai, Kisara Yuki, Siddabasave Gowda B. Gowda, Divyavani Gowda and Shu-Ping Hui
Foods 2025, 14(13), 2268; https://doi.org/10.3390/foods14132268 - 26 Jun 2025
Viewed by 754
Abstract
Sea urchins (Echinoidea) are marine echinoderms commonly consumed as seafood in East Asia. To date, various metabolic components of sea urchins have been analyzed, and their health benefits for humans have also been attracting attention. Lipids are the major biomolecules present [...] Read more.
Sea urchins (Echinoidea) are marine echinoderms commonly consumed as seafood in East Asia. To date, various metabolic components of sea urchins have been analyzed, and their health benefits for humans have also been attracting attention. Lipids are the major biomolecules present in sea urchins. However, the comprehensive lipid profiling of sea urchins is limited. In this study, we aimed to perform the comprehensive lipid profiling of six types of sea urchins using liquid chromatography–mass spectrometry (LC/MS). The application of untargeted lipidomics led to the identification of 281 lipid molecular species in six varieties of fresh sea urchin gonads. Each lipid metabolite was identified based on its retention time and MS/MS fragmentation pattern. The results of the analysis showed the highest abundance of lipid percentage in Kitamurasakiuni (14.3%), followed by Hokuyobafununi (12.4%). In all the analyzed sea urchins, glycerolipids such as triacylglycerols were found to be the most abundant lipid components. Multivariate analysis revealed that Murasakiuni showed a different lipid profile from the other types. Interestingly, the polyunsaturated fatty acid to saturated fatty acid ratios and health-related nutritional indices factors were found to be higher in Hokuyobafununi compared to other varieties. The ω-3 fatty acids, such as docosapentaenoic acid (FA 22:6) and eicosapentaenoic acid (FA 20:5), were also abundant in Hokuyobafununi. Lipids such as ether and N-acyl-type lysophosphatidylethanolamines were detected for the first time in sea urchins. This study highlights the nutritional significance of sea urchins and their potential use in the development of functional foods. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Analysis)
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