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Application of Artificial Intelligence in Food Detection
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Application of Artificial Intelligence in Food Detection

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

Deadline for manuscript submissions: 16 July 2026 | Viewed by 768

Special Issue Editors

Dr. Xiaofeng Xue

E-Mail Website
Guest Editor
Apicultural Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100093, China
Interests: food analysis; machine learning; food detection; authenticity identification; food quality; development of detection methods
Dr. Yitian Xu

E-Mail Website
Guest Editor
College of Science, China Agricultural University, Beijing 100083, China
Interests: artificial intelligence; machine learning; feature optimization; target recognition; food detection
Dr. Fei Pan

E-Mail Website
Guest Editor
State Key Laboratory of Resource Insects, Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
Interests: machine learning; food detection; authenticity identification; food quality; artificial intelligence; material design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce the launch of a new Special Issue in Foods, entitled “Application of Artificial Intelligence in Food Detection.”

The global food system continues to grapple with persistent and evolving challenges related to safety, authenticity, and quality assurance. In this context, artificial intelligence (AI) and machine learning (ML), especially advancements in deep learning, computer vision, and spectral data analysis, have the potential to revolutionize how we detect, monitor, and validate food attributes throughout the entire supply chain.

This Special Issue aims to showcase high-quality original research articles and comprehensive reviews, exploring the integration of cutting-edge AI-driven methods into key areas of food analysis. The topics of interest include, but are not limited to, the following:

  • Detection of food adulteration and fraud.
  • Verification of geographical and botanical origin.
  • Non-destructive quality grading and shelf-life prediction.
  • Rapid identification of pathogens and contaminants.
  • Interpretation of complex spectral or imaging data for real-time decision-making.
  • Interpretability analysis of food detection.

Dr. Xiaofeng Xue
Dr. Yitian Xu
Dr. Fei Pan
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-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

  • artificial intelligence
  • food authentication
  • spectroscopic analysis
  • quality control
  • computer vision
  • food traceability

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

Published Papers (1 paper)

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Research

21 pages, 1850 KB  
Article
A Validation-Driven Explainable Deep Ensemble Framework for Image-Based Saffron Adulteration Detection
by Syed Nisar Hussain Bukhari and Kingsley A. Ogudo
Foods 2026, 15(10), 1661; https://doi.org/10.3390/foods15101661 - 10 May 2026
Viewed by 308
Abstract
Saffron (Crocus sativus L.), one of the world’s most valuable spices, is highly vulnerable to adulteration due to its premium market price and the limitations of conventional analytical methods for rapid, non-destructive authentication. Although recent deep learning-based approaches have reported promising accuracy, [...] Read more.
Saffron (Crocus sativus L.), one of the world’s most valuable spices, is highly vulnerable to adulteration due to its premium market price and the limitations of conventional analytical methods for rapid, non-destructive authentication. Although recent deep learning-based approaches have reported promising accuracy, many rely on single models or naïve ensembles and lack rigorous validation and statistical reliability assessment. This study proposes a validation-driven and explainable deep ensemble framework for image-based saffron adulteration detection. Multiple pretrained convolutional neural networks (DenseNet169, ResNet50, and VGG16) are integrated using a validation-driven weighted ensemble strategy in which fusion weights are computed exclusively from validation performance within the training folds and fixed prior to evaluation on the held-out fold, thereby preventing information leakage between model selection and performance assessment. The proposed framework achieved 98.61% classification accuracy, 98.17% F1-score, and 98.61% AUC, outperforming the best individual base model by up to 1.4% in F1-score. Stratified five-fold cross-validation demonstrated stable performance, with a mean accuracy of 97.81% ± 0.53, confirming robustness across data splits. Statistical validation using McNemar’s test (p < 0.05) and 5 × 2 cross-validated significance testing verified that performance improvements over constituent models are statistically reliable. Grad-CAM-based explainability and background-invariance analysis further demonstrated that predictions are driven primarily by intrinsic filament-level characteristics, with only a marginal (~0.9%) performance reduction under ROI-cropped evaluation. The proposed framework provides a robust, interpretable, and statistically validated solution for saffron authentication and offers methodological insights for reliable image-based food adulteration detection under limited data conditions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Food Detection)
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