Browsing by Author "Ince, Volkan"

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    AI-Driven Food Safety Risk Prediction: a Transformer-Based Approach With RASFF Database
    (Emerald Group Publishing Ltd, 2025) Sari, Omer Faruk; Bader-El-Den, Mohamed; Leadley, Craig; Esmeli, Ramazan; Mohasseb, Alaa; Ince, Volkan
    PurposeThis study aims to enhance food safety risk classification by systematically evaluating the effectiveness of machine learning and transformer-based AI models using the RASFF dataset. While AI-powered surveillance has gained attention, most research focuses on isolated applications of machine learning without systematically comparing them to advanced transformer architectures. This research addresses this gap by evaluating the predictive accuracy and interpretability of the model to ensure that AI-driven risk assessments are both effective and transparent for regulation.Design/methodology/approachThe study employs a structured evaluation framework in which traditional machine learning models, including logistic regression, support vector machines and random forest, are compared with advanced transformer-based models such as BERT, RoBERTa and BioBERT. Additionally, explainable AI (XAI) techniques, particularly SHAP analysis, enhance the interpretability of the models by identifying the key food safety risk factors that influence classification decisions.FindingsTransformer-based models significantly outperform traditional machine learning methods, with RoBERTa achieving the highest classification accuracy. The SHAP analysis highlights key hazards salmonella, aflatoxins, listeria and sulphites as primary factors in serious risk classification, while procedural attributes like certification status and temperature control are less impactful. Despite improvements in accuracy, computational efficiency and scalability remain challenges for real-world deployment.Originality/valueWe introduce a novel end-to-end AI framework that integrates state-of-the-art transformers with Explainable AI for the RASFF database. By integrating explainable AI, it bridges the gap between AI research and regulatory implementation and provides actionable insights for policymakers and industry stakeholders to improve risk management and early hazard detection.
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    Predicting Spoilage Intensity Level in Sausage Products Using Explainable Machine Learning and GAN-Based Data Augmentation
    (Springer, 2025) Ince, Volkan; Bader-El-Den, Mohamed; Esmeli, Ramazan; Maurya, Lalit; Sari, Omer Faruk
    Spoilage in processed meat products, such as poultry and pork sausages, presents significant challenges for food safety, quality control, and waste reduction. This study presents a machine learning-based framework to classify spoilage intensity levels using sensory, physicochemical, and microbiological features. To overcome limitations caused by small datasets, we applied synthetic data augmentation using a tabular variational autoencoder (TVAE) to generate high-fidelity samples that enhance model generalization. Additionally, traditional oversampling techniques such as SMOTE and ADASYN were employed for comparative purposes and to further address class imbalance issues. Seven machine learning classifiers were evaluated logistic regression, support vector machine, K-nearest neighbors, random forest, gradient boosting, voting classifier, and multilayer perceptron. The best classification performance was achieved when models were trained on GAN-based synthetic data and tested on real samples. For poultry sausage spoilage prediction, the gradient boosting classifier reached the highest accuracy of 97%. For pork sausages, random forest achieved the highest accuracy of 95%. These results confirm the effectiveness of data augmentation in improving predictive robustness. To ensure model transparency, we integrated explainable AI techniques SHAP and LIME into the pipeline. These analyses revealed that sampling time, CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document} concentration, pH, and microbial species such as Lactobacillus curvatus and Leuconostoc carnosum were among the most influential features in spoilage prediction. The combination of synthetic data generation and interpretable machine learning enables a reliable, scalable, and explainable approach to spoilage classification. This methodology has strong potential for enhancing quality control systems in the meat industry while reducing waste and improving safety along the food supply chain.