Explainable AI for enhanced food safety diagnostics
Discover a novel explainable AI framework that integrates Topological Data Analysis (TDA) with deep learning to improve image-based diagnostics for food safety. Achieve greater than 90% accuracy in detecting counterfeits and ensuring brand consistency.
Overview
Mined XAI introduces an innovative solution that combines Topological Data Analysis (TDA) with deep learning to enhance the accuracy and interpretability of image-based diagnostic systems for food safety. This approach is designed to detect targets of interest such as counterfeit products, adulteration, and ensure brand consistency with high precision. The technology utilizes TDA's persistence homology to analyze topological features across various scales, offering a robust, precise, and interpretable model for food safety applications.
Technical specifications
Key features:
- Integration of Topological Data Analysis (TDA) with deep learning for enhanced feature extraction
- Use of persistence homology to study continuity of topological features
- High accuracy (>90%) and low false positive rate (<5%) in detecting counterfeit and adulterated products
- Adaptable and scalable machine learning models suitable for various analytical tasks
- Explainable AI framework that reveals connections between features and datasets
Technology readiness level
Currently at Technology Readiness Level 4, this solution has demonstrated effectiveness in classifying spirits and food products, with ongoing development to create reproducible methods for automated feature extraction and explainable machine learning frameworks. Future validation aims to establish a gold standard accuracy in result identification.
About Mined XAI
Mined XAI provides enterprise-grade explainable artificial intelligence (XAI) solutions designed to transform complex, multi-dimensional data into strategic, actionable insights. Founded in 2020 and headquartered in Dayton, Ohio, the company utilizes a proprietary deep topological modeling approach, originally developed through a DARPA program and refined at a US Air Force research organization. Unlike traditional forecasting systems that often rely on linear assumptions and historical transaction data, Mined XAI’s methodology identifies hidden relationships across various data streams—including demand, inventory, operational, and commercial information—to deliver transparent, defensible, and scalable decision-support models for organizations.
By focusing on explainability, the company helps clients in sectors such as wholesale distribution, manufacturing, and supply chain management address complex challenges in enterprise forecasting, risk modeling, fleet management, and resource optimization. Mined XAI’s solutions are engineered to improve forecast accuracy, detect supply chain risks earlier, and boost overall operational efficiency. The company has demonstrated its market utility through long-term strategic partnerships, notably with Winsupply Inc., which completed a minority investment in Mined XAI in 2025 following a multi-year collaboration focused on leveraging XAI to enhance distribution and logistics performance.
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