AI-Driven VOC Sensor System for Early Allergen Detection and Proactive Allergy Management

The early detection of airborne allergens is essential for individuals with allergies and respiratory sensitivities, as exposure to allergenic compounds can trigger adverse health effects. This paper presents an advanced allergen monitoring system that integrates Volatile Organic Compound (VOC) sensors with Artificial Intelligence (AI)-driven scent recognition to enable real-time detection, classification, and mitigation of airborne allergens. The system employs a multi-sensor array comprising Metal Oxide Semiconductor (MOS) sensors, Photoionization Detectors (PID), and Electrochemical sensors to detect and analyze allergenic VOCs such as floral compounds, mold spores, and food-based triggers. Calibration using Gas Chromatography-Mass Spectrometry (GC-MS) ensures high specificity and sensitivity in VOC identification, providing a reliable basis for sensor accuracy. To enhance classification and predictive accuracy, AI-based machine learning models, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNNs), and Long Short- Term Memory (LSTMs), process sensor data to detect allergenic VOC patterns while filtering environmental noise. The system also features a mobile application that delivers real-time exposure risk assessments and personalized mitigation strategies, empowering users to take preventive actions. This paper details the methodology, system architecture, real-world applications, and future research directions, emphasizing the potential of AI-driven VOC sensing technology for proactive allergen management and improved quality of life for allergy-sensitive individuals.