Authors : Fionna Ananth; Eldin Rino P.; Vijay Prakash R.; Dhanees Surya Jenifer

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/nhdewhn3

Scribd : https://tinyurl.com/4y9erenf

DOI : https://doi.org/10.38124/ijisrt/25nov764

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Abstract : 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.

Keywords : Proactive Allergen Management, VOC Sensors, AI-Driven Scent Recognition, Allergy Detection, Early Intervention, Personalized Alerts.

References :

1. Cowen, Todd & Cheffena, Michael. (2022). Template Imprinting Versus Porogen Imprinting of Small Molecules: A Review of Molecularly Imprinted Polymers in Gas Sensing. International Journal of Molecular Sciences. 23. 9642. 10.3390/ijms23179642. 
2. Oliveira, Aline & Morais, Aniel & Lima, Gabriela & Souza, Rafael & Oliveira Lopes, Luis Cláudio. (2023). Detection of Volatile Organic Compounds (VOCs) in Indoor Environments Using Nano Quadcopter. Drones. 7. 660. 10.3390/drones7110660
3. Zhu, Xiao & Ahmed, Waqar & Schmidt, Kamila & Barroso, Raíssa & Fowler, Stephen & Blanford, Christopher. (2024). Validation of an Electronic VOC
4. Sensor Against Gas Chromatography–Mass Spectrometry. IEEE Transactions on Instrumentation and Measurement. PP. 1-1. 10.1109/TIM.2024.3485428. 
5. Tomić M, Šetka M, Vojkůvka L, Vallejos S. VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials. Nanomaterials (Basel). 2021;11(2):552. Published 2021 Feb 22.  doi:10.3390/nano11020552
6. A, Vishal & G, Aakash & S, Sanchay & T, Preethiya. (2024). DeepAllergy: A Deep Learning Approach for
7. Accurate and Rapid Food Allergen Detection. 22252230. 10.1109/ICACCS60874.2024.10717042
8. Herman, R. A., & Song, P. (2020). Allergen falsedetection using official bioinformatic algorithms. GM crops & food, 11(2), 93–96.  https://doi.org/10.1080/21645698.2019.1709021
9. Pandey, Puneeta & Yadav, Radheshyam. (2018). A Review on Volatile Organic Compounds (VOCs) as Environmental Pollutants: Fate and Distribution. International Journal of Plant and Environment. 4. 10.18811/ijpen.v4i02.2. 
10. Thakur, Uttam & Bhardwaj, Radha & Hazra, Arnab. (2021). Statistical Analysis for Selective Identifications of VOCs by Using Surface Functionalized MoS2 Based Sensor Array. Chemistry Proceedings. 5. 35. 10.3390/CSAC2021-10451. 
11. Isokawa, Teijiro & Sakai, Yusuke & Matsui, Nobuyuki. (2017). A neural network-based odor recognition system. 1-1. 10.1109/ICIEV.2017.8338534. 
12. Grodniyomchai, Boonyawee & Chalapat, Khattiya & Jitkajornwanich, Kulsawasd & Jaiyen, Saichon. (2019). A Deep Learning Model for Odor Classification Using Deep Neural Network. 1-4. 10.1109/ICEAST.2019.8802538.