VOC Analyzer for Lung Cancer

Goal:
Engineer a handheld breath analyzer that detects volatile organic compounds (VOCs) linked to early-stage lung cancer using a hybrid sensor array and AI classification.

Impact:
Lung cancer is often detected only after spreading, when survival rates drop below 20%. However, changes in VOC composition can occur months to years earlier. Current GC-MS testing is prohibitively expensive and confined to laboratories. This project translates that precision into a portable device, potentially transforming screening into a simple breath test available at clinics or homes.

General Parts & Functionality:
Combines MOS, PID, and NDIR sensors in a controlled airflow system to detect unique VOC signatures. A microcontroller processes signals and sends results to a smartphone app for ML-based analysis.

Roles Involved:

• Hardware: Assemble and calibrate sensors, flow control, and power system.
• Firmware: Manage sampling sequences and signal collection.
• AI/ML: Develop VOC pattern recognition and classification models.
• CAD Design: Design hygienic, modular handheld enclosures.
• Research/Biology: Correlate detected compounds with clinical lung cancer profiles.

Benefits:
Enables early, painless detection before tumors form, reduces healthcare costs, and provides a scalable tool for global lung health screening.