AI-Powered Raman SpectroscopyRaman Spectroscopy for Counterfeit Antimalarial Detection
Why Does This Matter?
Global Health Impact
Substandard and counterfeit antimalarial drugs contribute to treatment failure, rising drug resistance, and millions of preventable illnesses worldwide. This project enables rapid, non-destructive chemical verification at the point of care, helping protect vulnerable populations where laboratory testing is unavailable.
Accessibility & Cost
Commercial Raman systems are expensive and infrastructure-heavy. This work shows that affordable hardware and open-source tools can deliver meaningful chemical analysis outside traditional laboratories.
Rapid Field Deployment
The compact, portable design allows fast drug screening in clinics, pharmacies, and field settings without sample preparation or specialized training.
Intelligent Analysis
Machine learning transforms raw Raman spectra into reliable classifications, enabling consistent drug authentication without expert interpretation. This approach improves accuracy, scalability, and usability, making advanced chemical analysis accessible to non-specialists in high-impact environments.
How Does it Work?
Optical Excitation
A laser illuminates the sample, causing most light to scatter elastically and a small fraction to scatter with shifted energy, producing Raman signals used for chemical identification.
Collection of Raman Signal
Backscattered light is gathered by lenses and directed through a diffraction grating, which separates wavelengths so that the detector can record intensity at each Raman shift for analysis.
Detection & Digitization
The CCD captures dispersed Raman light as analog voltages. The microcontroller uses its ADC to convert these analog signals into digital spectral data for further processing.
Machine Learning Classification
The Raspberry Pi runs a trained CNN model on the digitized spectrum, classifying whether the sample is legitimate or counterfeit by comparing learned spectral features in real time.
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