New Raman Fingerprinting for Pesticide Detection

Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument

Researchers from Sabanci University (Istanbul, Turkey) and Imperial College London (United Kingdom) have unveiled a new method for detecting pesticide residues using a custom-built Raman spectroscopy system paired with machine learning.

The study, published in Biosensors, introduces a comprehensive fingerprinting approach for 14 widely used pesticides (many of which are banned but still found in agricultural exports).

Using a Cobolt 08-NLD 785 nm laser with a maximum power of 400 mW, the team developed a high-resolution spectral library that significantly reduces fluorescence interference, outperforming traditional 532 nm systems. The system was tested on real-world samples like cucumbers, peppers, and wheat flour, demonstrating its ability to detect trace pesticide residues with minimal sample preparation.

The fingerprint library has been made publicly available, paving the way for scalable applications in food safety.

Schematic diagram of the developed house Raman instrument showing the Cobolt 08-NLD 785 nm laser, the system (fiber) that carries the light from the laser source to other parts of the system (Biosensors 2025, 15, 168, page 7).