Access to clean water is a pressing challenge affecting
millions
of lives and the aquatic body of the Earth. Sensitive detection of
pollutants such as pesticides is particularly important to address
this challenge. This study reports eco-friendly preparation of the
surface-enhanced Raman scattering (SERS) substrate for machine learning-assisted
detection of pesticides in water. The proposed SERS platform was prepared
on a copy paper by reducing a silver salt using the extract of a natural
plant, Cedrus libani. The fabricated
SERS platform was characterized in detail using scanning electron
microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction,
and X-ray photoelectron spectroscopy. The high-density formation of
silver nanoparticles with an average diameter of 41 nm on the surface
of the paper enabled detection of analytes with a nanomolar level
sensitivity. This SERS capability was used to collect Raman signals
of four different pesticides in water: myclobutanil, phosmet, thiram,
and abamectin. Raman spectra of the pesticides are highly complex,
challenging accurate determination of the pesticide type. To overcome
this challenge and distinguish pesticides, machine learning (ML) approach
was used. The ML-mediated detection of harmful pesticides on a versatile,
green, and inexpensive SERS platform appears to be promising for environmental
applications.