Machine learning identification of organic compounds using visible light

TL;DR

This paper presents a machine learning approach that accurately identifies organic compounds using visible light measurements, enabling remote chemical detection without relying on infrared absorption spectra.

Contribution

The study introduces a novel machine learning classifier that identifies organic compounds from visible light refractive index data, expanding optical identification methods beyond infrared techniques.

Findings

High accuracy in classifying organic compounds from visible light data

Effective identification using a single-wavelength measurement

Potential for autonomous material detection applications

Abstract

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols or applications.