# A comparative analysis of vibrational spectra for odorant classification

## TL;DR

This study uses vibrational spectra to classify flower scents, showing that certain molecular vibrations are linked to specific odors like garlic or sweetness.

## Contribution

The paper introduces a method using vibrational spectra and spectral clustering to classify odorants based on their molecular characteristics.

## Key findings

- Vibrational spectra in the fingerprint region reveal distinct molecular characteristics linked to specific odors.
- Infrared spectroscopy outperformed Raman and vibrational density of states in classifying compound odors.
- Functional groups with heteroatoms (N and S) or conjugated systems are associated with distinctive odor clusters.

## Abstract

The olfactory characteristics of flowers have diverse biological and industrial applications. The physicochemical properties of odorants can reveal information about the molecules responsible for a flower’s scent. In particular, the distinct molecular characteristics are revealed by vibrational spectra in the fingerprint region. In this study, we calculate the vibrational spectra of compounds from two flower families: Orchidaceae and Apocynaceae. We classified these molecules based on their vibrational spectra using a spectral clustering algorithm and analyzed the most representative vibrational modes. This method effectively clusters molecules with distinctive odors, such as garlic, decay, and sweetness, which are associated with compounds containing heteroatoms (N and S) or conjugated systems. The clustering obtained by infrared spectroscopy reflected a better classification by compound odor compared to Raman spectroscopy and vibrational density of states. Hence, some vibrational modes, particularly those associated with specific functional groups, may play a major role in odor discrimination. These findings suggest that vibrational spectra can offer odor-relevant information for olfactory categorization.