A simple DNN regression for the chemical composition in essential oil

TL;DR

This paper explores the use of simple deep neural network regressors to predict essential oil properties from chemical composition, highlighting challenges like overfitting due to limited data.

Contribution

It introduces a straightforward DNN regression approach for chemical composition prediction in essential oils, an area with limited prior research.

Findings

Models trained effectively despite small datasets

All three DNN regressors showed promising results

Overfitting was observed due to limited data

Abstract

Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little attention, with the exception of a few prior studies. In this study, we configured three simple DNN regressors to predict essential oil property based on chemical composition. Despite showing overfitting due to the small size of dataset, all models were trained effectively in this study.