A simple DNN regression for the chemical composition in essential oil
This work addresses a gap in predicting essential oil properties for applications in chemistry or pharmacology, but it is incremental as it applies existing methods to a new data type.
The authors tackled the problem of predicting essential oil properties from chemical composition using deep neural networks, achieving effective training of three models despite overfitting due to a small dataset.
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.