Predictive Modeling of Empty Puparia Age Using Cuticular Hydrocarbon Concentrations: A Machine Learning Approach

This dataset comprises concentration measurements of four different cuticular hydrocarbons (Pentacosane - C25, Heptacosane - C27, Octacosane - C28, and Nonacosane - C29) extracted from empty puparia of Calliphora vicina. These puparia were stored in both paper towel and soil pupation mediums under controlled laboratory conditions. The measurements were taken at various ages of the empty puparia, with age recorded in days and concentrations measured in nanograms per microliter (ng/µL).

Each row in the dataset represents a specific observation, detailing the age of the empty puparia along with the concentrations of the four hydrocarbons. These concentrations are expressed in units of ng/µL, indicating the quantity of each hydrocarbon present in a microliter of the extraction solution.

For analysis, two machine learning models, Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost), were employed to accommodate the unique characteristics of the dataset. These models utilized the concentrations of n-C25, n-C27, n-C28, and n-C29 hydrocarbons to predict the age of the empty puparia.

The dataset is supplemented with three files: one Excel sheet containing concentration measurements of empty puparia investigated over 180 days in laboratory conditions, and two Word files containing R script codes for implementing the SVM and XGBoost machine learning algorithms to estimate the age of the empty puparia.