Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach.

ABSTRACT: Petroleum-derived waxes are used in the food industry as additives to provide texture and as coatings for foodstuffs such as fruits and cheeses. Therefore, food waxes are subject to strict quality controls to comply with regulations. In this research, a combination of visible and near-infrared (Vis-NIR) spectroscopy with machine learning was employed to effectively characterize two commonly marketed petroleum waxes of food interest: macrocrystalline and microcrystalline. The present study employed unsupervised machine learning algorithms like hierarchical cluster analysis (HCA) and principal component analysis (PCA) to differentiate the wax samples based on their chemical composition. Furthermore, nonparametric supervised machine learning algorithms, such as support vector machines (SVMs)

SUBMITTER: Barea-Sepulveda M 

PROVIDER: S-EPMC10528079 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature