RT journal article
T1 Rapid Classification of Petroleum Waxes: A Vis-NIR Spectroscopy and Machine Learning Approach
A1 Barea Sepúlveda, Marta
A1 Pérez Calle, José Luis
A1 Ferreiro González, Marta
A1 Palma Lovillo, Miguel
A2 Química Analítica
K1 discrimination
K1 food waxes
K1 machine learning
K1 paraffins
K1 petroleum-derived products
K1 random forest
K1 spectralprint
K1 support vector machine
K1 visible–near-infrared spectroscopy
AB 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) and random forest (RF), were applied to the spectroscopic data for precise classification. Results from the HCA and PCA demonstrated a clear trend of grouping the wax samples according to their chemical composition. In combination with five-fold cross-validation (CV), the SVM models accurately classified all samples as either macrocrystalline or microcrystalline wax during the test phase. Similar high-performance outcomes were observed with RF models along with five-fold CV, enabling the identification of specific wavelengths that facilitate discrimination between the wax types, which also made it possible to select the wavelengths that allow discrimination of the samples to build the characteristic spectralprint of each type of petroleum wax. This research underscores the effectiveness of the proposed analytical method in providing fast, environmentally friendly, and cost-effective quality control for waxes. The approach offers a promising alternative to existing techniques, making it a viable option for automated quality assessment of waxes in food industrial applications.
PB Multidisciplinary Digital Publishing Institute (MDPI)
SN 2304-8158
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31639
UL http://hdl.handle.net/10498/31639
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 12-nov-2026