RT journal article
T1 Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples
A1 Barea Sepúlveda, Marta
A1 Ferreiro González, Marta
A1 Pérez Calle, José Luis
A1 Fernández Barbero, Gerardo
A1 Ayuso Vilacides, Jesús
A1 Palma Lovillo, Miguel
A2 Química Analítica
A2 Química Física
K1 Gasoline
K1 Research Octane Number
K1 HS-MS eNose
K1 NIRS
K1 Chemometrics
K1 Support Vector Machine
K1 Random Forest
K1 Low-level data fusion
AB In the quality control of flammable and combustible liquids, such as gasoline, both rapid analysis and automated data processing are of great importance from an economical viewpoint for the petroleum industry. The present work aims to evaluate the chemometric tools to be applied on the Headspace Mass Spectrometry (HS-MS eNose) and Near-Infrared Spectroscopy (NIRS) results to discriminate gasoline according to their Research Octane Number (RON). For this purpose, data from a total of 50 gasoline samples of two types of RON-95 and 98-analyzed by the two above-mentioned techniques were studied. The HS-MS eNose and NIRS data were com-bined with non-supervised exploratory techniques, such as Hierarchical Cluster Analysis (HCA), as well as other supervised classification techniques, namely Support Vector Machine (SVM) and Random Forest (RF). For su-pervised classification, the low-level data fusion was additionally applied to evaluate if the combined use of the data increases the scope of relevant information. The HCA results showed a clear clustering trend of the gasoline samples according to their RON with HS-MS eNose data. SVM in combination with 5-Fold Cross-Validation successfully classified 100% of the samples with the HS-MS eNose data set. The RF algorithm in combination with 5-Fold Cross-Validation achieved the best accuracy rate for the test set with the low-level data fusion system. Furthermore, it allowed us to identify the most important features that could define the differences between RON 95 and RON 98 gasoline. On the other hand, using the HS-MS eNose and NIRS low-level data fusion reached better results than those obtained using NIRS data individually, with accuracy rates of 100% in both SVM and RF performances with the test set. In general, the performance of the SVM and RF algorithms was found to be similar.
PB ELSEVIER
SN 0026-265X
YR 2022
FD 2022-01
LK http://hdl.handle.net/10498/25765
UL http://hdl.handle.net/10498/25765
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 07-nov-2026