Classification of Lubricating Oil Types Using Mid-Infrared Spectroscopy Combined with Linear Discriminant Analysis–Support Vector Machine Algorithm

• Published in: Lubricants Vol. 11; no. 6; p. 268
• Main Authors: Xu, Jigang, Liu, Shujun, Gao, Ming, Zuo, Yonggang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Additives; Algorithms; Calibration; Classification; Crude oil; Discriminant analysis; Factor analysis; Friction; Genetic algorithms; Identification and classification; Infrared analysis; Infrared spectra; Infrared spectroscopy; Kennard–Stone algorithm; LDA-SVM; Lubricants & lubrication; lubricating oil; Lubricating oils; Machine learning; Methods; mid-infrared spectra; Optimization; Pattern recognition; Principal components analysis; Spectroscopic analysis; Spectrum analysis; Sulfur content; Support vector machines; Verification; China
• ISSN: 2075-4442; 2075-4442
• DOI: 10.3390/lubricants11060268

To realize the classification of lubricating oil types using mid-infrared (MIR) spectroscopy, linear discriminant analysis (LDA) was used for the dimensionality reduction of spectrum data, and the classification model was established based on the support vector machine (SVM). The spectra of the samples were pre-processed by interval selection, Savitzky–Golay smoothing, multiple scattering correction, and normalization. The Kennard–Stone algorithm (K/S) was used to construct the calibration and validation sets. The percentage of correct classification (%CC) was used to evaluate the model. This study compared the results obtained with several chemometric methods: PLS-DA, LDA, principal component analysis (PCA)-SVM, and LDA-SVM in MIR spectroscopy applications. In both calibration and verification sets, the LDA-SVM model achieved 100% favorable results. The PLS-DA analysis performed poorly. The cyclic resistance ratio (CRR) of the calibration set was classified via the LDA and PCA-SVM analysis as 100%, but the CRR of the verification set was not as good. The LDA-SVM model was superior to the other three models; it exhibited good robustness and strong generalization ability, providing a new method for the classification of lubricating oil types by MIR spectroscopy.