Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6631394 | Fuel | 2018 | 8 Pages |
Abstract
Chemometric analysis was used to classify different oils based on the m/z 256 mass chromatogram. Three hundred oil samples comprised of 12 LFOs, 104 HFOs (13 weathered fuel oils), and 184 crude oil samples (63 weathered crude oils) were analyzed by GC-MS, and the m/z 256 mass chromatogram was chosen for classification. After normalization, the m/z 256 mass chromatograms were aligned using the correlation optimized warping (COW) method with segment lengths of 50 data points and slack parameters of 4 data points. They were then analyzed by principal component analysis (PCA). The score graphs of the PCs revealed that there was a good discriminant for HFOs, LHOs and oils. According to the changing tendency of the correct sample percentage, five PCs were chosen for the linear discriminant analysis (LDA). A good model with three discriminant equations for HFOs, LFOs and oils was obtained. For the training set with 172 samples, the correct percentage reached 100%, and 99% was obtained for the test set with 128 samples. This study proved that the COW-PCA-LDA method based on the m/z 256 mass chromatogram can be used in oil fingerprinting identification, especially for oil type classification.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Peiyan Sun, Kaiwen Bao, Haoshuai Li, Fujuan Li, Xinping Wang, Lixin Cao, Guangmei Li, Qing Zhou, Hongxia Tang, Mutai Bao,