The Study On Lubricating Oil Fingerprints By Using Microscopic Confocal Raman Spectra

With the economic globalization and the rapid development of the marine transportation, the trade of crude oil and refined oil products continues to grow, oil spill accident to happen from time to time. And, the pollution prevention attracts more and more attention, in which environmental pollution caused by lube oil effluent should not be ignored. On the other hand, lubricating oil is an important element of energy conservation and emissions reduction. The performance and appearance of different lubricating oil products may be similar to each other, but the component of the base oil and additive is not always the same. If the different lubricating oil products were mixed, the lubrication performance often drops significantly. In this sense, lubricating oil fingerprint study is of great significance for distinguishing between different products and for traceability of lube oil effluent. Compared with Infrared spectrum, gas chromatography and other identification methods, Raman spectroscopy is convenient, causes no sample damage, and has been widely used in many fields.In this thesis, microscopic confocal Raman spectroscopy was used to explore the Raman fingerprint characteristics of seven kinds of lubricating oil and three kinds of additives. Chemical fingerprints containing both fluorescence and Raman characteristics were collected, converted to vector points in multi-dimensional space, respectively.After standardized processing, all the chemical fingerprint spectra were used for hierarchical cluster analysis performed with Euclidean, the4th Minkowski distance criterion, and the furthest adjacent element, the median method. The results showed that all the lubricating oil and additive samples can be correctly clustered. Then, the fingerprints of lubricating oil and additives in the ranges of1200-1800cm-1,1800-3500cm-1were used for Bayes discriminant analysis, and identifying the correct rates were found to be96.67%and100%, respectively. Raman data in the range of2700-3500cm-1are believed to contain abundant fingerprint information, and more suitable for the discriminant analysis.Further, principal component analysis of the sample data showed that the cumulative contribution rate of the first three principal components were99.22%, in which the contribution rate of the1st,2nd,3rd principal component characteristics were 81.09,14.37and3.76%, respectively.Furthermore, the fluorescence information of the samples was deducted by using6th polynomial fitting method so that the fingerprint spectra mainly contain Raman information, after which all the chemical fingerprint spectra were used for hierarchical cluster analysis performed with block, the1st Minkowski distance criterion, and the ward method. The results showed that the seven kinds of lubricating oil and the three kinds of additive samples can also be correctly clustered.The research showed that, by using Raman fingerprint, the identification of lubricating oil and additives products is convenient, which can provide important theoretical basis and practical reference for quickly identification and fingerprint database construction of lubricating oil products.