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1 H‐ NMR with Multivariate Analysis for Automobile Lubricant Comparison
Author(s) -
Kim Siwon,
Yoon Dahye,
Lee DongKye,
Yoon Changshin,
Kim Suhkmann
Publication year - 2017
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.13471
Subject(s) - lubricant , cluster analysis , identification (biology) , automotive industry , multivariate statistics , multivariate analysis , forensic engineering , computer science , materials science , engineering , statistics , mathematics , composite material , artificial intelligence , biology , botany , aerospace engineering
Identification of suspected automobile‐related lubricants could provide valuable information in forensic cases. We examined that automobile lubricants might exhibit the chemometric characteristics to their individual usages. To compare the degree of clustering in the plots, we co‐plotted general industrial oils that were highly dissimilar with automobile lubricants in additive compositions. 1 H‐ NMR spectroscopy was used with multivariate statistics as a tool for grouping, clustering, and identification of automobile lubricants in laboratory conditions. We analyzed automobile lubricants including automobile engine oils, automobile transmission oils, automobile gear oils, and motorcycle oils. In contrast to the general industrial oils, automobile lubricants showed relatively high tendencies of clustering to their usages. Our pilot study demonstrated that the comparison of known and questioned samples to their usages might be possible in forensic fields.