Oil fingerprint identification technology using a simplified set of biomarkers selected based on principal component difference

Gas chromatography-mass spectrometry (GC-MS), which can separate and quantify thousands of individual petroleum biomarker compounds, is generally acknowledged as the most powerful technique for oil fingerprinting nowadays. Traditional oil fingerprint studies employ the whole suite of biomarkers measured in chromatographic analysis, which is prone to introducing ambiguous variables in the whole set and being time and labour intensive. To extract the most representative and meaningful indicators for the oil fingerprinting and identification, this paper proposes a method based on principal component difference to select a simplified set of biomarkers, providing the possibility of faster elution and analysis procedures. For the purpose of further verifying the reliability and accuracy of our method, identification simulation experiments including principal component analysis (PCA) spatial clustering, hierarchical clustering, and generalized regression neural network are carried out with the whole set and the simplified set of biomarkers, respectively. All the results and analyses demonstrate that the simplified set of biomarkers selected by our proposed method can achieve almost the same or even better identification results than those of the whole set of biomarkers.