Study on weathering characteristic of aromatic hydrocarbons specific to unconditional oil based on concentration-resolved-fluorescence spectroscopy

Wang, Chunyan; Huang, Xiaodong; Fan, Xinjian; Yang, Chun; Wang, Zhendi

doi:10.1360/n092017-00391

Cited by 2 publications

(1 citation statement)

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“…The main idea of PCA is to map m‐dimension variables to the p‐dimension, which are new orthogonal variables, also known as principal components. [ 22,23 ]…”

Section: Data Processing Methods For Selecting Biomarkersmentioning

confidence: 99%

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

Zhang

Wang

et al. 2021

Can J Chem Eng

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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.

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“…The main idea of PCA is to map m‐dimension variables to the p‐dimension, which are new orthogonal variables, also known as principal components. [ 22,23 ]…”

Section: Data Processing Methods For Selecting Biomarkersmentioning

confidence: 99%