title: A statistical estimation approach for quantitative concentrations of compounds lacking authentic standards/surrogates based on linear correlations between directly measured detector responses and carbon number of different functional groups.
abstract: A statistical approach was investigated to estimate the concentration of compounds lacking authentic standards/surrogates (CLASS). As a means to assess the reliability of this approach, the response factor (RF) of CLASS is derived by predictive equations based on a linear regression (LR) analysis between the actual RF (by external calibration) of 18 reference volatile organic compounds (VOCs) consisting of six original functional groups and their physicochemical parameters ((1) carbon number (CN), (2) molecular weight (MW), and (3) boiling point (BP)). If the experimental bias is estimated in terms of percent difference (PD) between the actual and projected RF, the least bias for 18 VOCs is found from CN (17.9 ± 19.0%). In contrast, the PD values against MW and BP are 40.6% and 81.5%, respectively. Predictive equations were hence derived via an LR analysis between the actual RF and CN for 29 groups: (1) one group consisting of all 18 reference VOCs, (2) three out of six original functional groups, and (3) 25 groups formed randomly from the six functional groups. The applicability of this method was tested by fitting these 29 equations into each of the six original functional groups. According to this approach, the mean PD for 18 compounds dropped as low as 5.60 ± 5.63%. This approach can thus be used as a practical tool to assess the quantitative data for CLASS.
authors.affiliation: Atmospheric Environment Laboratory, Department of Environment & Energy, Sejong University, Seoul 143-747, Republic of Korea.
authors.forename: Yong-Hyun
authors.initials: YH
authors.lastname: Kim
authors.forename: Ki-Hyun
authors.initials: KH
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D002244:Carbon / Q000032:analysis* / Q000737:chemistry*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D016014:Linear Models; D013223:Statistics as Topic; D055549:Volatile Organic Compounds / Q000032:analysis* / Q000737:chemistry*
chemical_list: D055549:Volatile Organic Compounds; D002244:Carbon