title: A regression model for calculating the second dimension retention index in comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.
abstract: Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) system has become a key analytical technology in high-throughput analysis. Retention index has been approved to be helpful for compound identification in one-dimensional gas chromatography, which is also true for two-dimensional gas chromatography. In this work, a novel regression model was proposed for calculating the second dimension retention index of target components where n-alkanes were used as reference compounds. This model was developed to depict the relationship among adjusted second dimension retention time, temperature of the second dimension column and carbon number of n-alkanes by an exponential nonlinear function with only five parameters. Three different criteria were introduced to find the optimal values of parameters. The performance of this model was evaluated using experimental data of n-alkanes (C7-C31) at 24 temperatures which can cover all 0-6s adjusted retention time area. The experimental results show that the mean relative error between predicted adjusted retention time and experimental data of n-alkanes was only 2%. Furthermore, our proposed model demonstrates a good extrapolation capability for predicting adjusted retention time of target compounds which located out of the range of the reference compounds in the second dimension adjusted retention time space. Our work shows the deviation was less than 9 retention index units (iu) while the number of alkanes were added up to 5. The performance of our proposed model has also been demonstrated by analyzing a mixture of compounds in temperature programmed experiments.
authors.affiliation: College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China; The Advanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai 201804, China; The Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai 201804, China. Electronic address: wangbing@ustc.edu.
authors.forename: Bing
authors.initials: B
authors.lastname: Wang
authors.affiliation: College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Shen
authors.affiliation: Bioproduct and Biosystem Engineering, University of Minnesota, Twin Cities, MN 55108, USA.
authors.forename: Aiqin
authors.initials: A
authors.lastname: Fang
authors.affiliation: College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.affiliation: College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Changjun
authors.initials: C
authors.lastname: Jiang
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Institute of Health Sciences, Anhui University, Hefei, Anhui, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
mesh_terms: D000473:Alkanes / Q000032:analysis / Q000737:chemistry; D002244:Carbon / Q000032:analysis / Q000737:chemistry; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D016014:Linear Models; D012015:Reference Standards; D013696:Temperature; D013997:Time Factors
chemical_list: D000473:Alkanes; D002244:Carbon
keywords: GC×GC/TOF–MS; Regression model; Retention index; Retention time; Second dimension retention time