Prediction of Retention Indices and Response Factors of Oxygenates for GC-FID by Multilinear Regression

Research output: Contribution to journalResearch articleContributedpeer-review



The replacement of fossil carbon sources with green bio-oils promotes the importance of several hundred oxygenated hydrocarbons, which substantially increases the analytical effort in catalysis research. A multilinear regression is performed to correlate retention indices (RIs) and response factors (RFs) with structural properties. The model includes a variety of possible products formed during the hydrodeoxygenation of bio-oils with good accuracy (RRF2 0.921 and RRI2 0.975). The GC parameters are related to the detailed hydrocarbon analysis (DHA) method, which is commonly used for non-oxygenated hydrocarbons. The RIs are determined from a paraffin standard (C5–C15), and the RFs are calculated with ethanol and 1,3,5-trimethylbenzene as internal standards. The method presented here can, therefore, be used together with the DHA method and be expanded further. In addition to the multilinear regression, an increment system has been developed for aromatic oxygenates, which further improves the prediction accuracy of the response factors with respect to the molecular constitution (R2 0.958). Both predictive models are designed exclusively on structural factors to ensure effortless application. All experimental RIs and RFs are determined under identical conditions. Moreover, a folded Plackett–Burman screening design demonstrates the general applicability of the datasets independent of method- or device-specific parameters.


Original languageEnglish
Article number133
Number of pages12
Journal Data : open access ʻData in scienceʼ journal
Issue number9
Publication statusPublished - 14 Sept 2022

External IDs

Scopus 85138624845
dblp journals/data/KretzschmarSBW22
WOS 000856289700001
Mendeley 53e26a0a-36fa-3ccd-b453-062ec7bd47b8
ORCID /0000-0002-8928-8340/work/142235817
ORCID /0000-0001-7323-7816/work/142257425



  • response factors, predictive modelling, oxygenated hydrocarbons, detailed hydrocarbon analysis (DHA), flame ionization detector (FID), gas chromatography (GC), retention indices, detailed hydrocarbon analysis (DHA), flame ionization detector (FID), gas chromatography (GC), oxygenated hydrocarbons, predictive modelling, response factors, retention indices