| Title: | GC-IMS data on the discrimination between geographic origins of olive oils |
| Author(s): | Christmann J; Rohn S; Weller P; |
| Address: | "Institute for Instrumental Analytics and Bioanalysis, Mannheim University of Applied Sciences, Paul-Wittsack-Strasse 10, 68163 Mannheim, Germany. Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany. Department of Food Chemistry and Analysis, Institute of Food, Technology and Food Chemistry, Technische Universitat Berlin, TIB 4/3-1, Gustav-Meyer-Allee 25, 13355 Berlin, Germany" |
| DOI: | 10.1016/j.dib.2022.108730 |
| ISSN/ISBN: | 2352-3409 (Electronic) 2352-3409 (Linking) |
| Abstract: | "Gas chromatography hyphenated with ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for sensitive and selective detection of volatile organic compounds. It is commonly used for non-target screening (NTS) of complex sample materials, such as food products. Resulting spectra are used as 'fingerprints' for multivariate chemometric data analysis to extract information. This has been successfully applied in the field of food fraud detection in several published studies. The presented dataset contains GC-IMS measurements of extra virgin olive oil samples from Spain, Italy, and Greece. It allows classification and class modelling to differentiate geographic origins and was used in the associated publication gc-ims-tools, a new Python package for chemometric analysis of GC-IMS data (https://doi.org/10.1016/j.foodchem.2022.133476) as an example to demonstrate the functionality" |
| Keywords: | Chemometrics Food fraud detection Headspace Non-target screening; |
| Notes: | "PubMed-not-MEDLINEChristmann, Joscha Rohn, Sascha Weller, Philipp eng Netherlands 2022/11/26 Data Brief. 2022 Nov 8; 45:108730. doi: 10.1016/j.dib.2022.108730. eCollection 2022 Dec" |
