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GC-IMS data on the discrimination between geographic origins of olive oils
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 us...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679686/ https://www.ncbi.nlm.nih.gov/pubmed/36426046 http://dx.doi.org/10.1016/j.dib.2022.108730 |
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author | Christmann, Joscha Rohn, Sascha Weller, Philipp |
author_facet | Christmann, Joscha Rohn, Sascha Weller, Philipp |
author_sort | Christmann, Joscha |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9679686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96796862022-11-23 GC-IMS data on the discrimination between geographic origins of olive oils Christmann, Joscha Rohn, Sascha Weller, Philipp Data Brief Data Article 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. Elsevier 2022-11-08 /pmc/articles/PMC9679686/ /pubmed/36426046 http://dx.doi.org/10.1016/j.dib.2022.108730 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Christmann, Joscha Rohn, Sascha Weller, Philipp GC-IMS data on the discrimination between geographic origins of olive oils |
title | GC-IMS data on the discrimination between geographic origins of olive oils |
title_full | GC-IMS data on the discrimination between geographic origins of olive oils |
title_fullStr | GC-IMS data on the discrimination between geographic origins of olive oils |
title_full_unstemmed | GC-IMS data on the discrimination between geographic origins of olive oils |
title_short | GC-IMS data on the discrimination between geographic origins of olive oils |
title_sort | gc-ims data on the discrimination between geographic origins of olive oils |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679686/ https://www.ncbi.nlm.nih.gov/pubmed/36426046 http://dx.doi.org/10.1016/j.dib.2022.108730 |
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