Gas chromatography ion mobility spectrometry (GC-IMS) dataset of honey samples with different botanical origins

被引：1

作者：

Christmann, Joscha ^{[1
,2
]}

Rohn, Sascha ^{[2
]}

Weller, Philipp ^{[1
]}

机构：

[1] Mannheim Univ Appl Sci, Inst Instrumental Analyt & Bioanal, Paul Wittsack Str 10, D-68163 Mannheim, Germany

[2] Tech Univ Berlin, Inst Food Technol & Food Chem, Dept Food Chem & Anal, TIB 4-3-1,Gustav Meyer Allee 25, D-13355 Berlin, Germany

来源：

DATA IN BRIEF | 2024年 / 54卷

关键词：

Non-target screening; Volatile organic compounds; Food fraud detection; Chemometrics; Multivariate data analysis;

D O I：

10.1016/j.dib.2024.110532

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non -target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys ( Robinia pseudoacacia ), 19 canola honeys ( Brassica napus ) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x . (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-nc/4.0/ )

引用

页数：5

共 4 条

[1] Finding features-variable extraction strategies for dimensionality reduction and marker compounds identification in GC-IMS data [J].