Untargeted headspace gas chromatography – Ion mobility spectrometry analysis for detection of adulterated honey

Arroyo‐Manzanares, Natalia; García-Nicolás, María; Castell, Ana; Campillo, Natalia; Viñas, Pilar; López-Garcı́a, Ignacio; Hernández‐Córdoba, Manuel

doi:10.1016/j.talanta.2019.120123

Cited by 82 publications

(51 citation statements)

References 37 publications

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“…In the last decade, ion mobility spectrometry (IMS) with radioactive ionization in combination with chemometrics gained popularity in food analysis [20][21][22][23]. Gerhardt et al used headspace GC-IMS (HS-GC-IMS) for the classification of virgin olive oils [20,22] as well as for authenticity control of honey [21].…”

Section: Introductionmentioning

confidence: 99%

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

Brendel

Schwolow

Rohn³

et al. 2020

Anal Bioanal Chem

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For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops.

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Section: Introductionmentioning

confidence: 99%

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

Brendel

Schwolow

Rohn³

et al. 2020

Anal Bioanal Chem

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“…Ion mobility spectrometry (IMS) is a cutting-edge technique that, coupled with gas chromatography (GC), is proving to be a powerful analytical tool in a wide range of research fields, such as foodomics [1,2]. IMS is based on gas phase ion separation inside a drift tube under the influence of a constant electric field at atmospheric pressure.…”

Section: Introductionmentioning

confidence: 99%

Headspace Gas Chromatography Coupled to Mass Spectrometry and Ion Mobility Spectrometry: Classification of Virgin Olive Oils as a Study Case

García-Nicolás

Arroyo‐Manzanares

Arce

et al. 2020

Foods

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Due to its multiple advantages, ion mobility spectrometry (IMS) is being considered as a complementary technique to mass spectrometry (MS). The goal of this work is to investigate and compare the capacity of IMS and MS in the classification of olive oil according to its quality. For this purpose, two analytical methods based on headspace gas chromatography (HS-GC) coupled with MS or with IMS have been optimized and characterized for the determination of volatile organic compounds from olive oil samples. Both detectors were compared in terms of sensitivity and selectivity, demonstrating that complementary data were obtained and both detectors have proven to be complementary. MS and IMS showed similar selectivity (10 out of 38 compounds were detected by HS-GC-IMS, whereas twelve compounds were detected by HS-GC-MS). However, IMS presented slightly better sensitivity (Limits of quantification (LOQ) ranged between 0.08 and 0.8 µg g−1 for HS-GC-IMS, and between 0.2 and 2.1 µg g−1 for HS-GC-MS). Finally, the potential of both detectors coupled with HS-GC for classification of olive oil samples depending on its quality was investigated. In this case, similar results were obtained when using both HS-GC-MS and HS-GC-IMS equipment (85.71 % of samples of the external validation set were classified correctly (validation rate)) and, although both techniques were shown to be complementary, data fusion did not improve validation results (80.95% validation rate).

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“…Among all the listed techniques, the use of IR ( Li et al, 2020 , Su and Sun, 2019 , Chien et al, 2019 ), NMR ( He et al, 2020 , Consonni and Cagliani, 2018 ), IRMS ( Geană et al, 2020 , Xu et al, 2020 , Zhou et al, 2018 ) and chromatography-based techniques ( Arroyo-Manzanares et al, 2019 , Makowicz et al, 2018 , Jandrić et al, 2017 ) has increased a lot the last years. IRMS has been mostly used to detect the addition of exogenous sugars to honey ( Geană et al, 2020 ), but also to classify honey based on their botanical origin ( Xu et al, 2020 ) and on their geographical origin ( Zhou et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Use of elemental profiles to verify geographical origin and botanical variety of Spanish honeys with a protected denomination of origin

et al. 2021

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Untargeted headspace gas chromatography – Ion mobility spectrometry analysis for detection of adulterated honey

Cited by 82 publications

References 37 publications

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

Headspace Gas Chromatography Coupled to Mass Spectrometry and Ion Mobility Spectrometry: Classification of Virgin Olive Oils as a Study Case

Use of elemental profiles to verify geographical origin and botanical variety of Spanish honeys with a protected denomination of origin

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