The penetration of green sample-preparation techniques in comprehensive two-dimensional gas chromatography

Tranchida, Peter Q.; Maimone, Mariarosa; Purcaro, Giorgia; Dugo, Paola; Mondello, Luigi

doi:10.1016/j.trac.2015.03.011

Cited by 27 publications

(12 citation statements)

References 48 publications

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“…For more thorough information, a series of reviews can be consulted (Adahchour et al, 2006a(Adahchour et al, ,b,c, 2008Tranchida et al, 2004Tranchida et al, , 2007bTranchida et al, , 2015Tranchida et al, , 2016Cortes et al, 2009;Nizio et al, 2012;Cordero et al, 2015;Sampat et al, 2016). The focus of this chapter will be devoted to the penetration of GC Â GC into the lipid analysis field.…”

Section: Applicationsmentioning

confidence: 99%

Theory and Practice of UHPLC and UHPLC–MS

Guillarme

Veuthey

2017

Handbook of Advanced Chromatography /Mass Spectrometry Techniques

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Section: Applicationsmentioning

confidence: 99%

Theory and Practice of UHPLC and UHPLC–MS

Guillarme

Veuthey

2017

Handbook of Advanced Chromatography /Mass Spectrometry Techniques

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“…Headspace (HS) SPME is a solvent‐free sample preparation method, which has had an exponential diffusion in many fields of applications ( i.e . clinical [1,2], environmental [3,4], and food [5–7]) since its introduction in the 1990s [8]. The theory that underlays this extraction technique is based on the equilibrium among a three‐phase system, i.e .…”

Section: Introductionmentioning

confidence: 99%

Exploring multiple‐cumulative trapping solid‐phase microextraction for olive oil aroma profiling

Mascrez

Purcaro

2020

J of Separation Science

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Headspace solid-phase microextraction is a solvent-free sample preparation technique that is based on the equilibrium among a three-phase system, i.e., sample-headspacefiber. A compromise between sensitivity and extraction time is usually needed to optimize the sample throughput, especially when a large number of samples are analyzed, as usually the case in cross-samples studies. This work explores the capability of multiple-cumulative trapping solid-phase microextraction on the characterization of the aroma profiling of olive oils, exploiting the automation capability of a novel headspace autosampler. It was shown that multiple-cumulative solid-phase microextraction has the potential to improve the overall sensitivity and burst the level of information for cross-sample studies by using cumulative shorter extraction times. K E Y W O R D Sextra-virgin olive oil, gas chromatography-mass spectrometry, headspace solid-phase microextraction, multiple-cumulative trapping solid-phase microextraction 1934

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“…Identifications of the VOCs is important for understanding the metabolic pathways involved during response to infection and to guide antibiotic development. To be able to provide this information, we used comprehensive two-dimensional gas chromatography (GC × GC) coupled with time-of-flight mass spectrometry (ToF MS), one of the most powerful tools available for the analysis of volatile metabolites (Tranchida et al 2015). …”

Section: Introductionmentioning

confidence: 99%

Breath metabolome of mice infected with Pseudomonas aeruginosa

et al. 2019

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Introduction The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa. Objectives To determine a discriminatory core of molecules in the “breath-print” of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific “breath-print” signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20–25, 2014. https://doi.org/10.1111/2049–632X.12107). Methods Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model. Results Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups. Conclusions To the best of our knowledge, this is the first study to report on a ‘core’ murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.

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The penetration of green sample-preparation techniques in comprehensive two-dimensional gas chromatography

Cited by 27 publications

References 48 publications

Theory and Practice of UHPLC and UHPLC–MS

Theory and Practice of UHPLC and UHPLC–MS

Exploring multiple‐cumulative trapping solid‐phase microextraction for olive oil aroma profiling

Breath metabolome of mice infected with Pseudomonas aeruginosa

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