Potential of the reversed-inject differential flow modulator for comprehensive two-dimensional gas chromatography in the quantitative profiling and fingerprinting of essential oils of different complexity

Cordero, Chiara; Rubiolo, Patrizia; Cobelli, L.; Stani, Gianluca; Miliazza, Armando; Giardina, Matthew; Firor, Roger L.; Bicchi, Carlo

doi:10.1016/j.chroma.2015.09.027

Cited by 39 publications

(22 citation statements)

References 36 publications

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“…Fingerprinting: the potential of GC × GC–MS in such an application field is unprecedented; again, the choice of the modulation approach will depend on the depth of detail required. Even though cryogenic modulation is more powerful than flow modulation, it is noteworthy that the latter did enable the cross‐comparison of over 300 compounds in vetiver oils …”

Section: Discussionmentioning

confidence: 99%

“…Very little has been performed in the GC × GC field, related to essential‐oil fingerprinting. In recent work, Cordero et al used FM GC × GC with dual FID/QMS detection for the fingerprinting of four types of vetiver oil, namely Brazil, Java, Haiti and Bourbon . Chromatograms were generated considering a threshold for peak volumes (> 30,000) and s/n values (> 25).…”

Section: Comprehensive 2d Gc Analysis Of Essential Oils and Perfumesmentioning

confidence: 99%

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Analysis of essential oils through comprehensive two‐dimensional gas chromatography: General utility

Tranchida

Franchina

Mondello

2017

Flavour & Fragrance J

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The first description of a comprehensive two‐dimensional gas chromatography (GC×GC) elucidation of an essential oil appeared in 2000 (J. High Resol. Chromatogr. 2000;23:208‐214); since then, a variety of investigations have been published also involving complex samples related to essential oils, such as perfumes. In many instances, there was no mismatch between the use of GC×GC, and the analytical scope; on the other hand, in some specific cases the use of GC×GC appeared to be less adequate. The present contribution is focused on the application of GC×GC, within the context of essential oil analysis. Perfumes are also tightly related to essential oils, and so are herein considered. Rather than to describe and critically‐review all the research related to such investigations, attention has been devoted to the general analytical usefulness of GC×GC in the aforementioned application fields, over more established approaches, such as conventional GC and heart‐cutting multidimensional GC.

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Section: Discussionmentioning

confidence: 99%

Section: Comprehensive 2d Gc Analysis Of Essential Oils and Perfumesmentioning

confidence: 99%

Analysis of essential oils through comprehensive two‐dimensional gas chromatography: General utility

Tranchida

Franchina

Mondello

2017

Flavour & Fragrance J

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“…The first commercial FM used fixed volume accumulation loop devices obtained with Capillary Flow Technology (CFT) microfluidic plates. They implement both the forward fill/flush (FFF) injection dynamics described by Seeley et al [7] and the reverse fill/flush (RFF) dynamics connoted by a more efficient band re-injection, improved 2D peak widths and symmetry, and effective handling of collection-channel overloading [8][9][10][11][12]. Commercial RFF modulators are available from Agilent Technologies [13] and by Sep-Solve with the FM named Insight™ [14].…”

Section: Introductionmentioning

confidence: 99%

A step forward in the equivalence between thermal and differential-flow modulated comprehensive two-dimensional gas chromatography methods

Stilo

Gabetti

Bicchi

et al. 2020

Journal of Chromatography A

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Comprehensive two-dimensional gas chromatography (GC×GC) based on flow-modulation (FM) is gaining increasing attention as an alternative to thermal modulation (TM), the recognized GC×GC benchmark, thanks to its lower operational cost and rugged performance. An accessible, rational procedure to perform method translation between the two platforms would be highly valuable to facilitate compatibility and consequently extend the flexibility and applicability of GC×GC. To enable an effective transfer, the methodology needs to ensure preservation of the elution pattern, separation power, and sensitivity. Here, a loop-type thermal modulation system with dual detection (TM-GC×GC-MSD/FID) used for the targeted analysis of allergens in fragrances is selected as reference method. Initially, six different columns configurations are systematically evaluated for the flow-modulated counterpart. The set up providing the most consistent chromatographic separation (20 m x 0.18 mm d c x 0.18 μm d f + 1.8 m x 0.18 mm d c x 0.18 μm d f) is further evaluated to assess its overall performance in terms of sensitivity, linearity, accuracy, and pattern reliability. The experimental results convincingly show that the method translation procedure is effective and allows successful transfer of the target template metadata. Additionally, the FM-GC×GC-MSD/FID system is suitable for challenging applications such as the quantitative profiling of complex fragrance materials.

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“…(U)HPLC coupled with mass-spectrometer is now standard instrumentation for such applications [7][8][9][10][11][12], with lesser number of works considering GC-MS [13,14], NMR [15,16], IR [17][18][19], and other analytical techniques (e.g. electronic nose [14], electrophoresis [12,18,20]).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for LC–MS medicinal plants identification

Nazarenko

Kharyuk

Oseledets

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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Potential of the reversed-inject differential flow modulator for comprehensive two-dimensional gas chromatography in the quantitative profiling and fingerprinting of essential oils of different complexity

Cited by 39 publications

References 36 publications

Analysis of essential oils through comprehensive two‐dimensional gas chromatography: General utility

Analysis of essential oils through comprehensive two‐dimensional gas chromatography: General utility

A step forward in the equivalence between thermal and differential-flow modulated comprehensive two-dimensional gas chromatography methods

Machine learning for LC–MS medicinal plants identification

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