2023MethodsXOpen access

Monitoring changes in the volatile fraction of roasted nuts and seeds by using comprehensive two-dimensional gas chromatography and matrix templates

Pilar Manzano, Beatriz Martín-Gómez, Adrián Fuente-Ballesteros, Ana M. Ares, Juan Diego, José Bernal

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Abstract

Static headspace coupled with comprehensive two-dimensional gas chromatography and a flame ionization detector (HS-GC × GC-FID), has been applied to monitor changes in the volatile fraction of commercial edible nuts and seeds (peanuts, almonds, hazelnuts, and sunflower seeds). Effects of the roasting conditions (time, 5-40 min; temperature, 150-170 °C), which were employed under different combinations by using a ventilated oven, on target volatile fraction were examined to identify potential differences in relation to the roasting treatment of raw samples. In addition, reference templates were created, from the HS-GC × GC-FID method, for each of the four food matrices analyzed, and they were applied to characterize the samples according to the presence or absence of volatile compounds. Finally, these templates were successfully employed to make a quick distinction between different roasting conditions.•HS-GC × GC-FID was applied to study the volatile profile of edible nuts and seeds.•Reference templates (GC × GC-FID) were created for each of the four food matrices.•Rapid discrimination between raw and roasted samples was achieved.

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What this paper is about

Static headspace coupled with comprehensive two-dimensional gas chromatography and a flame ionization detector (HS-GC × GC-FID), has been applied to monitor changes in the volatile fraction of commercial edible nuts and seeds (peanuts, almonds, hazelnuts, and sunflower seeds). Effects of the roasting conditions (time, 5-40 min; temperature, 150-170 °C), which were employed under different combinations by using a ventilated oven, on target volatile fraction were examined to identify potential differences in relation to the roasting treatment of raw samples. In addition, reference templates were created, from the HS-GC × GC-FID method, for each of the four food matrices analyzed, and they were applied to characterize the samples according to the presence or absence of volatile compounds. Finally, these templates were successfully employed to make a quick distinction between different roasting conditions.•HS-GC × GC-FID was applied to study the volatile profile of edible nuts and seeds.•Reference templates (GC × GC-FID) were created for each of the four food matrices.•Rapid discrimination between raw and roasted samples was achieved.

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Available abstract

Static headspace coupled with comprehensive two-dimensional gas chromatography and a flame ionization detector (HS-GC × GC-FID), has been applied to monitor changes in the volatile fraction of commercial edible nuts and seeds (peanuts, almonds, hazelnuts, and sunflower seeds). Effects of the roasting conditions (time, 5-40 min; temperature, 150-170 °C), which were employed under different combinations by using a ventilated oven, on target volatile fraction were examined to identify potential differences in relation to the roasting treatment of raw samples. In addition, reference templates were created, from the HS-GC × GC-FID method, for each of the four food matrices analyzed, and they were applied to characterize the samples according to the presence or absence of volatile compounds. Finally, these templates were successfully employed to make a quick distinction between different roasting conditions.•HS-GC × GC-FID was applied to study the volatile profile of edible nuts and seeds.•Reference templates (GC × GC-FID) were created for each of the four food matrices.•Rapid discrimination between raw and roasted samples was achieved.

Key concepts: Roasting, Gas chromatography, Flame ionization detector, Chemistry, Fraction (chemistry), Chromatography, Template, Two-dimensional gas

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Monitoring changes in the volatile fraction of roasted nuts and seeds by using comprehensive two-dimensional gas chromatography and matrix templates — Research Paper | ScholarLens