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- W3089166734 abstract "The volatile fraction of hazelnuts encrypts information about: cultivar/geographical origin, post-harvest treatments, oxidative stability and sensory quality. However, sensory features could be buried under other dominant chemical signatures posing challenges to an effective classification based on pleasant/unpleasant notes. Here a novel workflow that combines Untargeted and Targeted (UT) fingerprinting on comprehensive two-dimensional gas-chromatographic patterns is developed to discriminate spoiled hazelnuts from those of acceptable quality. By flash-profiling, six hazelnut classes are defined: Mould, Mould-rancid-solvent, Rancid, Rancid-stale, Rancid-solvent, and Uncoded KO. Chromatographic fingerprinting on composite 2D chromatograms from samples belonging to the same class (i.e., composite class-images) enabled effective selection of chemical markers: (a) octanoic acid that guides the sensory classification being positively correlated to mould; (b) ƴ-nonalactone, ƴ-hexalactone, acetone, and 1-nonanol that are decisive to classify OK and rancid samples; (c) heptanoic and hexanoic acids and ƴ-octalactone present in high relative abundance in rancid-solvent and rancid-stale samples." @default.
- W3089166734 created "2020-10-01" @default.
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- W3089166734 date "2021-03-01" @default.
- W3089166734 modified "2023-10-05" @default.
- W3089166734 title "An effective chromatographic fingerprinting workflow based on comprehensive two-dimensional gas chromatography – Mass spectrometry to establish volatiles patterns discriminative of spoiled hazelnuts (Corylus avellana L.)" @default.
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- W3089166734 doi "https://doi.org/10.1016/j.foodchem.2020.128135" @default.
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