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      Characteristic fingerprints and volatile flavor compound variations in Liuyang Douchi during fermentation via HS-GC-IMS and HS-SPME-GC-MS

      , , , , ,
      Food Chemistry
      Elsevier BV

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          Recent progress in food flavor analysis using gas chromatography–ion mobility spectrometry (GC–IMS)

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            Re-investigation on odour thresholds of key food aroma compounds and development of an aroma language based on odour qualities of defined aqueous odorant solutions

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              Target vs spectral fingerprint data analysis of Iberian ham samples for avoiding labelling fraud using headspace - gas chromatography-ion mobility spectrometry.

              The data obtained with a polar or non-polar gas chromatography (GC) column coupled to ion mobility spectrometry (IMS) has been explored to classify Iberian ham, to detect possible frauds in their labelling. GC-IMS was used to detect the volatile compound profile of dry-cured Iberian ham from pigs fattened on acorn and pasture or on feed. Due to the two-dimensional nature of GC-IMS measurements, great quantities of data are obtained and an exhaustive chemometric processing is required. A first approach was based on the processing of the complete spectral fingerprint, while the second consisted of the selection of individual markers that appeared throughout the spectra. A classification rate of 90% was obtained with the first strategy, and the second approach correctly classified all Iberian ham samples according to the pigs' diet (classification rate of 100%). No significant differences were found between the GC columns tested in terms of classification rate.

                Author and article information

                Journal
                Food Chemistry
                Food Chemistry
                Elsevier BV
                03088146
                November 2021
                November 2021
                : 361
                : 130055
                Article
                10.1016/j.foodchem.2021.130055
                34023693
                7c7e34d9-4836-40e8-8d84-5955ddea27c3
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

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