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Updated: May 27, 2026

Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
A comparison of warmed-over flavour in pork by sensory analysis, GC/MS and the electronic nose
M G O'Sullivan1, D V Byrne, M T Jensen
1Department of Dairy and Food Science, The Royal Veterinary and Agricultural University, DK-1958, Frederiksberg C, Copenhagen, Denmark.
Abstract:
Pork muscle samples (M. longissimus dorsi and M. psoas major) were obtained from pigs given one of 4 dietary treatments, (i) control diet, (ii) supplemental iron (300 mg iron (II) sulphate/kg feed), (iii) supplemental vitamin E (200 mg dl-α-tocopheryl acetate/kg of feed) and (iv) supplemental vitamin E+supplemental iron. Warmed-over flavour (WOF) was evaluated by a trained sensory panel (n=8) for the four treatments cooked and refrigerated at 4 °C for up to 5 days. Gas chromatography mass spectrometry (GC/MS) and Electronic nose analysis was performed on a subset of the full design which included samples of M. longissimus dorsi, treatments (ii) and (iii) and M. psoas major with treatment (i) for 0 days of WOF development. Day 5 of WOF development was included in the subset and represented by samples of M. longissimus dorsi, treatment (iv) and M. psoas major, treatments (ii) and (iii). Bi-linear modeling was used to determine the correlation of GC/MS and electronic nose data to sensory data. Also, the reproducibility and reliability of electronic nose data was evaluated by repeating the analysis of samples in a different laboratory and with a time difference of approximately 11 months. Mean-centring was used to normalise the data from these two different electronic noise data sets. GC/MS data correlated to sensory data with specific compounds (e.g., pentanal, 2-pentylfuran, octanal, nonanal, 1-octen-3-ol and hexanal), proving to be good indices of oxidation in cooked samples of M. longissimus dorsi and M. psoas major. Electronic nose data correlated to sensory data and separated the sensory variation. The reproducibility of this data was high with the second set of samples being predictive of the first set.
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