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Multivariate statistical analysis for the identification of potential seafood spoilage indicators
Kuuliala, L.; Abatih, E.; Ioannidis, A.-G.; Vanderroost, M.; De Meulenaer, B.; Ragaert, P.; Devlieghere, F. (2018). Multivariate statistical analysis for the identification of potential seafood spoilage indicators. Food Control 84: 49-60. https://hdl.handle.net/10.1016/j.foodcont.2017.07.018
In: Food Control. Butterworth Scientific/Elsevier: London. ISSN 0956-7135; e-ISSN 1873-7129
Peer reviewed article  

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Trefwoord
    Marien
Author keywords
    Hierarchical cluster analysis; Intelligent packaging; Principalcomponents analysis; Partial least squares regression analysis;Selected-ion flow-tube mass spectrometry

Auteurs  Top 
  • Kuuliala, L.
  • Abatih, E.
  • Ioannidis, A.-G.
  • Vanderroost, M.
  • De Meulenaer, B.
  • Ragaert, P.
  • Devlieghere, F.

Abstract
    Volatile organic compounds (VOCs) characterize the spoilage of seafood packaged under modified atmospheres (MAs) and could thus be used for quality monitoring. However, the VOC profile typically contains numerous multicollinear compounds and depends on the product and storage conditions. Identification of potential spoilage indicators thus calls for multivariate statistics. The aim of the present study was to define suitable statistical methods for this purpose (exploratory analysis) and to consequently characterize the spoilage of brown shrimp (Crangon crangon) and Atlantic cod (Gadus morhua) stored under different conditions (selective analysis). Hierarchical cluster analysis (HCA), principal components analysis (PCA) and partial least squares regression analysis (PLS) were applied as exploratory techniques (brown shrimp, 4 °C, 50%CO2/50%N2) and PLS was further selected for spoilage marker identification. Evolution of acetic acid, 2,3-butanediol, isobutyl alcohol, 3-methyl-1-butanol, dimethyl sulfide, ethyl acetate and trimethylamine was frequently in correspondence with changes in the microbiological quality or sensory rejection. Analysis of these VOCs could thus enhance the detection of seafood spoilage and the development of intelligent packaging technologies.

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