Multivariate statistical analysis for the identification of potential seafood spoilage indicators
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. Identificat...
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ftunivtampere:oai:trepo.tuni.fi:10024/126213 2024-01-07T09:42:09+01:00 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. Tampere University Materials Science 2018-07-21 12 1181852 fulltext https://trepo.tuni.fi/handle/10024/126213 https://doi.org/10.1016/j.foodcont.2017.07.018 en eng 84 0956-7135 https://trepo.tuni.fi/handle/10024/126213 URN:NBN:fi:tty-201802141218 doi:10.1016/j.foodcont.2017.07.018 openAccess 216 Materials engineering article 2018 ftunivtampere https://doi.org/10.1016/j.foodcont.2017.07.018 2023-12-14T00:06:24Z 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. Peer reviewed Article in Journal/Newspaper atlantic cod Gadus morhua Tampere University: Trepo Food Control 84 49 60 |
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Tampere University: Trepo |
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ftunivtampere |
language |
English |
topic |
216 Materials engineering |
spellingShingle |
216 Materials engineering Kuuliala, L. Abatih, E. Ioannidis, A. G. Vanderroost, M. De Meulenaer, B. Ragaert, P. Devlieghere, F. Multivariate statistical analysis for the identification of potential seafood spoilage indicators |
topic_facet |
216 Materials engineering |
description |
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. Peer reviewed |
author2 |
Tampere University Materials Science |
format |
Article in Journal/Newspaper |
author |
Kuuliala, L. Abatih, E. Ioannidis, A. G. Vanderroost, M. De Meulenaer, B. Ragaert, P. Devlieghere, F. |
author_facet |
Kuuliala, L. Abatih, E. Ioannidis, A. G. Vanderroost, M. De Meulenaer, B. Ragaert, P. Devlieghere, F. |
author_sort |
Kuuliala, L. |
title |
Multivariate statistical analysis for the identification of potential seafood spoilage indicators |
title_short |
Multivariate statistical analysis for the identification of potential seafood spoilage indicators |
title_full |
Multivariate statistical analysis for the identification of potential seafood spoilage indicators |
title_fullStr |
Multivariate statistical analysis for the identification of potential seafood spoilage indicators |
title_full_unstemmed |
Multivariate statistical analysis for the identification of potential seafood spoilage indicators |
title_sort |
multivariate statistical analysis for the identification of potential seafood spoilage indicators |
publishDate |
2018 |
url |
https://trepo.tuni.fi/handle/10024/126213 https://doi.org/10.1016/j.foodcont.2017.07.018 |
genre |
atlantic cod Gadus morhua |
genre_facet |
atlantic cod Gadus morhua |
op_relation |
84 0956-7135 https://trepo.tuni.fi/handle/10024/126213 URN:NBN:fi:tty-201802141218 doi:10.1016/j.foodcont.2017.07.018 |
op_rights |
openAccess |
op_doi |
https://doi.org/10.1016/j.foodcont.2017.07.018 |
container_title |
Food Control |
container_volume |
84 |
container_start_page |
49 |
op_container_end_page |
60 |
_version_ |
1787423064702582784 |