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Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression

Lotta Kuuliala (UGent) , Marc Sader, Antonio Mattia Solimeo, Raul Perez Fernandez (UGent) , Mike Vanderroost (UGent) , Bernard De Baets (UGent) , Bruno De Meulenaer (UGent) , Peter Ragaert (UGent) and Frank Devlieghere (UGent)
(2019) INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY. 303. p.46-57
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Abstract
The development of quality monitoring systems for perishable food products like seafood requires extensive data collection under specified packaging and storage conditions, followed by advanced data analysis and interpretation. Even though the benefits of using volatile organic compounds as food quality indices have been recognized, few studies have focused on real-time quantification of the seafood volatilome and subsequent systematic identification of the most important spoilage indicators. In this study, spoilage of Atlantic salmon (Salmo salar) stored under modified atmospheres (% CO2/O-2/N-2) and air was characterized by performing multivariate statistical analysis and augmented ordinal regression modelling for data collected by microbiological, chemical and sensory analyses. Out of 25 compounds quantified by selected-ion flow-tube mass spectrometry, ethanol, dimethyl sulfide and hydrogen sulfide were found characteristic under anaerobic conditions (0/0/100 and 60/0/40), whereas spoilage under air was primarily associated with the production of alcohols and ketones. Under high-O-2 MAP (60/40/0), only 3-methylbutanal fulfilled the identification criteria. Overall, this manuscript presents a systematic and widely applicable methodology for the identification of most potential seafood spoilage indicators within the context of intelligent packaging technology development. In particular, parallel application of statistics and modelling was found highly beneficial for the performance of the quality characterization process and for the practical applicability of the obtained results in food quality monitoring.
Keywords
Intelligent packaging, Partial least squares regression, Seafood quality, SIFT-MS, Volatile organic compound, INTELLIGENT PACKAGING SYSTEMS, TUNA THUNNUS-ALBACARES, 2 DEGREES-C, MICROBIOLOGICAL SPOILAGE, ENRICHED ATMOSPHERES, MICROBIAL SPOILAGE, VOLATILE COMPOUNDS, CARBON-DIOXIDE, GAS-CHROMATOGRAPHY, SPARUS-AURATA

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MLA
Kuuliala, Lotta, et al. “Spoilage Evaluation of Raw Atlantic Salmon (Salmo Salar) Stored under Modified Atmospheres by Multivariate Statistics and Augmented Ordinal Regression.” INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, vol. 303, 2019, pp. 46–57, doi:10.1016/j.ijfoodmicro.2019.04.011.
APA
Kuuliala, L., Sader, M., Solimeo, A. M., Perez Fernandez, R., Vanderroost, M., De Baets, B., … Devlieghere, F. (2019). Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression. INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 303, 46–57. https://doi.org/10.1016/j.ijfoodmicro.2019.04.011
Chicago author-date
Kuuliala, Lotta, Marc Sader, Antonio Mattia Solimeo, Raul Perez Fernandez, Mike Vanderroost, Bernard De Baets, Bruno De Meulenaer, Peter Ragaert, and Frank Devlieghere. 2019. “Spoilage Evaluation of Raw Atlantic Salmon (Salmo Salar) Stored under Modified Atmospheres by Multivariate Statistics and Augmented Ordinal Regression.” INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 303: 46–57. https://doi.org/10.1016/j.ijfoodmicro.2019.04.011.
Chicago author-date (all authors)
Kuuliala, Lotta, Marc Sader, Antonio Mattia Solimeo, Raul Perez Fernandez, Mike Vanderroost, Bernard De Baets, Bruno De Meulenaer, Peter Ragaert, and Frank Devlieghere. 2019. “Spoilage Evaluation of Raw Atlantic Salmon (Salmo Salar) Stored under Modified Atmospheres by Multivariate Statistics and Augmented Ordinal Regression.” INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY 303: 46–57. doi:10.1016/j.ijfoodmicro.2019.04.011.
Vancouver
1.
Kuuliala L, Sader M, Solimeo AM, Perez Fernandez R, Vanderroost M, De Baets B, et al. Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression. INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY. 2019;303:46–57.
IEEE
[1]
L. Kuuliala et al., “Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression,” INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, vol. 303, pp. 46–57, 2019.
@article{8621291,
  abstract     = {{The development of quality monitoring systems for perishable food products like seafood requires extensive data collection under specified packaging and storage conditions, followed by advanced data analysis and interpretation. Even though the benefits of using volatile organic compounds as food quality indices have been recognized, few studies have focused on real-time quantification of the seafood volatilome and subsequent systematic identification of the most important spoilage indicators. In this study, spoilage of Atlantic salmon (Salmo salar) stored under modified atmospheres (% CO2/O-2/N-2) and air was characterized by performing multivariate statistical analysis and augmented ordinal regression modelling for data collected by microbiological, chemical and sensory analyses. Out of 25 compounds quantified by selected-ion flow-tube mass spectrometry, ethanol, dimethyl sulfide and hydrogen sulfide were found characteristic under anaerobic conditions (0/0/100 and 60/0/40), whereas spoilage under air was primarily associated with the production of alcohols and ketones. Under high-O-2 MAP (60/40/0), only 3-methylbutanal fulfilled the identification criteria. Overall, this manuscript presents a systematic and widely applicable methodology for the identification of most potential seafood spoilage indicators within the context of intelligent packaging technology development. In particular, parallel application of statistics and modelling was found highly beneficial for the performance of the quality characterization process and for the practical applicability of the obtained results in food quality monitoring.}},
  author       = {{Kuuliala, Lotta and Sader, Marc and Solimeo, Antonio Mattia and Perez Fernandez, Raul and Vanderroost, Mike and De Baets, Bernard and De Meulenaer, Bruno and Ragaert, Peter and Devlieghere, Frank}},
  issn         = {{0168-1605}},
  journal      = {{INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY}},
  keywords     = {{Intelligent packaging,Partial least squares regression,Seafood quality,SIFT-MS,Volatile organic compound,INTELLIGENT PACKAGING SYSTEMS,TUNA THUNNUS-ALBACARES,2 DEGREES-C,MICROBIOLOGICAL SPOILAGE,ENRICHED ATMOSPHERES,MICROBIAL SPOILAGE,VOLATILE COMPOUNDS,CARBON-DIOXIDE,GAS-CHROMATOGRAPHY,SPARUS-AURATA}},
  language     = {{eng}},
  pages        = {{46--57}},
  title        = {{Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression}},
  url          = {{http://doi.org/10.1016/j.ijfoodmicro.2019.04.011}},
  volume       = {{303}},
  year         = {{2019}},
}
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