Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar)
Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particular...
Published in: | International Journal of Food Microbiology |
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ftunivgent:oai:archive.ugent.be:8685346 2023-06-11T04:10:18+02:00 Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) Kuuliala, Lotta Perez Fernandez, Raul Tang, Mengzi Vanderroost, Mike De Baets, Bernard Devlieghere, Frank 2021 application/pdf https://biblio.ugent.be/publication/8685346 http://hdl.handle.net/1854/LU-8685346 https://doi.org/10.1016/j.ijfoodmicro.2020.108955 https://biblio.ugent.be/publication/8685346/file/8685350 eng eng https://biblio.ugent.be/publication/8685346 http://hdl.handle.net/1854/LU-8685346 http://dx.doi.org/10.1016/j.ijfoodmicro.2020.108955 https://biblio.ugent.be/publication/8685346/file/8685350 No license (in copyright) info:eu-repo/semantics/restrictedAccess INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY ISSN: 0168-1605 ISSN: 1879-3460 Agriculture and Food Sciences Food Science Microbiology General Medicine Latent Dirichlet Allocation Food quality Metabolomics Potential spoilage indicator Volatile organic compound VOLATILE ORGANIC-COMPOUNDS MASS-SPECTROMETRY IDENTIFICATION MODULES STORAGE LDA journalArticle info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2021 ftunivgent https://doi.org/10.1016/j.ijfoodmicro.2020.108955 2023-04-19T22:12:07Z Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling more specifically, Latent Dirichlet Allocation (LDA) in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo solar) at 4 degrees C under different gaseous atmospheres (% CO2/O-2/N-2): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N-2 (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis. Article in Journal/Newspaper Atlantic salmon Salmo salar Ghent University Academic Bibliography International Journal of Food Microbiology 337 108955 |
institution |
Open Polar |
collection |
Ghent University Academic Bibliography |
op_collection_id |
ftunivgent |
language |
English |
topic |
Agriculture and Food Sciences Food Science Microbiology General Medicine Latent Dirichlet Allocation Food quality Metabolomics Potential spoilage indicator Volatile organic compound VOLATILE ORGANIC-COMPOUNDS MASS-SPECTROMETRY IDENTIFICATION MODULES STORAGE LDA |
spellingShingle |
Agriculture and Food Sciences Food Science Microbiology General Medicine Latent Dirichlet Allocation Food quality Metabolomics Potential spoilage indicator Volatile organic compound VOLATILE ORGANIC-COMPOUNDS MASS-SPECTROMETRY IDENTIFICATION MODULES STORAGE LDA Kuuliala, Lotta Perez Fernandez, Raul Tang, Mengzi Vanderroost, Mike De Baets, Bernard Devlieghere, Frank Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) |
topic_facet |
Agriculture and Food Sciences Food Science Microbiology General Medicine Latent Dirichlet Allocation Food quality Metabolomics Potential spoilage indicator Volatile organic compound VOLATILE ORGANIC-COMPOUNDS MASS-SPECTROMETRY IDENTIFICATION MODULES STORAGE LDA |
description |
Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling more specifically, Latent Dirichlet Allocation (LDA) in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo solar) at 4 degrees C under different gaseous atmospheres (% CO2/O-2/N-2): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N-2 (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis. |
format |
Article in Journal/Newspaper |
author |
Kuuliala, Lotta Perez Fernandez, Raul Tang, Mengzi Vanderroost, Mike De Baets, Bernard Devlieghere, Frank |
author_facet |
Kuuliala, Lotta Perez Fernandez, Raul Tang, Mengzi Vanderroost, Mike De Baets, Bernard Devlieghere, Frank |
author_sort |
Kuuliala, Lotta |
title |
Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) |
title_short |
Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) |
title_full |
Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) |
title_fullStr |
Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) |
title_full_unstemmed |
Probabilistic topic modelling in food spoilage analysis : a case study with Atlantic salmon (Salmo salar) |
title_sort |
probabilistic topic modelling in food spoilage analysis : a case study with atlantic salmon (salmo salar) |
publishDate |
2021 |
url |
https://biblio.ugent.be/publication/8685346 http://hdl.handle.net/1854/LU-8685346 https://doi.org/10.1016/j.ijfoodmicro.2020.108955 https://biblio.ugent.be/publication/8685346/file/8685350 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY ISSN: 0168-1605 ISSN: 1879-3460 |
op_relation |
https://biblio.ugent.be/publication/8685346 http://hdl.handle.net/1854/LU-8685346 http://dx.doi.org/10.1016/j.ijfoodmicro.2020.108955 https://biblio.ugent.be/publication/8685346/file/8685350 |
op_rights |
No license (in copyright) info:eu-repo/semantics/restrictedAccess |
op_doi |
https://doi.org/10.1016/j.ijfoodmicro.2020.108955 |
container_title |
International Journal of Food Microbiology |
container_volume |
337 |
container_start_page |
108955 |
_version_ |
1768384622169686016 |