| Title: | Probabilistic topic modelling in food spoilage analysis: A case study with Atlantic salmon (Salmo salar) |
| Author(s): | Kuuliala L; Perez-Fernandez R; Tang M; Vanderroost M; De Baets B; Devlieghere F; |
| Address: | "Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium; Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium. Electronic address: Lotta.Kuuliala@UGent.be. Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium. Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium" |
| DOI: | 10.1016/j.ijfoodmicro.2020.108955 |
| ISSN/ISBN: | 1879-3460 (Electronic) 0168-1605 (Linking) |
| Abstract: | "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 salar) at 4 degrees C under different gaseous atmospheres (% CO(2)/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" |
| Keywords: | "Animals Food Microbiology/*methods/standards Food Quality Food Storage Gases/analysis Metabolomics *Models, Statistical Salmo salar/*microbiology Seafood/*microbiology Volatile Organic Compounds/analysis Latent Dirichlet Allocation Potential spoilage indi;" |
| Notes: | "MedlineKuuliala, L Perez-Fernandez, R Tang, M Vanderroost, M De Baets, B Devlieghere, F eng Netherlands 2020/11/14 Int J Food Microbiol. 2021 Jan 16; 337:108955. doi: 10.1016/j.ijfoodmicro.2020.108955. Epub 2020 Nov 4" |
