Large-scale multivariate dataset on the characterization of microbiota diversity, microbial growth dynamics, metabolic spoilage volatilome and sensorial profiles of two industrially produced meat products subjected to changes in lactate concentration and packaging atmosphere

Poirier, Simon; Luong, Ngoc-Du Martin; Anthoine, Valérie; Guillou, Sandrine; Membré, Jeanne‐Marie; Moriceau, Nicolas; Rezé, Sandrine; Zagorec, Monique; Feurer, Carole; Frémaux, B.; Jeuge, Sabine; Robieu, Emeline; Champomier-Vergès, Marie; Coeuret, Gwendoline; Cauchie, Emilie; Daube, Georges; Korsak, N.; Coroller, Louis; Desriac, Noémie; Desmonts, Marie-Hélène; Gohier, Rodérick; Werner, Dalal; Loux, Valentin; Rué, Olivier; Dohollou, Marie-Hélène; Defosse, Tatiana A.; Chaillou, Stéphane

doi:10.1016/j.dib.2020.105453

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…Summary overview of the dataset and of the analysis strategy. Technical details about the experimental design can be found in our data article (Poirier et al, 2020). Briefly, we monitored the production of two meat products: poultry and pork chipolata-type sausages.…”

Section: Resultsmentioning

confidence: 99%

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Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

Poirier,

Coeuret,

Champomier-Vergès

et al. 2023

Preprint

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The use of omics data for monitoring the microbial flow of fresh meat products along a production line and the development of spoilage prediction tools from these data is a promising but challenging task. In this context, we produced a large multivariate dataset (over 600 samples) obtained on the production lines of two similar types of fresh meat products (poultry and raw pork sausages).We describe a full analysis of this dataset in order to decipher how the spoilage microbial ecology of these two similar products may be shaped differently depending on production parameter characteristics. Our strategy involved a holistic approach to integrate unsupervised and supervised statistical methods on multivariate data (OTU-based microbial diversity; metabolomic data of volatile organic compounds; sensory measurements; growth parameters), and a specific selection of potential uncontrolled (initial microbiota composition) or controlled (packaging type; lactate concentration) drivers. Our results demonstrate that the initial microbiota, which is shown to be very different between poultry and pork sausages, has a major impact on the spoilage scenarios and on the effect that a downstream parameter such as packaging type has on the overall evolution of the microbial community. Depending on the process, we also show that specific actions on the pork meat (such as deboning and defatting) elicit specific food spoilers such as Dellaglioa algida, which becomes dominant during storage. Finally, ecological network reconstruction allowed us to map six different metabolic pathways involved in the production of volatile organic compounds involved in spoilage. We were able connect them to the different bacterial actors and to the influence of packaging type in an overall view. For instance, our results demonstrate a new role of Vibrionaceae in isopropanol production, and of Latilactobacillus fuchuensis and Lactococcus piscium in methanethiol/disylphide production. We also highlight a possible commensal behavior between Leuconostoc carnosum and Latilactobacillus curvatus around 2,3-butanediol metabolism. We conclude that our holistic approach combined with large-scale multi-omic data was a powerful strategy to prioritize the role of production parameters, already known in the literature, that shape the evolution and/or the implementation of different meat spoilage scenarios

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Section: Resultsmentioning

confidence: 99%

“…The sequencing data were deposited in the bioproject PRJNA522361. Sample accession numbers vary from SAMN10964863 to SAMN10965438 for the gyrB amplicon, and from SAMN10970131 to SAMN10970566 for 16S (Poirier et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

Poirier,

Coeuret,

Champomier-Vergès

et al. 2023

Preprint

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“…The data presented in this article and their production methods are detailed in our data paper ( Poirier et al, 2020 ). The two meat types were chosen because of their similar recipe (batter of 75–80% ground meat and 15–20% fat mixed with spices, both including potassium lactate as a preservative).…”

Section: Methodsmentioning

confidence: 99%

“…For this purpose, we have produced a large multivariate dataset obtained on the production lines of two similar types of fresh meat products (poultry and raw pork sausages) and have made it available for the scientific community ( Poirier et al, 2020 ). In the present study, we describe a full analysis of this dataset and decipher how the spoilage microbial ecology of these two similar products is nevertheless shaped differently depending on the parameters characteristic of each one.…”

Section: Introductionmentioning

confidence: 99%

Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

Poirier,

Coeuret,

Champomier-Vergès

et al. 2023

Front. Microbiol.

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BackgroundThe use of omics data for monitoring the microbial flow of fresh meat products along a production line and the development of spoilage prediction tools from these data is a promising but challenging task. In this context, we produced a large multivariate dataset (over 600 samples) obtained on the production lines of two similar types of fresh meat products (poultry and raw pork sausages). We describe a full analysis of this dataset in order to decipher how the spoilage microbial ecology of these two similar products may be shaped differently depending on production parameter characteristics.MethodsOur strategy involved a holistic approach to integrate unsupervised and supervised statistical methods on multivariate data (OTU-based microbial diversity; metabolomic data of volatile organic compounds; sensory measurements; growth parameters), and a specific selection of potential uncontrolled (initial microbiota composition) or controlled (packaging type; lactate concentration) drivers.ResultsOur results demonstrate that the initial microbiota, which is shown to be very different between poultry and pork sausages, has a major impact on the spoilage scenarios and on the effect that a downstream parameter such as packaging type has on the overall evolution of the microbial community. Depending on the process, we also show that specific actions on the pork meat (such as deboning and defatting) elicit specific food spoilers such as Dellaglioa algida, which becomes dominant during storage. Finally, ecological network reconstruction allowed us to map six different metabolic pathways involved in the production of volatile organic compounds involved in spoilage. We were able connect them to the different bacterial actors and to the influence of packaging type in an overall view. For instance, our results demonstrate a new role of Vibrionaceae in isopropanol production, and of Latilactobacillus fuchuensis and Lactococcus piscium in methanethiol/disylphide production. We also highlight a possible commensal behavior between Leuconostoc carnosum and Latilactobacillus curvatus around 2,3-butanediol metabolism.ConclusionWe conclude that our holistic approach combined with large-scale multi-omic data was a powerful strategy to prioritize the role of production parameters, already known in the literature, that shape the evolution and/or the implementation of different meat spoilage scenarios.

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“…The experimental data used in this study were extracted from a data paper monitoring several spoilage responses of fresh turkey sausages with different lactate formulations and conditioned under modified atmospheres during chilled storage (the data paper is available online [23]). Only data associated with the monitoring of pH were used herein.…”

Section: Products and Ph Measurementmentioning

confidence: 99%

A Bayesian Approach to Describe and Simulate the pH Evolution of Fresh Meat Products Depending on the Preservation Conditions

Luong

Coroller

Zagorec

et al. 2022

Foods

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Measuring the pH of meat products during storage represents an efficient way to monitor microbial spoilage, since pH is often linked to the growth of several spoilage-associated microorganisms under different conditions. The present work aimed to develop a modelling approach to describe and simulate the pH evolution of fresh meat products, depending on the preservation conditions. The measurement of pH on fresh poultry sausages, made with several lactate formulations and packed under three modified atmospheres (MAP), from several industrial production batches, was used as case-study. A hierarchical Bayesian approach was developed to better adjust kinetic models while handling a low number of measurement points. The pH changes were described as a two-phase evolution, with a first decreasing phase followed by a stabilisation phase. This stabilisation likely took place around the 13th day of storage, under all the considered lactate and MAP conditions. The effects of lactate and MAP on pH previously observed were confirmed herein: (i) lactate addition notably slowed down acidification, regardless of the packaging, whereas (ii) the 50%CO2-50%N2 MAP accelerated the acidification phase. The Bayesian modelling workflow—and the script—could be used for further model adaptation for the pH of other food products and/or other preservation strategies.

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Large-scale multivariate dataset on the characterization of microbiota diversity, microbial growth dynamics, metabolic spoilage volatilome and sensorial profiles of two industrially produced meat products subjected to changes in lactate concentration and packaging atmosphere

Cited by 9 publications

References 6 publications

Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

Holistic integration of omics data reveals the drivers that shape the ecology of microbial meat spoilage scenarios

A Bayesian Approach to Describe and Simulate the pH Evolution of Fresh Meat Products Depending on the Preservation Conditions

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