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. 2016 Apr 26;7:1556–1559. doi: 10.1016/j.dib.2016.04.044

CO2 and O2 solubility and diffusivity data in food products stored in data warehouse structured by ontology

Valérie Guillard a,⁎, Patrice Buche a, Juliette Dibie b, Stéphane Dervaux b, Filippo Acerbi a, Estelle Chaix a, Nathalie Gontard a, Carole Guillaume a
PMCID: PMC4865664  PMID: 27222852

Abstract

This data article contains values of oxygen and carbon dioxide solubility and diffusivity measured in various model and real food products. These data are stored in a public repository structured by ontology. These data can be retrieved through the @Web tool, a user-friendly interface to capitalise and query data. The @Web tool is accessible online at http://pfl.grignon.inra.fr/atWeb/.

Keywords: Diffusivity, Solubility, Data, Data warehouse, Ontology, Food, O2, CO2

Specifications Table

Subject area Biochemistry
More specific subject area Food science and food engineering
Type of data Table, links
How data was acquired Chemical titration (for CO2quantification) and luminescence-based detection (for O2detection) implemented in dedicated experimental set-ups
Data format Analyzed, ready to use
Experimental factors Samples considered are model and real food products without any pre-treatment except addition of sodium azide to avoid microbial growth
Experimental features Solubility is measured by quantifying the concentration of dissolved gas in a sample in equilibrium with a fix and controlled partial pressure.
Diffusivity is identified from an experimental diffusion kinetic curve by using a mathematical model and appropriate numerical treatment (algorithm of optimization).
Data source location University of Montpellier, FR-34060, France
Data accessibility Data is within this article.
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Value of the data

  • •

    A unique set of CO2 solubility and diffusivity data indispensable in food engineering to model CO2 gas transfer in food.

  • •

    A unique set of O2 diffusivity values within synthetic oils as a function of temperature.

  • •

    O2 diffusivity data could be used to predict oxidation of O2-sensitive compounds in foods.

  • •

    These data could serve as benchmark for other researchers coping with research on gas transfer in food for numerous simulation.

1. Data

Data shared with this article are more than 100 data of solubility and diffusivity of gases (O2 and CO2) in food samples. These data are stored in a data warehouse called @Web in which the data management is guided by ontology.

All data are available for uploading at the URL specified below and recalled in the table hereafter with the details about the nature and amount of data available at each URL.

Data type Table URL (copy/paste the URL in your Internet browser) Amount of data
CO2 solubility pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2775&idDoc=1335&id=35272672 34
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2776&idDoc=1335&id=35305550 21
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2773&idDoc=1335&id=35245144 48
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2732&idDoc=1332&id=34354344 3
CO2 diffusivity pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2780&idDoc=1346&id=35361064 12
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2826&idDoc=1346&id=36350532 11
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2779&idDoc=1346&id=35346176 11
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2778&idDoc=1346&id=35333312 12
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2777&idDoc=1346&id=35320430 16
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2733&idDoc=1332&id=34367074 11
O2 diffusivity pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2764&idDoc=1342&id=35103704 24
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2755&idDoc=1342&id=34742706 30
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2754&idDoc=1342&id=34730102 10
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2765&idDoc=1342&id=35116534 44
pfl.grignon.inra.fr/atWeb/TableServlet?viewTable=2910&idDoc=1342&id=40225446 2
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2. Experimental design, materials and methods

O2. Oxygen optical sensors (Presens GmbH, Regensberg, Germany) were used to monitor O2 partial pressure. This measurement is based on dynamic luminescence quenching. Due to an excitation flash emitted through an optical fibre, the luminophore contained in the sensor goes into an excited state and thus emits fluorescence backscatter signal, which is detected by the optical fibre. If the luminophore is in contact with an oxygen molecule, the backscatter signal is changed due to a dynamic quenching of luminescence. The change in the backscatter signal permits to detect the O2 partial pressure in the medium. Two different set-ups exist (1) an invasive O2-sensitive optical sensor made of a syringe probe (micro-sensors, Presens GmbH, Regensburg, Germany) connected to the optical fibre and oxygen metre (Oxy-4 micro, Presens) and (2) a non-invasive oxygen sensor made of a dot of 5 mm of diameter that can be stuck on the wall of a transparent container and measurement is then made through the transparent container.

Oxygen sorption kinetics were measured at fixed temperature value when imposing a controlled partial pressure of O2 in the surrounding of the sample. The mono-directional O2 ingress into the sample was measured locally at the bottom or in the middle of the thin layer of food material previously free of O2 using one of the aforementioned sensors. More details on the experimental set-up could be found in [1], [2], [3].

CO2. The solubility of CO2 was measured at equilibrium by quantification of the gas dissolved in the sample using chemical titration [4], [5]. This measurement was done in a set-up where the sample is in a controlled chamber (controlled temperature, relative humidity, CO2 gas composition).

The diffusion of CO2 was characterised by (1) imposing a gradient of CO2 to a piece of material of simple geometry (cylinder or plane sheet), (2) measuring the CO2 sorption kinetic in the sample and (3) identifying diffusivity values by adjusting a dedicated mathematical model to the experimental kinetic. Two types of kinetic could be obtained: (1) CO2 space-dependent profile in the cylindrical sample after its slicing and CO2 quantification in each slice or (2) CO2 time-dependent profile after CO2 quantification in each thin slice (one slice corresponding at one time of kinetic) [4], [6].

Numerical treatment. For both O2 and CO2, diffusivities are identified by fitting a dedicated mathematical model to the experimental kinetic curve (space-dependent profile or time-dependent profile). This identification step is performed using a routine (“lsqnonlin”) of Matlab® software.

Acknowledgements

Part of the data presented here were acquired in the framework of the Map׳Opt project (ANR-10-ALIA-002 2011 to 2015) funded by the French National Research Agency, whose title is “Equilibrium gas composition in modified atmosphere packaging and food quality”.

Footnotes

Appendix A

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2016.04.044.

Appendix A. Supplementary material

Supplementary material

mmc1.pdf (1.2MB, pdf)

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

mmc1.pdf (1.2MB, pdf)

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