Evidence map›Paper›PMID 38928751›Full record

ArticleFoods (Basel, Switzerland)2024

Investigation of Freezing and Freeze-Drying for Preserving and Re-Using a Whole Microbial Cheese Community.

Wenfan Cao, Stéphanie Passot, Françoise Irlinger, Fernanda Fonseca

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wenfan CaoINRAE, AgroParisTech, UMR SayFood, Université Paris-Saclay, F-911230 Palaiseau, France.
Stéphanie PassotINRAE, AgroParisTech, UMR SayFood, Université Paris-Saclay, F-911230 Palaiseau, France.ORCID 0000-0003-2799-5493
Françoise IrlingerINRAE, AgroParisTech, UMR SayFood, Université Paris-Saclay, F-911230 Palaiseau, France.
Fernanda FonsecaINRAE, AgroParisTech, UMR SayFood, Université Paris-Saclay, F-911230 Palaiseau, France.ORCID 0000-0003-3394-182X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preserving microbial ecosystems obtained from traditional cheese-making processes is crucial to safeguarding the biodiversity of microbial cheese communities and thus ensuring that the high flavor quality of traditional cheeses is maintained. Few protocols have been proposed for the long-term storage of microbial consortia. This work aimed to develop preservation methods to stabilize the entire microbial community in smear-ripened cheese without multiplication or isolation. A simplified microbial community, capable of reproducing the metabolic pattern of cheese maturation, was used in three independent cheese productions. Cheese samples were taken before and after the ripening step, mixed with maltodextrin or saline solution, and subjected to different stabilization conditions including freezing and freeze-drying, followed by 1 month of storage. Microbial survival was quantified using the colony-forming unit assay. Differential scanning calorimetry was used to relate the physical events occurring within the samples to the microbial storage stability. Freezing at -80 °C resulted in the lowest loss of culturability (<0.8 log unit), followed by freezing at -20 °C and freeze-drying. The ripening bacteria appeared as the most sensitive microorganisms within the community. Moreover, a successful cheese production using the best-stabilized community showed the possibility of preserving and re-using an entire microbial community of interest.

Indexed as

cheese ripeningecosystemglass transitionlactic acid bacteriamicrobiotaripening bacteriastabilizationyeasts

Identifiers

PMID38928751
PMCPMC11202935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.