Bioprotective lactobacilli in Crescenza and Gouda cheese models to inhibit fungal spoilage
Bibliographic record
Abstract
Bioprotective cultures are applied to control the fungal spoilage of fermented dairy products but only few studies evaluated their efficacy in cheese. To evaluate growth and antifungal activity of bioprotective lactobacilli, cultures were used singly and combined adjunct cultures for laboratory-scale Crescenza cheese, a fresh cheese, and for pilot-scale Gouda cheese, respectively. Growth of the bioprotective cultures was characterized by surface plating, strain-specific qPCR and nanopore sequencing of full-length 16S rRNA genes. Analysis of Crescenza cheese by viable plate counts documented growth over 14 d of storage. In Gouda cheese, strain-specific qPCR demonstrated the growth of the lactobacilli during the first 45 d of ripening. Metagenomic sequencing demonstrated that the microbial community of Gouda cheeses was dominated by the starter and adjunct cultures with the relative abundance of other bacteria accounting for less than 0.5%. Adjunct cultures inhibited Penicillium caseifulvum and P. roqueforti in Crescenza cheese; Lacticaseibacillus rhamnosus FUA3185 and Lct. paracasei FUA3413 extended the mold-free days almost 1.5-fold. Growth of yeasts was not inhibited. In Gouda cheese, Lactiplantibacillus plantarum FUA3183 and FUA3247 reduced the growth of Debaryomyces hansenii FUA4064 by 1.5 log and extended the mold-free shelf life for 3 d in 45 d-ripened cheese. Longer ripening time decreased the antifungal activity and the culture was no longer active towards yeasts. The abundance of Mn2+ in the curds of Crescenza cheese (0.2 to 0.4 mg/kg FW) was not altered by bioprotective cultures. The antifungal effect of Lct. rhamnosus, Lct. paracasei and Lp. plantarum thus depends on the type of cheese.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".