Ecological diversity and associated volatilome of typical mountain Caciotta cheese from Italy
Bibliographic record
Abstract
Traditional products are particularly appreciated by consumers and among these products, cheese is a major contributor to the Italian mountainous area economics. In this study, shotgun metagenomics and volatilomics were used to understand the biotic and abiotic factors contributing to mountain Caciotta cheese typicity and diversity. Results showed that the origin of cheese played a significant role; however, curd cooking temperature, pH, salt concentration and water activity also had an impact. Viral communities exhibited higher biodiversity and discriminated cheese origins in terms of production farms. Among the most dominant bacteria, Streptococcus thermophilus showed higher intraspecific diversity and closer relationship to production farm when compared to Lactobacillus delbrueckii. However, despite a few cases in which the starter culture was phylogenetically separated from the most dominant strains sequenced in the cheese, starter cultures and dominant cheese strains clustered together suggesting substantial starter colonization in mountain Caciotta cheese. The Caciotta cheese volatilome contained prominent levels of alcohols and ketones, accompanied by lower proportions of terpenes. Volatile profile not only demonstrated a noticeable association with production farm but also significant differences in the relative abundances of enzymes connected to flavor development. Moreover, correlations of different non-homologous isofunctional enzymes highlighted specific contributions to the typical flavor of mountain Caciotta cheese. Overall, this study provides a deeper understanding of the factors shaping typical mountain Caciotta cheese, and the potential of metagenomics for characterizing and potentially authenticating food products.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".