Correlation and Difference between Core Micro-Organisms and Volatile Compounds of Suan Rou from Six Regions of China
Bibliographic record
Abstract
Suan Rou (SR), a traditional fermented meat, is widely favored by consumers due to its unique flavor and characteristics. To study the relationship between the core differential micro-organisms and differential volatile organic compounds (VOCs) of SR from six regions of China, high-throughput sequencing (HTS) and gas-chromatography−ion mobility spectrometry (GC-IMS) technologies were used to analyze the correlation between micro-organisms and VOCs in SR from Xiangxi of Hunan, Rongshui of Guangxi, Zunyi of Guizhou, Jinping of Guizhou, Congjiang of Guizhou, and Libo of Guizhou. A total of 13 core micro-organisms were identified at the genus level. Moreover, 95 VOCs were identified in the SR samples by GC-IMS analysis, with alcohols, aldehydes, ketones, and esters comprising the major VOCs among all the samples. The results showed a strong correlation (|r| > 0.8, p < 0.05) between the core differential micro-organisms and differential VOCs, including four bacteria, five fungi, and 12 VOCs. Pediococcus, Debaryomyces, Zygosaccharomyces, and Candida significantly contributed to the unique VOCs of SR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".