Regional aroma characteristics of sorghum for Chinese liquor production
Bibliographic record
Abstract
Sorghum is a key raw material in the production of Chinese liquor, that contributes to product quality. The role of regional sources of sorghum remains unclear, so in this study, six samples of sorghum were selected from two regions in China and from Canada. In this work, free and bound aroma compounds were analysed in six samples of sorghum. In all, 34 free aroma compounds were analysed by gas chromatography-mass spectrometry, including eleven alcohols, five esters, three acids, five aldehydes and ketones, eight benzoic compounds, one terpene and one other compound. Alcohols and benzoic compounds (1-octanol, 1-dodecanol, 1-octen-3-ol, 2-phenylethanol, 2-ethylphenol, 2-methoxyphenol, 4-ethyl-2-methoxyphenol) distinguished the regional characteristic of free aroma compounds in sorghum, based on multivariate statistical analysis. Additionally, 28 compounds were detected in bound forms. Among them, benzoic compounds (phenol, 3,5-dimethyl benzaldehyde, 2-phenylethanol), acids (heptanoic acid, hexanoic acid, octanoic acid), aldehydes (octanal, (E)-2-decenal, (E)-2-octenal, (E)-2,4-nonadienal) could be used to distinguish regional characteristics of bound aromas in sorghum. This study shows that the regional characteristics of free and bound aroma compounds in sorghum could be distinguished. This work provides insight in the selection of sorghum to modify the aroma of Chinese liquor and other fermented beverages. © 2020 The Institute of Brewing & Distilling
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".