Research on the Requirements of the Flavor Liquor Daqu for the Wheat Raw Material Quality
Bibliographic record
Abstract
Wheat is the main component for producing the flavor liquor Daqu. It is essential to meet the raw material quality requirements for high-quality Daqu to breed special wheat varieties and simultaneously develop the supporting cultivation practices. From 2018 to 2020, a field experiment involving four representative wheat cultivars (Chuanmai 104, Mianmai 367, Xikemai 8, and Shumai 1671) and six nitrogen application rates (0, 45, 90, 135, 180, and 225 kg/hm2) in four different agro-ecological sites (Guanghan, Zitong, and Xichang in Sichuan Province, and Nanzhang in Hubei Province) in the upper and middle reaches of the Yangtze River basin was conducted. This study evaluated the main quality parameters of wheat raw materials and the quality of the flavor liquor Daqu. The results showed that, for most sensory and chemical parameters of Daqu, the main effects of year, location and variety, were all significant (P<0.05) or extremely significant (P<0.01). The total sensory score in 2019 was 9.7% higher than that in 2020. The mean total sensory score in Guanghan and Nanzhang location were 44.6 and 44.3 (maximum total score 60), respectively, significantly (P<0.05) different from that in Xichang location (39.7). The mean sensory score of Xikemai 8 and Chuanmai 104 were 45.4 and 43.8, respectively, which were significantly (P<0.05) higher than those of Mianmai 367 (42.2) and Shumai 1671 (42.0). The effects of nitrogen application rate on sensory score was not significant. Moreover, the values of the chemical parameters (acidity, saccharification power, liquefaction power, and ferment power) of Daqu for all treatments all met the requirements of "QB/T 4259-2011 flavor Daqu". Under different statistical methods, the raw material quality parameters associated with sensory evaluation of Daqu were different. Compared with other parameters, the comminution degree was more closely related to the sensory score of Daqu. For the samples with total sensory score≥45, the quantile values of 25%, 50% and 75% of comminution degree were 70.5%, 72.3% and 73.8%, respectively. In general, wheat raw materials with higher bulk density, falling value, water absorption of dough, medium to high grain opaque rate and suitable comminution degree are more suitable for Daqu production.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".