Correlating Molecular Spectroscopy and Molecular Chemometrics to Explore Carbohydrate Functional Groups and Utilization of Coproducts from Biofuel and Biobrewing Processing
Bibliographic record
Abstract
Dried distillers grains with solubles (DDGS) was coproducts from bioethanol and biobrewing industry. It was an excellent resource of protein and energy feedstuff in China. Conventional studies often focus on traditional nutritional profiles. To data, there is little research on molecular structure-nutrition interaction of carbohydrate in coproducts. In this study, five kinds of corn-grain based DDGS and two kinds of barley-grain based DDGS were collected from different manufactures in the north of China. They were coded as "1, 2, 3, 4, 5, 6, and 7", respectively. The primary purposes of this project were to investigate the molecular structure-nutrition interaction of carbohydrate in coproducts, in terms of (1) carbohydrate-related chemical composition and nutrient profiles, (2) predicted values for energy in coproducts for animal, and (3) in situ digestion of dry matter. The result showed that acid detergent fiber content in corn DDGS and barley DDGS had negative correlation with structural carbohydrate peak area, cellulose compounds, and carbohydrate component peaks (first, second, and total peak area), which were measured with molecular spectroscopy. The correlation between carbohydrate peak area (second and total) and digestible fiber (tdNDF) were negative. There were no correlation between carbohydrate spectral intensities and energy values, carbohydrate subfractions partitioned by CNCPS system, and in situ rumen degradation. The results indicate that carbohydrate spectral profiles (functional groups) are associated with the carbohydrate nutritive values in coproducts from biofuel and biobrewing processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".