Structural, chemical and nutrient characterization of original feed stock and co-products from a newly built biofuel plant in Saskatchewan
Bibliographic record
Abstract
The objective of this study was to determine the structural, chemical and nutrient variation associated with different batches of original feedstock grain (wheat) and co-products (wheat distillers dried grain with solubles, wDDGS) from bioethanol processing and to study possibility of using molecular spectroscopy FT/IR-ATR technique as a rapid tool of identifying the structure spectral differences among the batches. The samples were collected from different batches of wheat and wDDGS produced from the same and newly build bioethanol plant in western Canada with updated bioethanol processing technology.. The results of chemical composition analysis showed significant differences (P<0.05) among the batches of wheat and among the batches of wDDGS. The variation in term of range among the batches of wheat for crude protein (CP), neutral detergent fibre (NDF) and acid detergent fibre (ADF) were 2.1, 2.5 and 1.0 %DM, respectively. Among the batches of wDDGS, the variation in CP, NDF and ADF were 3.9, 5.2 and 3.8 %DM, respectively. The soluble crude protein (SCP) content ranged by 7.2% of CP among the batches of wheat and by 4.2% of CP among the batches of wDDGS. In mineral profiles, variation among the batches of wheat and wDDGS in sulfur were 0.03 and 0.34 % of DM, respectively. In mineral profiles, higher phosphorus and sulfur content associated with wDDGS could be a concern. The sulfur content ranged by 0.3 % of DM among the batches of wDDGS with a mean of 1.1 %DM. For the estimated subfractions with Cornell Net Carbohydrate and Protein System (CNCPS), the immediately rumen available PA fraction (NPN) ranged by 14.8 % of CP among the batches of wheat and by 2.7 % of CP among batches of wDDGS. The particle size analysis revealed the small particle size in all three batches of wDDGS with the geometric mean of ranging from 650-690 µm. In situ rumen degradation kinetic results showed a significant differences (P<0.05) among both wheat and wDDGS batches. Multivariate molecular spectral analyses showed a potential of using FT/IR molecular vibrational spectroscopy as a rapid method to identify structure difference associated with different batches. In conclusion, the structural, chemical and nutrient availability vary among the batches of wheat and among the batches of wDDGS. The molecular spectroscopy shows a potential as a rapid tool to identify batch difference in chemical and nutrient profiles and to detect the response of functional group to bioethanol processing. Further study is needed to analyse molecular spectral characteristics in details for development of a successful identification tool to identify batch differences. The variation among the batches of wDDGS should be considered in ration formulation especially at high inclusion rates (40-50% wDDGS in diet DM) even the co-products are produced from the same bioethanol plant.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".