An emerging method for rapid characterization of feed structures and feed component matrix at a cellular leveland relation to feed quality and nutritive value
Bibliographic record
Abstract
Feed quality, feed characteristics, nutrient utilization and digestive behaviour are closely related to: (i) total feed composition, (ii) feed intrinsic structures, and (iii) biological component matrix (such as protein to starch matrix, protein to carbohydrate matrix). Conventional "wet" chemical analysis can determine total chemical composition, but fails to detect the feed intrinsic structures and biological component matrix due to destruction of feed samples during the processing for chemical analysis and the "wet" chemical analysis cannot link structural information to chemical information within intact feed tissue. Recently, advanced synchrotron-based Fourier transform infrared (FTIR) microspectroscopy has been developed as a non-destructive and non-invasive structural-chemical analytical technique. This technique can link chemical information to structural information of biological samples within intact tissue within cellular dimensions. It can provide four kinds of information simultaneously: tissue composition, tissue structure, tissue chemistry and tissue environment. However, this novel technique has been found mainly for medical science research, extremely rare for feed science and nutrition research. The objective of this review article was to illustrate synchrotron-based FTIR microspectroscopy as a novel research tool for rapid characterization of feed structures at a cellular level and for detection of chemical features and molecular chemical make-up of feed biological component matrix and nutrient interaction. The emphasis of this article was to show that feed structural-chemical features at a cellular level are closely related to feed characteristics, feed quality and nutritive value in animals. The synchrotron-based technology will provide us with a greater understanding of the plant-animal interface.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".