Use of synchrotron FTIR microspectroscopy to identify chemical differences in barley endosperm tissue in relation to rumen degradation characteristics
Bibliographic record
Abstract
Valier (feed-type) and Harrington (malting-type) barley differ in rumen degradation characteristics. Harrington, in contrasts to Valier, exhibits a high rate and extent of rumen degradation, which can lead to metabolic problems such as acidosis and bloat in ruminants. Traditional “wet” chemical analysis cannot detect biological differences between barley varieties due to destruction of endosperm structure during processing. Synchrotron Fourier transform infrared microspectroscopy (SR-FTIR) is capable of exploring the chemical makeup of intact plant tissue with high signal to noise ratio at spatial resolutions as fine as 3~10 µm. The objective of this study was to use SR-FTIR microspectroscopy to explore and identify chemical differences in the ultra-structural matrix of the endosperm tissue of the two barley varieties as related to differences in rumen degradation characteristics. The results showed that the infrared absorbance intensity (Log 1/R) of the starch and protein varied considerably between the two varieties, but were not statistically significant. Harrington had a wider range of starch to protein IR absorbance intensity ratio (1.406 to 10.119 vs. 1.419 to 4.274), suggesting that it is more heterogeneous than Valier in endosperm chemical makeup. Valier had a lower ratio of starch to protein IR absorbance intensity than Harrington (P < 0.05), which implies that the starch granules in Valier are more closely associated with the protein matrix. This close association may prevent the starch granules from being rapidly degraded in the rumen. This work shows that the chemical makeup of intact plant tissues can be carried out by SR-FTIR microspectroscopy at ultra-spatial resolution (10 × 10 µm). Key words: Synchrotron infrared microspectroscopy, feed chemistry, barley, rumen degradability
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".