Ruminal nutrient availability and inherent structural features of six barley varieties using in situ technique and Mid-IR spectroscopy
Bibliographic record
Abstract
Barley grain is one of the main sources of feed for ruminants in Canada. Although barley varieties may have similar chemical composition, they exhibit different rumen degradation characteristics and nutrient availabilities. These biological differences may be related to structural chemical make-up or structural features among the varieties. The objectives of this study were to use the in situ technique and two Mid-IR Spectroscopy techniques, Diffuse Reflectance Fourier Transform IR Spectroscopy (DRIFT) and Synchrotron-based Fourier Transform IR Microspectroscopy (SFTIRM) to determine ruminal nutrient availabilities and inherent structural features in the hull, seed and endosperm of six barley varieties (AC Metcalfe, McLeod, CDC Dolly, CDC Helgason, CDC Trey, and CDC Cowboy) and to study the relationships between structural characteristics, mean and median particle size and nutrient availability. The nylon bag technique was used to incubate coarsely dry-rolled barley samples for 0, 2, 4, 8, 12, 24 and 48 h in the rumen of three mature Holstein dry dairy cows, which were ruminally cannulated. The rumen degradation kinetics of dry matter (DM), crude protein (CP) and starch were determined using first order degradation kinetics equations. Results indicated that there were significant differences in the mean and median particle size, degradation kinetics of each individual nutrient (DM, CP, and starch) among the six barley varieties. CDC Helgason showed the lowest degradation rate and extent of all nutrients (DM, CP, and starch) among the six barley varieties with larger particle size. Compared with other five varieties, CDC Helgason may be more suitable for ruminants feeding because of the lowest degradation rate and extent. The results also revealed a strong correlation between median particle size and the rate and extent of rumen degradation. The results also showed that both DRIFT and SFTIR techniques associated with uni- and two multi- variate analyses were capable to efficiently discriminate and classify the inherent molecular structural features among the different varieties of barleys. Uni-variate analyses were conducted using both the DRIFT spectroscopy (hull and whole seed sample) and SFTIR microspectroscopy (endosperm tissue). The results from hull samples showed significant differences in the peak area of aromatic lignin, cellulosic compound, and total carbohydrates (CHO), and the ratio of lignin to cellulosic compound among the six barley varieties. The results from whole seed samples showed significant difference in the peak area and height of Amide I, peak area of total CHO and structural CHO (cellulosic), and the ratio of Amide I to total CHO area, and the ratio of total CHO to structural CHO. Significant differences were also found in the SFTIR results from endosperm tissue. With two multivariate spectral analysis techniques: Agglomerative hierarchical cluster (AHCA) and Principal component analyses (PCA) applied on whole seed sample, the CDC Helgason was distinguished from AC Metcalfe, CDC Dolly, McLeod and CDC Cowboy in fingerprint (1800-800 cm-1) and CHO region (1185-800 cm-1), from AC Metcalfe, McLeod and CDC Cowboy in protein region (1715-1485 cm-1). Information from this study involving probing the seed internal structure of barley may provide a further insight as to why barley varieties exhibit different rumen degradations.
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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 | 0.000 | 0.000 |
| 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 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".