Evaluation of prediction of indigestible fiber fraction (iNDF) of whole-crop barley silage by using non-destructive spectroscopic techniques as a fast-screening method: comparison between FTIR vs. NIR
Bibliographic record
Abstract
The objective of this study was to reveal the potential of using Fourier transform mid-infrared (FTIR) and near infrared (NIR) spectroscopy as tools for the determination of indigestible neutral (NDF) fraction (iNDF) of whole-crop barley silage. A total of 48 whole-crop barley silage samples collected from 48 different farms in Western Canada were analyzed for iNDF. Reference values were matched with NIR and FTIR spectra. Spectral data processing (pretreatments) included first derivative, standard normal variate, multiplicative scattering correction, second derivative, and orthogonal signal correction. Prediction equations were obtained from each model using an external validation set. The coefficient of determination for the external validation of iNDF was 0.62 for FTIR and 0.41 for NIR, while the corresponding ratio performance deviation was 1.69 and 1.38 for FTIR and NIR, respectively. Results from this research showed the high potential of applying infrared molecular spectroscopy for the examination of forage plant fiber digestibility. More studies are needed to improve the accuracy and performance of FTIR and NIR spectroscopies in predicting the iNDF of whole-crop barley silage samples.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".