Rapid and Non-Destructive Prediction of Animal Feed Nutritive Parameters Using Near Infrared Spectroscopy and Multivariate Analysis
Bibliographic record
Abstract
This study elucidates the application of near-infrared spectroscopy (NIRS) and multivariate analysis for the rapid, non-destructive determination of animal feed nutritive parameters, specifically pH, NH3, and volatile fatty acid (VFA) values.NIRS spectra within the 1000-2500 nm range, coupled with reference methods, were employed for parameter determination.To enhance prediction accuracy, spectral pre-processing was undertaken using multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and first-derivative methods.Simultaneous determination of nutritive parameters was achieved through the establishment of prediction models employing three multivariate analyses: principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR).The results indicated superior pH (r=0.993) and NH3 (r=0.987)predictions using SVMR, while PLSR yielded the lowest root mean square error (RMSE) for VFA (r=0.998).These findings suggest that with appropriate spectral corrections and regression methodologies, NIRS can be effectively utilized for the rapid, simultaneous determination of animal feed nutritive parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".