Evaluation of near-infrared reflectance spectroscopy (NIRS) techniques for total and phytate phosphorus of common poultry feed ingredients
Bibliographic record
Abstract
The purpose of this study was to determine the feasibility of estimating the total and phytate P content of common poultry feed ingredients by near-infrared reflectance spectroscopy (NIRS). Samples of 8 plant-origin feedstuffs were collected from poultry producers in the USA and Canada during the summer of 2009: corn (133), soybean meal (114), corn distillers dried grains with solubles (DDGS; 89), bakery by-product meal (95), wheat (22), wheat middlings (31), canola meal (21), and wheat shorts (15). The samples were assayed by standard wet chemical techniques for total and phytate P contents. There was considerable variation found in most of the ingredient components. The average values for the laboratory determinations versus NIRS predictions were all within 0.030 for total phosphorus and 0.012 for phytate P. For phytate P, the magnitude of the standard errors of the predictions ranged from 0.009% for soybean meal to 0.012% for canola meal. These values may be sufficiently precise for nutritionists to use the NIRS predictions to estimate how much of the P in their ingredients is not available to the birds. For total P, the magnitude of the standard errors of the predictions ranged from 0.027% for corn DDGS to 0.142% for wheat middlings. In general, total P predictions by NIRS were not generally sufficiently precise for most nutritionists to use in feed formulation. Decision making may be quite easy in using NIRS estimates for the phytate P content of bakery by-product meal [R(2) = 0.89 for predicted = f (determined)] but not for the total P content of soybean meal (R(2) = 0.03). It is concluded that precise estimates of phytate P through NIRS should allow nutritionists for more efficient formulate and mix feed, lowering feed costs and reducing the amount of residual polluting phosphorus in poultry excreta.
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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 | 0.001 | 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 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".