Prediction of wheat chemical and physical characteristics and nutritive value by near-infrared reflectance spectroscopy
Bibliographic record
Abstract
1. The aims of this study were to investigate the potential of near infrared reflectance spectroscopy (NIRS) to predict the chemical and physical characteristics of wheat and also to predict the nutritive value of wheat for broiler chickens. 2. A total of 164 wheat samples, collected from a wide range of different sources (England, Northern Ireland and Canada), varieties and years, were used in this study. 3. Chemical and physical parameters measured included specific weight, thousand grain weight, in vitro viscosity, gross energy, nitrogen, neutral detergent fibre (NDF), starch, total and soluble non-starch polysaccharides (NSP), lysine, threonine, amylose, hardness, rate of starch digestion and protein profiles. 4. A total of 94 wheat samples were selected for inclusion in three bird trials and included at 650 g/kg in a typical UK starter/grower diet. Birds were housed in individual wire metabolism cages from 7 to 28 d and offered water and food ad libitum. Dry matter intake (DMI), live weight gain (LWG) and gain:feed ratio were measured weekly. A balance collection was carried out from d 14 to 21 for determination of apparent metabolisable energy (AME), ME:gain and dry matter retention. At 28 d the birds were humanely killed, the contents of the jejunum removed for determination of in vivo viscosity and the contents of the ileum removed for determination of ileal dry matter, starch and protein digestibility. 5. The wheat samples were scanned as whole and milled wheat, both dried and undried and NIRS calibrations, first excluding and then including the Canadian wheat samples, were developed. 6. NIRS calibrations for milled wheat samples may be useful for determining specific weight (R(cv)(2) = 0.75, for milled wheat dried), nitrogen (R(cv)(2) = 0.983 for milled and dried) and rate of starch digestion (R(cv)(2) = 0.791 for milled, dried and undried). 7. NIRS calibrations for whole wheat samples (undried) may be useful for determining wheat nutritive value, with good predictions for live weight gain (R(cv)(2) = 0.817) and feed conversion efficiency (R(cv)(2) = 0.825). 8. Inclusion of the Canadian wheat samples in the NIRS analysis provided additional robust calibrations for gross energy (R(cv)(2) = 0.86, dried and milled) and starch content (R(cv)(2) = 0.79, undried and milled). 9. This study shows that NIR is a useful tool in the accurate and rapid determination of wheat chemical parameters and nutritive value and could be extremely beneficial to both the poultry and wheat industry. 10. Further extension of the dataset would be recommended to further validate these findings.
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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) | 0.000 | 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".