Prediction of wheat chemical and physical characteristics and nutritive value by near-infrared reflectance spectroscopy
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle