377 Developing visible near-infrared spectroscopy calibration equations to predict the chemical composition of feces and nutrient digestibility based on pig fecal spectra
Notice bibliographique
Résumé
Abstract Optimizing feed efficiency is an effective way to curb overall pig production costs given the increasing feed prices. Visible near-infrared spectroscopy (NIRS) can be potentially implemented in the swine industry to determine nutrient digestibility, offering the advantages of rapidity, non-destructiveness, and cost-effectiveness. The current study aimed to develop calibration equation models for the chemical composition of feces and the apparent total-tract digestibility (ATTD) of nutrients, including dry matter (DM), crude protein (CP), gross energy (GE), ash, calcium, phosphorus, neutral detergent fiber (NDF), and acid detergent fiber (ADF) to make predictions based on the spectra of oven-dried pig feces. Fecal samples were collected from a total of 1,917 male finishing boars of purebred Large White sire and dam lines with 306 individual samples used for the development of calibration equations. Samples were scanned twice between 400 and 1099.5 nm with increments of 0.5 nm using a Foss FoodScan 2 (FOSS, Hilleroed, Denmark) in transmission visible-NIR. A total of 16 calibration models were generated and developed using FossCalibrator Pro software. The results showed that the coefficient of determination (R2) for calibration models of different nutrient content in feces was greater than those from ATTD of the same nutrient. Among that, R2 values for calibration, cross validation, and validation of DM and CP content all exceeded 0.8. Except for ash and phosphorus, R2 values of other nutrient contents were greater in calibration than in validation. Different from nutrient contents, the R2 of ATTD of nutrients, except for phosphorus and NDF, were less in calibration compared with validation. In validation, the residual prediction deviation (RPD) values of DM, CP, ash, and NDF were above 1.5, and the RPD values of ATTD of DM, CP, and GE were also greater than 1.5. The linearity and accuracy of calibration equation models for nutrient content in feces were higher than those for the ATTD of nutrients. The calibration models for CP content in feces and the digestibility of CP exhibited the highest calibration model quality. The external prediction of an independent sample set exhibits a real, abundant, and comprehensive prediction values using above calibration models based on 1,611 fecal samples. Except for DM, the coefficient of variation for the remaining parameters within the external prediction datasets was less than that in the validation datasets. In summary, calibration equations were successfully developed to predict the chemical composition of feces and nutrient digestibility based on oven-dried pig fecal spectra, especially for CP. The next step is to develop calibration equations using wet fecal samples. The application of NIRS technology to predict crude protein digestibility is promising and can be used to assist pig breeding companies in selecting animals with high protein efficiency.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».