Canine Food Preference Assessment of Animal and Vegetable Ingredient-Based Diets Using Single-Pan Tests and Behavioral Observation
Notice bibliographique
Résumé
Knowledge of canine food selection is critical for both the pet food industry and dog owners, since owners want quality foods that are palatable, while fulfilling their pet’s nutritional requirements. There are two common methods for assessing canine food preference: the two-pan test and the one-pan test. Neither test fully accounts for the complexity of the canine feeding experience, nor do they provide applicable representations of canine feeding behavior in the home. The objectives of this study were to: 1) determine whether dogs display a preference for animal ingredient-based diets as compared to vegetable ingredient-based diets and 2) examine whether dogs experience neophobia when presented with a novel diet. Eight adult Beagles (average age = 24 months, weighing 8-12kg) were individually fed each of four novel diets in a 4 x 4 replicated Latin square design, with 10-d treatment periods and four dietary treatments. Data were analyzed using a mixed model with repeated measures and significance was declared when P<0.05. The diets were: animal and vegetable ingredient-based diets, and animal and vegetable-based ingredients diluted with anhydrous alpha-d-glucose. The diluted diets were used for a larger study to determine true mineral digestibility. Dogs were fed twice per day. Behavioural observations were made by video on the first, and last two days of each 10-day treatment period of both AM and PM feedings. Time to consume feed, distraction, hesitation, level of anticipation pre-consumption, and interest post-consumption were recorded. Dogs experienced initial disruptive (neophobic) effects of a novel diet. Neophobia was demonstrated by a decreased (slower) rate of consumption, increased distraction during consumption of the diet, and increased hesitation on the first day of each new diet (p<0.05). The level of interest post-consumption was highest when dogs consumed the animal-based ingredients diet (p<0.05). This study presents insights into canine food preference assessment methods that may more accurately represent the dog owner’s experience. Further research is required to determine the minimum length of time necessary to eliminate neophobia to food. In addition, future research should also aim to establish whether interest post consumption is due primarily to food preference or acute satiety.
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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 | 0,000 | 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 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 ».