Sensory and nutritional evaluation of nine types of millet substituted for polished white rice in select Indian meal preparations
Notice bibliographique
Résumé
This study was conducted to test the suitability of using nine types of millets namely finger millet, pearl millet, white and yellow sorghum, little millet, barnyard millet, proso millet, kodo millet, and browntop millet in seven popular Indian meal preparations based on sensory characteristics and nutrient value. The popular Indian meal preparations tested were boiled grain, dosa, idli, bisi belle bath, pulao, puttu, and pongal. In total, 53 variations in meal preparations were developed using the millets and seven polished white rice-based meal preparations were developed as control. The main findings indicated that meal preparation crafted from various millets garnered overall sensory scores closely resembling to those derived from polished white rice. Notably, little millet exhibited high scores in pongal and dosa, and achieved elevated overall sensory scores compared to meal preparation from polished white rice. Bisi belle bath made of barnyard millet scored higher in overall sensory score than polished white rice. Moreover, there was significant association between some types of millets’ overall sensory characteristics ( p < 0.005) with polished white rice-based meal preparations. In terms of nutrient value, all the millet-based meal preparations had significantly high nutritional value compared to those made with polished white rice ( p < 0.05). Especially calcium content of the meal prepared with finger millet was significantly higher compared to polished white rice-based meals ( p < 0.05). Puttu, idli, and dosa prepared with finger millet had calcium content of 59.4, 10.8, and 70.9 mg/100 g compared to those prepared with the polished white rice which had only 1.3, 6.3, and 9.2 mg/100 g. The magnesium content of all millet-based meal preparations was generally several-folds higher compared to the polished white rice-based meal preparations ( p < 0.05). There is a significant difference in the fiber content of the meals prepared with millets compared to the meals prepared with polished white rice ( p < 0.05). This study was conducted using millets that are locally available and does not represent all the millet varieties available globally, as each type of millet has a wide range of varieties. Therefore, it is important to understand and choose the type and variety of millet while enhancing the nutritional value of diets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».