Use of millets for partial wheat replacement in bakery products
Notice bibliographique
Résumé
Bakery products account for a major part of the processed food industry. Bakery products are usually made with wheat flour, due to its unique functional characteristics to develop a gluten network when it is mixed with water. Millets, which are gluten free, are one of the oldest of cereals, cultivated since ancient times. The millets are best known for their drought resistance, shorter cultivation cycle and capability to grow in poor soils. Millets provide a wide range of health benefits and they are a good source of energy, proteins, minerals, vitamins and essential amino acids. Three minor millet grains, namely little (Panicum miliare or Panicum sumatrense), foxtail (Setaria italica), and barnyard millets (Echinochloa colona), were obtained from India and were used in this study. The primary objective was to explore the suitability of these three millet flours to replace wheat flour in the production of bread and cake. This objective was achieved by understanding the wheat-millet composite flours rheological behaviors, baking performance, change in secondary structures and heat and mass transfer during baking process.Peleg’s model was successfully applied to the water absorption experimental data and the Peleg’s constants such as Peleg’s rate constant (K1) and Peleg’s capacity constant (K2) were calculated for the three millets and the rate constant (K1) was for little millet (3.11, 1.7, 1.03), foxtail millet (3.07, 0.68, 0.93), barnyard millet (0.95, 1.06, 0.62) and capacity constant (K2) was for little millet (3.24, 3.12, 2.8), foxtail millet (3.23, 3.12, 2.63), barnyard millet (2.32, 2, 1.86) at the soaking temperature of 30, 40 & 50° C respectively. These constants decreased with the increase in the soaking temperature. Incorporation of the millet in the bread dough affected the dough rheology adversely in terms of workability, hardness, water absorption, etc. Little millet and foxtail millet exhibited better results in the dynamic rheological properties and barnyard millet highly negatively affected the dough rheology in terms of dough hardness, stability and dynamic rheological properties. Incorporation of millet flour increased the G’ and G’’ values of the bread dough however, the G’’ values were much higher when compared with G’ for all millet flours. This indicates that millet incorporated bread dough was more elastic than viscous.An increase in the millet concentration in bread dough decreased the baking performance of the dough and the higher millet incorporated bread was found to be hard and scored a lower value in the sensory evaluation. Similarly, an increase in millet concentration in cake batter decreased the baking performance of the batter. Higher millet incorporated cakes were found to be hard. In general, the little millet produced better bread and cake when compared with foxtail and barnyard millet. The overall acceptability of millet bread and cake were found to be higher in the sensory evaluations.All three millet flours exhibited similar FTIR spectroscopy when compared with wheat flour which indicates that the different millet flours have similar composition / functional groups. However, the millet flours were found to have some unique peaks such as one at 2853 cm-1 band which belongs to the lipids functional groups. Heat and mass transfer during little millet dough baking was studied and the Page and Henderson-Pabis models were used to express the baking kinetics. The coefficients and constants from the Page and Henderson-Pabis models were calculated from the baking data. Page model was found to be a better fit for the dough baking (drying) data. The coefficient and constants from both models were generally found to decrease with the increase in the millet concentration in the bread dough. The results presented in this research are useful for the development of bakery products using millets.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 |
| 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) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».