Use of millets for partial wheat replacement in bakery products
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".