Proximate Composition and Sensory Properties of Complementary Food Formulated From Malted Pre-Gelatinized Maize, Soybean and Carrot Flours
Bibliographic record
Abstract
The nutrient and sensory properties of malted pre-gelatinized maize supplemented with varying amounts of soy and carrot flour was evaluated. The blends (Malted pre-gelatinized maize flour : Soy flour : Carrot flour) in grams were: A (80: 20: 0), B (73.125: 23.125: 3.75), C (66.250: 26.250: 7.50), D (65.625: 23.125:11.25), E (65: 20:15), F (63.125: 33.125: 3.75), G (63.125:25.625: 11.25), H (60: 25: 15) and I (100:0:0). There were significant (P ≤ 0.5) differences in the proximate composition of the blends. The moisture content ranged between 3.55 - 8.10%. The protein content of the samples increased (P ≤ 0.5) with the increase in soy substitution and varied from 11.61% for the control (sample I) to 21.53% for sample F. The fat, ash and crude fibre content of the blends varied from 1.68 - 10.86, 1.45 - 2.8 and 0.20 - 4.40% respectively. The control had significantly (P ≤ 0.5) the highest carbohydrate content of 75.61%, while it varied between 55.30 and 71.60 % for others. The energy values varied from 360.43 - 405.00 Kcal/g. The sensory scores were based on a 9-point hedonic scale, with 1 and 9 expressed as dislike extremely and like extremely. The assessors’ likeness for the sensory attributes (colour, texture, taste, aroma and general acceptability) was below neither like nor dislike. This study revealed that substitution with soybeans and carrot flours increased the nutrient composition of the malted pre-gelatinized maize, soybean and carrot flour blends. Particularly the soy flour as sample F with the highest soy flour substitution (33. 123g) had significantly the highest protein (21.53%), fat (10.86%) and energy (405 Kcal/g) values. This would be recommended for good quality porridge. Although, the sensory analysis revealed the need for further investigation on processing methods especially the malting process as to enhance the overall acceptability of the product.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".