Evaluation of the Physicochemical, Nutritional, Textural, and Sensory Characteristics of Extrudates From Sorghum and Orange‐Fleshed Sweet Potato Flour Blends
Bibliographic record
Abstract
This study is aimed at producing extrudates using sorghum and orange‐fleshed sweet potato (OFSP) flour in varying ratios (90:10, 80:20, 70:30, 60:40, 50:50, 40:60, 30:70, and 20:80), with extrudates made from 100% sorghum serving as the control. The puffed snacks’ physicochemical, nutritional, textural, and sensory qualities were assessed, and the obtained data were analyzed through ANOVA. Our findings revealed notable variations in the physicochemical properties of the puffed snacks, showing a decrease in moisture, fat, protein, and crude fibre content as the percentage of OFSP flour increased. Furthermore, increased substitution of sorghum flour with OFSP in the extrudates led to a corresponding rise in vitamin A, B1, and C levels from 0.24, 0.15 and 0.21 mg/100 g in the control to 1.30, 0.19, and 1.82 mg/100 g in the extrudates made from 20% sorghum. More so, samples with increased OFSP content were preferred regarding springiness, chewiness, gumminess, and cohesiveness, whereas those with elevated percentages of sorghum received higher likeness for adhesiveness and stringiness. Extruded samples containing 80% and 90% sorghum levels received the highest overall acceptance ratings of 7.15 and 7.18, respectively. The research results are essential for the food industry to produce nutritious extrudates with appealing sensory characteristics and textures.
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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) | 0.001 | 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 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".