Soluble fiber and resistant starch components in some Indian and Canadian wheat varieties and in a wheat-soy product - Chapati
Bibliographic record
Abstract
This study aimed to quantify resistant starch (RS) beta-glucans (BG) and fructo-oligosaccharides (FOS) in Indian and Canadian wheat varieties and in chapaties made from these; and to assess the effects of soy flour on the levels of these components and its effects on the sensory and functional properties of the wheat-soy chapaties. Seven wheat varieties (Indian / Canadian) were milled into flour; supplemented with 0 % (control), 10 %, 20 % or 30 % defatted soy flour and made into chapaties. Flours and chapaties (freeze-dried, pulverized) were assayed for BG, FOS, RS and simple sugars (glucose / sucrose). Sensory evaluation was carried out by (9 point) hedonic rating of chapaties by 20 untrained Indian panelists. Flour water-holding capacity and water absorption indices (WAI) were determined. RS content of flours ranged from 7.1 g/100 g to 12.6 g/100g, but decreased when made into chapaties, (< 1 g/100 g), and decreased further with soy flour addition. BG content in flours ranged from 0.8 g/100 g to 1.4 g/100 g, while FOS content ranged from 1.3 g/100 g to 2.3 g/100 g. Minimal changes were observed in BG and FOS content when made into chapaties. Simple sugars were minimal in flours and chapaties. WAI of wheat flour was increased with addition of soy bean flour. Addition of up to 30 % soybean flour elevated the sensory acceptability of chapaties. While there is a decrease in RS with chapati making, the levels of BG and FOS are largely unchanged with processing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".