Influence of Bamboo Shoots (Dendrocalamus asper) Flour Addition and Baking Temperatures on the Sensory and Physical Characteristics of Cookies
Bibliographic record
Abstract
This study examines the effects of incorporating bamboo shoot flour and varying baking temperatures on the quality of cookies, in an effort to enhance their palatability and consumer acceptability.Utilizing a factorial randomized block design, this investigation was carried out in triplicate, considering two key factors: the proportion of bamboo shoot flour incorporated (A) and the baking temperature (B) at three different levels (140℃, 145℃, and 150℃).It was found that the use of bamboo shoot flour in cookie production is safe, with a recorded HCN content of 4.86 ppm, well beneath the maximum safety standard for consumption.However, bamboo shoot flour demonstrated mild antioxidant activity, attributed to the high temperatures and prolonged processing times employed in preparation, which likely diminished the antioxidant content.Significant effects of both bamboo shoot flour incorporation and baking temperature on the sensory and physical properties of the cookies were observed.The most desirable ratio of bamboo shoot flour to wheat flour was found to be 1:2, and the optimal baking temperature range was between 140℃ to 145℃.These parameters were found to yield the highest preference across nearly all evaluated metrics, suggesting a potential strategy for enhancing the use of underutilized bamboo shoots in snack foods.
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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 | 0.000 |
| 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".