Taguchi's methodology for determining optimum operating conditions in hydrothermal pretreatments applied to canola seeds
Bibliographic record
Abstract
The aim of the present work was to determine the optimum operating conditions in hydrothermal pretreatments applied to canola seeds. The effect of these pretreatments on the canola oil extraction yield and quality was evaluated. Samples were characterized by proximate analysis. Acidity and peroxide value were studied as parameters for the determination of oil quality. Seeds were exposed to direct steam contact in an autoclave. Pretreatments were carried out using different temperatures (100, 120 and 130 °C), exposition times (5, 15 and 30 min) and seed granulometry (ground seeds, with a particle size in a range from 0.420 to 1.000 mm; broken seeds, with a particle size ranging from 1.000 to 1.410 mm, and entire seeds). After each hydrothermal pretreatment, oil extraction was carried out by the Soxhlet method (hexane). The Taguchi method was followed in order to explore the optimum operating conditions by using an L9 experimental design, and select the most favourable levels of each variable. Initial oil content was 44.2% dry basis (db). The selected optimum experiment, using a temperature of 120 °C, a time of 5 min and broken seeds, generated an oil yield increase of 20% compared with non‐hydrothermally treated seeds, whereas quality parameters remained within the accepted values for trade standards.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".