A Standardized Method for the Instrumental Determination of Cooked Spaghetti Firmness
Bibliographic record
Abstract
ABSTRACT A standardized method to determine cooked spaghetti firmness was developed. The effects of process and instrument variables were investigated and optimized to provide reproducible results between laboratories and to enable discrimination among samples with similar firmness characteristics. Commercial spaghetti samples of varying thickness were chosen to artificially create a range in firmness, and used to investigate the effect of a wide range of variables on cooked spaghetti firmness including sample preparation, cooking procedure, postcooking treatment, sample presentation, and instrument settings. Cooked spaghetti firmness determined using a TA‐XT2i texture analyzer was significantly affected by optimum cook time, postcook cooling, rest time, and crosshead speed (P < 0.001), as well as strand length, spaghetti to cooking water ratio, number of strands cut, and strand position (P < 0.05). Although previous work showed a reasonable correlation between laboratories when using in‐house methods (r = 0.85), the correlation improved to r = 0.96 when using the standardized method to analyze 29 commercially produced spaghetti samples. The Spearman rank correlation increased from rs = 0.81 to rs = 0.95, prestandardization and poststandardization, indicating greater agreement between laboratories in sample ranking.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".