DETERMINATION AND PREDICTION OF ODOR THRESHOLDS FOR ODOR ACTIVE VOLATILES IN A NEUTRAL APPLE JUICE MATRIX
Bibliographic record
Abstract
ABSTRACT Odor thresholds were determined for 10 odor active compounds (OAC) in apple juice, using three‐alternate forced choice methodology. Thresholds were determined in a neutral juice matrix by 25–30 panelists in duplicate at 22C. Individual thresholds were calculated using the best estimate threshold method. Group thresholds were determined using the geometric mean of the individual thresholds. OAC differed substantially in their concentration ranges, aroma thresholds (0.06–5.49 µL/L) and response rates (1.5–234.5% correct response/[µL/L]). Juice thresholds exceeded water thresholds by ∼5–600 times. Multiple linear regressions were used to develop models to predict juice thresholds from water thresholds and physical constants, for apple juice (AJ) and published orange juice (OJ) values. The simplest most practical models utilized just one variable, the logarithm of the water threshold. Coefficients of correlation (R2) for the AJ and OJ models were 71.7 and 72.8%, respectively, and provided satisfactory estimates of juice thresholds. PRACTICAL APPLICATIONS This research established aroma thresholds in a juice matrix for 10 prevalent esters in apples and related them to water thresholds using log models. These thresholds allow industry to calculate more realistic odor activity values for quality control and research purposes in the apple juice industry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".