DETERMINATION AND PREDICTION OF ODOR THRESHOLDS FOR ODOR ACTIVE VOLATILES IN A NEUTRAL APPLE JUICE MATRIX
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ( R 2 ) 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it