Converting Apple Textural Parameters Obtained from Penetrometers and Their Relationships with Sensory Attributes
Bibliographic record
Abstract
Textural attributes of apple impact consumers’ acceptance of the fruit, and are frequently measured by researchers and industry experts to evaluate the fruit quality at different stages of production and marketing. Various instruments are used to conduct these textural evaluations in research and industry settings. The application of different instruments makes the comparison and integration of results extremely difficult. The main objectives of this study were to compare data obtained from three widely used textural instruments, investigate their relationships with each other and with sensory evaluations, and develop models to convert data among instruments. Three penetrometers were included in the study: (1) Fruit Texture Analyzer (FTA); (2) Mohr Digi-Test-2 (MDT-2); and (3) TA.XTplus Texture Analyzer (TA.XTplus). Eight apple varieties with a range of textural attributes were selected. Eleven sensory judges evaluated three apple slices (1/8 apple) from each variety. The instrumental measurements were conducted on 10 apples per instrument from each variety, with two measurements on each apple. Results of principal component analysis indicated that 95.82% of the variation in the texture data could be explained using only two principal components. Linear and nonlinear regression models were developed to convert data obtained from an instrument to those from other instruments.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".