Adulteration of Apple with Pear Juice: Emphasis on Major Carbohydrates, Proline, and Arbutin
Bibliographic record
Abstract
Detection of juice-to-juice adulteration based on chemical composition studies is a common method used by government regulatory agencies and food companies. This study investigated the use of major carbohydrate (fructose, glucose and sucrose), polyol (sorbitol), proline, and phenolic profiles as indicators of pear adulteration of apple juice (PAAJ). For this work, a total of 105 authentic apple juice samples from 13 countries and 27 authentic pear juice samples from 5 countries were analyzed. Because the major carbohydrate ranges for these juices showed significant overlap their use as markers for PAAJ detection would be very limited. It was found that sorbitol and proline means for apple and pear were significantly different; however, their broad natural ranges would afford PAAJ at levels up to 30% without detection. In addition, careful selection of the pear juice used as the adulterant would further limit the usefulness of these markers for PAAJ detection. Arbutin was conclusively identified as a marker for pear juice on the basis of its presence in all 27 authentic pear samples and its absence (<0.5 microg/mL) in all 105 apple juice samples analyzed in this study. The application of the developed HPLC-PDA method for arbutin analysis to detect PAAJ at levels as low as 2% (v/v) was demonstrated. A confirmation method for the presence of arbutin in pure pear juice and apple adulterated with pear juice was introduced on the basis of the hydrolysis of arbutin to hydroquinone employing beta-glucosidase, with reactant and product monitoring by HPLC-PDA.
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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 | 0.000 | 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 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".