Authenticity Analysis of Pear Juice Employing Chromatographic Fingerprinting
Bibliographic record
Abstract
Pear juice is predominately composed of carbohydrates/polyols (>95% of the total soluble solids), making it susceptible to adulteration by the addition of less expensive commercial sweeteners. In this research, the major carbohydrate and polyol (fructose, glucose, sucrose, and sorbitol) content of 32 pure pear juices representing five world producing regions and three years of production was determined. Additionally, methods employing oligosaccharide profiling to detect the debasing of these samples with four commercial sweeteners (HFCS 55 and 90, TIS, and HIS) were developed using capillary gas chromatography with flame ionization detection (CGC-FID) and high-performance liquid chromatography with pulsed amperometric detection (HPAE-PAD). Detection limits for the four commercial sweeteners ranged from 0.5 to 5.0% (v/v). In addition, the developed CGC-FID method could be used to (a) detect the addition of pear to apple juice via arbutin detection and (b) determine if a pear juice was produced using enzymatic liquefaction via the presence of O-β-d-glucopyranosyl-(1→4)-d-glucopyranose (cellobiose), all within a single chromatographic analysis.
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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 | 0.001 |
| 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".