Elucidating the Origin of Milk Products on the Chinese Market Using Multi Element Stable Isotope Technique
Bibliographic record
Abstract
Preference for foreign milk products is the cause of the economically motivated adulteration of milk products on the Chinese market. The present study was done to ascertain the feasibility of utilizing δ2 H, δ18O and δ15N stable isotope techniques in elucidating the geographical origin of milk products on the Chinese market. 42 milk samples from the United States of America, Canada, Southern China, Northern China, Australia and New Zealand were analyzed. An isotope ratio mass spectrometer with a combination of a high-temperature conversion elemental analyzer, Thermo-Fisher was used. Statistical analysis was performed using one-way ANOVA. The study revealed that both δ2 H and δ18O had a wide range of mean values: 13.86 to 22.25‰ and -82.86 to -28.5‰, respectively. There was a significant difference in the δ2 H (n=7; F=20880, P=7.876E-43) and δ18O (n=7; F=1399.0; P=9.215E-29) composition of the milk samples from the different regions. It was observed that δ2 H and δ18O composition is helpful in elucidating milk products from different regions on the Chinese market (P<0.05). The range of the mean δ15N values of the milk samples was very close, 3.06 to 5.61‰, indicating a possible limitation in employing δ15N isotopic technique. The nitrogen stable isotope could not provide a clear distinction for the milk products because δ15N of an animal reflects that of the diet. Hence in cases of similar diet, it may not be possible to provide a distinction between the animals using this technique.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".