The clustering analysis of the illegal cooked oil based on high pressure liquid chromatography
Bibliographic record
Abstract
Objective This paper provides an effective testing method of the illegal cooked oil.Methods The triglyceride and other impurities in oil were as targets to be analyzed using MALDI-TOF mass spectrum and high pressure liquid chromatography(HPLC).First,MALDI-TOF MS was employed for preliminary analysis of oil samples;then 14 feature HPLC peaks according to the peak time and area percentage were used to carry on the standardized processing.By using the cluster analysis technology,the 41 known oil samples were gathered into 6 categories and the sample databases were established,and the discriminate analysis of the unknown samples enabled us to inspect these oils.Results Qualified edible oil mixed with 10% of the illegal cooked oil could be detected.Using this method for a blind test of 26 samples,the results showed that the total accuracy rate was 92.3%.Conclusion This method is an effective reference method for illegal cooked oil detection.
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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".