Authentication of Dog Fat With Gas Chromatography-Mass Spectroscopy Combined With Chemometrics
Bibliographic record
Abstract
Indonesia is a country with a majority of Muslim population. As a Muslim majority country, providing halal products becomes a liability. The problem that has received a lot of attention right now is the concern that contamination of food products by meat, one of them is dog meat. The purpose of this study was to authenticate dog fat by using Gas Chromatography-Mass Spectrophotometry (GC-MS)combined with chemometrics. Dogs that used in the study were taken from Bantul, Yogyakarta. Dog snacks were heated in an oven at 90-100ᵒC for approximately one hour. Oils / fats obtained from derivatization process was carried out by using NaOCH3 and BF3. The methyl ester compound was injected into the GC-MS instrument system. The Results of this study was dog fat that analyzed by GC-MS contains 9 types of fatty acids, namely: lauric (1.19 ± 0.25)%, myristate (4.33 ± 0.30)%, pentadekanoate (0.12 ± 0.02)%, palmitoleate (4.60 ± 0.07)%, palmitate (12.80 ± 2.90)%, margarate (0.13 ± 0.09)%, oleate (44 , 33 ± 5.22)%, stearic (14.71 ± 0.32)%, and arachidonic (1.29 ± 0.11)%. Total content of fatty acids in dogs was 50.22% and saturated fatty acids were 33.03%. Chemometric grouping with the Principles Component Analysis (PCA) shows that dog fat is very close to lard. The fatty acids that contained in dog fat can be used to authenticate dog meat.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".