Chemometrics Analysis Combined with GC-MS and NMR Spectroscopy Analysis of Fatty Acids as a Means of Discriminating Butterfat Adulteration
Bibliographic record
Abstract
The adulteration of butter has become a major problem in food industries. Butter has the similar characteristic to lard which makes lard a desirable adulterant in butter due to economic advantages. Therefore, the method of detection to analyse the adulteration practice must be developed. This study used NMR spectroscopy in combination with chemometrics for the authentication of butter from lard. The presence of lard as an adulterant in butter has been analysed using Gas Chromatography–Mass Spectrometry (GC-MS) and Nuclear Magnetic Resonance (NMR) spectroscopy with the aid of chemometric of Principal Component Analysis (PCA) and Discriminant Analysis (DA). PC1 described 82% of the variation while PC2 accounted for 15% of the variation resulted in a model that described 80% of the total variance in the data. With 82% of the peak variation along the first PC, it was clear that all seventeen samples of butter in the market and pure lard sample were formed according to their own group and showed two well-defined and well-separated group. DA model classified 100% of all samples accurately according to its group (butter and butter adulterated with animal fats), meaning that no samples were misclassified into the wrong group. Lard was successfully determined at 2.63 ppm. In this study, NMR and PCA analysis has successfully discriminated between the market sample and lard and the results established that there is no lard being adulterated in all commercial butter samples. This could be a potential identification approach to determine if the product has been deceived in market.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".