Rapid Detection of Patchouli Oil Adulteration Using Support Vector Machine Classification and Discriminant Analysis with Near-Infrared Spectroscopy
Bibliographic record
Abstract
Patchouli oil (PO) is a valuable commodity in the global market due to its numerous pharmaceutical properties.Consequently, adulteration of pure patchouli oil with other oil substances has become a prevalent issue.This study aims to develop rapid and nondestructive classification models for detecting patchouli oil fraudulence using support vector machine classification (SVMC) and discriminant analysis (DA) based on nearinfrared (NIR) spectroscopic data.Pure PO was adulterated with oleoresin oil in proportions of 75:25 and 50:50 to create the adulterated samples.A total of 40 samples were used for model calibration, and 9 samples were employed for validation.NIR spectra covering a wavelength range of 1000-2500 nm were collected for all samples and preprocessed using the multiplicative scatter correction algorithm.Classification models based on SVMC and DA were developed using principal component analysis.Results demonstrated that pure patchouli oil and oleoresin oil could be accurately classified, while adulterated PO with a 50:50 proportion was detected with a maximum accuracy of 87%, and the 75:25 proportion yielded a detection accuracy of 97%.These findings suggest that combining NIR spectroscopy with appropriate classification models can efficiently detect patchouli oil adulteration in a rapid and non-destructive manner, offering a promising method for quality control in the industry.
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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".