Estimating Gurjun Oil Adulteration in Aceh Patchouli Oil Using NIRS and Multivariate Analysis
Bibliographic record
Abstract
Indonesia, a leading supplier of patchouli oil globally, produces a significant proportion of this high-value commodity in Aceh.Given the high economic value of patchouli oil, adulteration practices with less expensive oils, such as palm, gurjun, and turpentine oil, are commonplace.These adulterations not only degrade the quality of the oil but also pose potential safety risks, health hazards, and non-compliance with natural attributes.Among the adulterants, gurjun oil is frequently used due to its blendability and similarity in color and aroma to patchouli oil.This study was designed to develop a quantification model to estimate the gurjun oil content adulterating Aceh patchouli oil.The model employed near-infrared reflectance spectroscopy (NIRS) technology in combination with multivariate analysis methods, specifically principal component regression (PCR) and partial least squares (PLS) regression.To enhance the accuracy level of the NIRS calibration model, mean normalization (MN) and de-trending (DT) spectrum pretreatments were applied.The findings indicate that the application of NIRS technology coupled with multivariate analysis can effectively address the issue of gurjun oil adulteration in patchouli oil.Moreover, the PLS calibration method was shown to outperform PCR.The residual predictive deviation (RPD) index for the PLS model was found to be 2.34, compared to 1.85 for the PCR model.Spectrum pretreatments successfully enhanced the performance of the model, with calibration RPD indices for the PLS-MN and PLS-DT models registering at 2.98 and 2.59, respectively.In general, both models were deemed viable for application as the PLS-MN and PLS-DT models, after validation, yielded range to error ratio (RER) values of 3.39 and 3.60, respectively.Thus, this study underscores the potential of employing NIRS technology and multivariate analysis to combat the prevalent issue of patchouli oil adulteration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".