A simple and low-cost method for determination of methanol in alcoholic solutions
Bibliographic record
Abstract
Methanol poisoning can occur through consumption of methanol-containing alcohols, especially in areas where production, distribution, sale and consumption of alcohol is lawfully prohibited. Due to its toxic potency, determination of methanol in alcoholic solutions is important. The aim of the present study was to develop a rapid, simple and inexpensive method for quantification of methanol in alcoholic solutions that uses minimal equipment available in most laboratories. The method developed is based microdistillation and chromotropic acid, which can be conducted without sophisticated instruments or personal. The system consists of a micro-tube suspended in a falcon tube to function as a collector. Methanol is separated from wine by microdistillation at 90°C in water bath and converted to formaldehyde in the collector. The collector contains an acidic permanganate solution that converts methanol to formaldehyde. Formaldehyde was then quantified by use of chromotropic acid in concentrated sulfuric acid. Experimental variables were optimized by using central composite design (CCD). Method detection and quantification limits were 183 mg L −1 and 584 mg L −1 , respectively. The percent relative standard deviation (RSD%) were between 6.4 and 7.9. Accuracies were between 89.6 % and 92.4 %. Concentrations of methanol in five alcoholic solutions were between 2.9×10 4 and 3.0×10 4 , mg/L, v/v (ppm). Due to its simplicity and cost effectiveness, this method can be used for routine, real-time determination of methanol in alcoholic solutions.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".