Optimization of Flow Injection (FI) – Spectrophotometry for Hydroquinone Analysis
Bibliographic record
Abstract
Hydroquinone is one of the phenolic compounds used in various cosmetic products for skin lightening as it can inhibit tyrosinase enzyme in producing melanin. However, hydroquinone is classified as a toxic compound, therefore, several countries such as Africa, Canada, and Indonesia prohibits hydroquinone usage in cosmetics. This research was focused on the development of a method for hydroquinone analysis using flow injection (FI) combined with spectrophotometry based on the reaction of hydroquinone with phloroglucinol in alkaline condition producing orange complex detected at 493 nm. The FI method was optimized based on operational factors and chemical factors in order to achieve sensitivity. The maximum sensitivity of FI method was achieved under operational condition of 100 μL sample volume, 100 cm mixing coil 1, 50 cm mixing coil 2 and 2.8 mL/min with the chemical condition of 0.005 mol/L NaOH and 0.001 mol/L phloroglucinol. Under these optimum conditions, the proposed method showed linearity in the range concentration of 2 – 80 mg/L and the method was applied to cosmetic sample with acceptable recovery
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".