Multi-Wavelength Dye Concentration Determination for Enzymatic Assays: Evaluation of Chromogenic <i>para</i>-Nitrophenol over a Wide pH Range
Bibliographic record
Abstract
Enzymatic assays need robust, rapid colorimetric methods that can follow ongoing reactions. For this, we developed a highly accurate, multi-wavelength detection method that could be used for several systems. Here, it was applied to the detection of para-nitrophenol (pNP) in basic and acidic solutions. First, we confirmed by factor analysis that pNP has two forms, with unique spectral characteristics in the 240 to 600 nm range: Phenol in acidic conditions absorbs in the lower range, whereas phenolate in basic conditions absorbs in the higher range. Thereafter, the method was used for the determination of species concentration. For this, the intensity measurements were made at only two wavelengths with a microtiter plate reader. This yielded total dye concentration, species relative abundance, and solution pH value. The method was applied to an enzymatic assay. For this, a chromogenic substrate that generates pNP after hydrolysis catalyzed by a lipase from the fungus Yarrowia lipolytica was used. Over the pH range of 3-11, accurate amounts of acidic and basic pNP were determined at 340 and 405 nm, respectively. This method surpasses the commonly used single-wavelength assay at 405 nm, which does not detect pNP acidic species, leading to activity underestimations. Moreover, alleviation of this pH-related problem by neutralization is not necessary. On the whole, the method developed is readily applicable to rapid high-throughput of enzymatic activity measurements over a wide pH range.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".