Development of a spectrophotometric method for the quantification of c-phycocyanin in the cyanobacteria Aphanizomenon flos-aquae
Bibliographic record
Abstract
Abstract The current study presents the development of a reliable method for the quantification of c-phycocyanin. It was found that the spectrophotometric method commonly used for c-phycocyanin quantification tends to overestimate the actual amount of c-phycocyanin in AFA samples. Thus, the aim of this study was to account for c-phycocyanin variation between cyanobacteria species in order to reliably adapt the spectrophotometric quantification method of c-phycocyanin for Aphanizomenon flos-aquae (AFA). High performance liquid chromatography (HPLC) was used to avoid interference between molecules. The existing spectrophotometric equations for the quantification of AFA c-phycocyanin were adapted using a c-phycocyanin standard. The method was then used to obtain a new set of spectrophotometric quantification equations that were adapted to the strain of interest and ensured the accuracy of c-phycocyanin quantification while continuing to use a rapid, simple, and inexpensive method for pigment quantification. The method developed here could be adapted to improve the quantification methods for other types of phycocyanin, cyanobacteria, or even other compounds of interest that are currently quantified by spectrophotometry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".