Lipids in microalgae: Quantitation by acid-dichromate method in microtiter plate v2
Bibliographic record
Abstract
This protocol describes a method for quantitating total lipids in microalgae using the acid-dichromate method, a widely used colorimetric analysis technique. We present a procedure utilizing a 96-well microtiter plate for safe and efficient sample handling, enabling high throughput. Only 200 - 500 µl of 0.15% acid-dichromate is required per sample, significantly reducing the amount of corrosive and toxic reagent used. Furthermore, we demonstrate that measuring absorbance at 348 nm provides five times higher sensitivity in lipid quantitation compared to absorbance at 440 nm. A preliminary test for lipid-unknown samples is included to minimize uncertainty in the measurements. This test ensures that the method performs reliably within the detection range of 20 to 80 µg of lipids. Without this test, samples with lipid concentrations outside this range (either less than 20 µg or greater than 80 µg) may result in inaccurate or failed measurements. Specifically, samples with concentrations above 80 µg exhibit a linear response with an opposite slope, which could cause lipid concentrations to be underestimated if the calibration curve based on the 20 to 80 µg range is used. Accurate quantification can be achieved with as little as 20 µg, and the working detection limit is approximately 5 µg.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.011 |
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".