Improved Acetone-Butanol-Ethanol (ABE) Solution Analysis Using HPLC: Chromatograph Spectrum Deconvolution Using Asymmetric Gaussian Fit
Bibliographic record
Abstract
Currently, the analysis of acetone-butanol-ethanol (ABE) broths is performed using both High Performance Liquid Chromatography (HPLC) and Gas Chromatography (GC) for each sample since GC cannot be used in quantifying sugars and HPLC methods are not yet efficient enough to detect all components separately. In this study, a novel method was developed to quantify all main components present in ABE model solutions (acetone, butanol, ethanol, butyric acid, acetic acid, glucose and xylose) using only HPLC. Although the HPLC operating conditions were optimized to obtain the best possible resolution in HPLC chromatograms, it was observed that the peaks for butyric acid, acetone and ethanol overlapped. The same trend was observed for glucose and xylose. Using the asymmetric Gaussian fit, a program was written in MATLAB to detect the overlapped peaks, deconvolute them and calculate the area of each separated peak. The concentrations of each component were then calculated using the areas and the calibration curves for each component. Experimental results show that this method works well for the ABE model solutions and can be used to quantify all components in the solution when there are some overlapped peaks in the HPLC chromatograms.
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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 | 0.001 | 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) | 0.002 | 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".