Noninvasive, label‐free, and quantitative monitoring of lipase kinetics using terahertz emission technology
Bibliographic record
Abstract
Abstract Enzymes catalyze chemical transformations of great importance in many fields, and analysis of the rate of these transformations is equally important. The latter are typically monitored using surrogate substrates that produce quantifiable optical signals, owing to limitations associated with “label‐free” techniques that could be used to monitor the transformation of original substrate molecules. In this study, terahertz (THz) emission technology is used as a noninvasive and label‐free technique to monitor the kinetics of lipase‐induced hydrolysis of several substrate molecules (including the complex substrate whole cow's milk) and horseradish peroxidase‐catalyzed oxidation of o‐phenylenediamine in the presence of H2O2. This technique was found to be quantitative, and kinetic parameters are compared to those obtained by proton NMR spectroscopy or UV/Vis spectroscopy. This study sets the stage for investigating THz emission technology as a tool for research and development involving enzymes, and for monitoring industrial processes in the food, cosmetic, detergent, pharmaceutical, and biodiesel sectors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".