Rapid Fluorescence EEM spectra acquisition using super-cycle Hadamard-Transform Multiplexing
Bibliographic record
Abstract
Hadamard-transform multiplexing has recently been applied to increasingly complex spectroscopic techniques. It had been shown that the data acquisition time for fluorescence Excitation-Emission-Matrix spectroscopy can be reduced by one or two orders of magnitude using Hadamard-Transform multiplexing of the excitation light using a programmable lightsource. In these previous studies, the data acquisition rate had been limited by the time it took to record an EEM, i.e. to complete one cycle of multiplexed excitation spectra. The extraction of chemical information, such as concentration and chemical identity, is then obtained from parallel factor (PARAFAC) analysis of the sequence of EEM spectra. In this contribution we increase the data acquisition rate by another order of magnitude, i.e. to the time it takes to record a single excitation spectrum. Our algorithm is entirely based on improved data processing, i.e. it can be applied to previously recorded Hadamard-Transform multiplexed data sets. The algorithm is based on three previously unexplored approaches: (1) we perform a PARAFAC multivariate analysis on the raw (multiplexed) data set (2) the PARAFAC loading vectors are obtained prior to obtaining the score vectors, (3) when loading vectors are difficult to obtain from the stack of EEM spectra, we instead use a rolling-average approach to considerably increase the number of spectra and the stability of the fit. Analysis of experimental data shows that fluorescence EEM spectra with 7 excitation wavelengths and over 1000 emission wavelengths can be obtained in less than 20 ms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".