Hadamard-Transform Fluorescence Excitation-Emission-Matrix Spectroscopy
Bibliographic record
Abstract
We present a fluorescence excitation-emission-matrix spectrometer with superior data acquisition rates over previous instruments. Light from a white light emitting diode (LED) source is dispersed onto a digital micromirror array (DMA) and encoded using binary n-size Walsh functions ("barcodes"). The encoded excitation light is used to irradiate the liquid sample and its fluorescence is dispersed and detected using a conventional array spectrometer. After exposure to excitation light encoded in n different ways, the 2-dimensional excitation-emission-matrix (EEM) spectrum is obtained by inverse Hadamard transformation. Using this technique we examined the kinetics of the fluorescence of rhodamine B as a function of temperature and the acid-driven demetalation of chlorophyll-a into pheophytin-a. For these experiments, EEM spectra with 31 excitation channels and 2048 emission channels were recorded every 15 s. In total, data from over 3000 EEM spectra were included in this report. It is shown that the increase in data acquisition rate can be as high as [{n(n + 1)}/2]-fold over conventional EEM spectrometers. Spectral acquisition rates of more than two spectra per second were demonstrated.
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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 | 0.000 |
| 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".