Microscope-FTIR Spectrometry Based Sensor for Neurotransmitters Detection
Bibliographic record
Abstract
In this work, we present a new sensing approach for aqueous samples based on the microscope-FTIR spectrometer and applied for neurotransmitters. Our contribution in this work consists of a new sample handling system for the microscope-FTIR spectrometer based on a total reflective mirror, a heated hydrophobic layer for solvent removal/evaporation and sample confinement and a microfluidic system that handles sample injection unlike standard sample handling system which was based only on a total reflective mirror. In addition, another part of our contribution consists of proposing a new algorithm to extract molecular composition of the solution with high estimation ratios and based on the analysis of detected peaks on IR spectra. The data acquired from the microscope-FTIR spectrometer was analyzed by a newly developed algorithm to identify each neurotransmitter in homogeneous and non-homogeneous solutions with high selectivity. We used six neurotransmitter molecules (Dopamine hydrochloride, L-Ascorbic acid, Acetylcholine chloride, y-Aminobutyric, Glycine and L-Glutamic acid). The results obtained based on the algorithm developed showed that, using the new system, the six neurotransmitters can be identified in homogeneous and mixture solutions with an estimation ratio range of 88.8%-100% for Dopamine hydrochloride, 80%-100% for L-Ascorbic acid, 75%-100% for Acetylcholine chloride, 75%-100% for L-Glutamic, 77.7%-100% for y-Aminobutyric and 75%-100% for Glycine.
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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".