High-performance miniature integrated infrared spectrometers for industrial and biochemical sensing
Bibliographic record
Abstract
High-resolution, miniature integrated spectrometers have been constructed for the NIR and MIR spectral ranges, based on MPBT's proprietary IOSPEC technology. Advanced slab-waveguide integrated optics have been employed to extend the performance of the miniature IR spectrometers to rival that of much larger FT-JR spectrometers. Monolithic integration of the miniature spectrometer, input optics and detector array provides a very compact and robust package that is suitable for industrial and field environments. Despite the compact size of the spectrometers, resolutions of 4 to 8 cm-1 are achievable over dedicated spectral ranges (2000 to 4000 nm, respectively). These spectrometers are coupled to 256channel linear detector arrays controlled by software based on Visual C++ to provide rapid spectral acquisition and analysis. This technology facilitates on-line infrared spectral analysis of an industrial or biochemical process at scan rates exceeding 200 spectra/sec. Since the spectral data is measured directly, significantly less data processing is required than for FT-JR techniques, allowing more CPU time for spectral identification and analysis. Multi-channel, time-resolved spectral measurements permit the study of the intermediate steps in a process or reaction. This paper discusses recent advances in the performance of the miniature integrated spectrometers. New detector geometries and data processing techniques have facilitated a substantial improvement in the overall system SNR over that feasible with typical sequentially-scanned detector arrays. Preliminary experimental transmittance spectra of optical filters and plastics are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.004 | 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".