Portable LWIR hyperspectral imager based on MEMS Fabry-Perot interferometer and broadband microbolometric detector array
Bibliographic record
Abstract
The fast growing consumer electronics market of connected and wearable devices is driving a wealth of new applications. Personal capability for detecting and monitoring substances part of our everyday life (food, cosmetics, drugs, etc.) by spectroscopic means will soon become a reality as a number of new miniature spectrometers are being reported. These devices mostly operate in the visible and near infrared spectral region due to the readily available lowcost detectors in these spectral regions. However, enhanced selectivity is achievable in the molecular fingerprint spectral region (7-20 μm), allowing for applications that would be difficult or impossible at lower spectral wavelengths. To this end, a compact, portable, Long-Wave Infrared (LWIR) hyperspectral imager was developed. It is based on INO’s MICROXCAM-384 camera featuring a 384 x 288 pixel, 35 μm pitch uncooled bolometric broadband Focal Plane Array (FPA) and Fraunhofer ENAS’ 2 mm x 2 mm aperture MEMS tunable Fabry-Pérot Interferometer (FPI). The INO’s broadband FPA exhibits a Noise Equivalent Temperature Difference (NETD) lower than 25 mK (for the 8-12 μm range at 300 K, 50 fps and f/1) and a flat spectral response from 3 to 14 μm. The footprint of the hyperspectral imager is 7 cm x 8 cm x 10 cm excluding the source. The spectral resolution varies from 55 to 220 nm depending on the type of FPI used. The Noise Equivalent Spectral Radiance (NESR) is 430 mW/(m2 .sr.μm) at 9 μm. Using this hyperspectral imager, spectra of various substances including polymers were recorded in the transmission, reflection and transflectance configurations. A good agreement was found with spectra obtained by applying the FPI transfer function to spectra recorded with a commercial FTIR spectrometer. The LWIR configuration of the imaging spectrometer will be described and test results 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".