Optimizing non-dispersive infrared channels for derived cetane number prediction: Impact of spectral resolution and feature selection
Bibliographic record
Abstract
Jet fuels exhibit considerable variability in their chemical properties. This variability impacts properties like Derived Cetane Number (DCN), a measure of ignition quality that can vary significantly in the commercial fuel supply as it is not specified for jet fuels. Conventional methods for measuring DCN, such as Ignition Quality Testers, are accurate but rely on large equipment and long measurement times making them impractical for portable use. Vibrational spectroscopy offers a promising alternative, linking spectral information to fuel properties, yet high-resolution spectrometers remain bulky and challenging to miniaturize. Non-dispersive infrared (NDIR) sensors provide a compact solution, capable of accessing a few key spectral elements to predict fuel properties. Despite their lower number of elements and typically lower resolution, NDIR sensors are low-cost, power-efficient, and compact, making them ideal for onboard or handheld applications. This study extends analysis previously performed in the near-infrared region (4000-12000 cm -1 ) to the mid-infrared region (714-1428 cm -1 ), evaluating both commercial off-the-shelf (COTS) and custom narrow-bandwidth channels. Using a channel optimization process, custom channels with a 2 cm -1 bandwidth and 50% overlap achieved an R 2 of 0.92 in a linear model, closely matching the performance of high-resolution methods. Additionally, a nonlinear support vector regression (SVR) model further improved predictions, achieving an R 2 of 0.93 with just ten channels. These findings suggest that well-designed NDIR sensors can deliver accurate DCN predictions, offering a practical alternative to larger spectrometers. This approach holds promise for real-time fuel analysis in portable applications, bridging the gap between accuracy and miniaturization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".