Application of chemometrics on Raman spectra from Mars: Recent advances and future perspectives
Bibliographic record
Abstract
Abstract The SuperCam and SHERLOC instruments onboard the NASA/Perseverance rover are returning the first Raman spectra to be ever collected from another planet. Similarly, the RLS instrument onboard the ESA/Rosalind Franklin rover will collect Raman spectra from powdered rocks sampled from the subsurface of Mars. To optimize the scientific exploitation of Raman spectra returned from planetary exploration missions, tailored chemometric tools are being developed that take into account the analytical capability of the mentioned Raman spectrometers. In this framework, the ERICA research group is using laboratory simulators of SuperCam and RLS to perform representative laboratory studies that will enhance the scientific outcome of both Mars2020 and ExoMars missions. On one hand, preliminary studies proved the chemometric analysis of RLS datasets could be used to obtain a reliable semi‐quantitative estimation of the main mineral phases composing Martian geological samples. On the other hand, it was proved the data fusion of Raman and LIBS spectra gathered by SuperCam could be used to enhance the discrimination of mineral phases from remote geological targets. Besides describing the models developed by the ERICA group, this work presents an overview of the complementary chemometric approaches so far tested in this field of study and propose further improvements to be addressed in the future.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".