Multivariate image analysis strategies for ToF‐SIMS images with topography
Bibliographic record
Abstract
Abstract Despite the benefits of multivariate analysis methods, many challenges remain with their robust applications to real‐life samples relevant to industry. Here, we use hair fibres pre‐treated with a multi‐component formulation to investigate different multivariate analysis strategies for complex time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images obtained in practical analysis. This is challenging because of extreme topography, a large number of unknown chemical components and detector saturation. We compare results from principal component analysis (PCA) and multivariate curve resolution (MCR) with no scaling, Poisson scaling and binomial scaling. Because of severe topography, scaling methods are modified to operate in the spectral domain only. We propose the use of a maximum ion intensity spectrum to highlight localised chemical features and diagnose detector saturation. Dead time correction with suitable data scaling is demonstrated to be essential for the detection of small, localised chemical variations. While PCA results are difficult to interpret, MCR results resemble secondary ion mass spectrometry (SIMS) spectra and distributions directly. MCR is also superior to manual analysis for the detection of an important interaction between multiple ingredients. However, unlike PCA, the scores and loadings obtained on different MCR factors are correlated. The consequence of this for the optimal resolution of independent chemical features is discussed in detail. Binomial scaling is identified as the most appropriate data scaling method for this image due to detector saturation. This study provides a robust analysis strategy for complex ToF‐SIMS images, essential for increasingly complex multi‐organic surfaces and biomaterials. © Crown copyright 2009. Reproduced with the permission of HerMajesty's Stationery Office. Published by John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".