OpenPCA and Raman mapping to decipher complex spectral datasets from multi-component samples: application to cannabis trichomes
Bibliographic record
Abstract
The development of analytical techniques that decode chemical information in complex biochemical samples to discriminate different structural components may open the way for several new findings. In this study, principal component analysis (PCA) is carried out using an ad hoc Matlab coding that provides a transparent access to multivariate analysis of Raman mapping datasets. Here, we illustrated the efficacy of this method to extract meaningful results from Raman images of Cannabis sativa trichomes. A large dataset of Cannabis trichome comprising of 441 Raman spectra was examined for the first time using our OpenPCA. By mapping the chemical distribution in the trichome, we could locate the secretary vesicles in the PC score maps generated from the mapped Raman spectra. Black-box PCA solutions available in commercial software can be limited by rigid input interfaces which may prevent obtaining information by tuning the PCA analysis on selected wavenumber ranges. Hence, the OpenPCA scripts facilitate the task of obtaining key information from widely distributed range of wavenumbers that are characteristic to a specific cannabinoid, namely Δ9-THC and CBD. Overall, the PCA-coding algorithm shows advantages in decoding Raman spectra that could be extended to handle all kinds of datasets with simultaneous spatial and chemical details.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".