Improved Discrimination of Mass Spectral Isomers Using the High‐Dimensional Consensus Mass Spectral Similarity Algorithm
Bibliographic record
Abstract
ABSTRACT This study employs a high‐dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real‐time mass spectrometry (DART‐MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called “high dimensional.” It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene‐related (C 10 H 16 and C 10 H 14 ) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography–electron ionization–mass spectrometry (GC‐EI‐MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI‐MS data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".