Pathogen identification with laser-induced breakdown spectroscopy: the effect of bacterial and biofluid specimen contamination
Bibliographic record
Abstract
In this paper, the potential use of laser-induced breakdown spectroscopy (LIBS) for the rapid discrimination and identification of bacterial pathogens in realistic clinical specimens is investigated. Specifically, the common problem of sample contamination was studied by creating mixed samples to investigate the effect that the presence of a second contaminant bacterium in the specimen had on the LIBS-based identification of the primary pathogen. Two closely related bacterial specimens, Escherichia coli strain ATCC 25922 and Enterobacter cloacae strain ATCC 13047, were mixed together in mixing fractions of 10:1, 100:1, and 1000:1. LIBS spectra from the three mixtures were reliably classified as the correct E. coli strain with 98.5% accuracy when all the mixtures were withheld from the training model and classified against spectra from pure specimens. To simulate a rapid test for the presence of urinary tract infection pathogens, LIBS spectra were obtained from specimens of Staphylococcus epidermidis obtained from distilled water and sterile urine. LIBS spectra from the urine-harvested bacteria were classified as S. epidermidis with 100% accuracy when classified using a model containing only spectra from other Staphylococci species and with 88.5% accuracy when a model containing five genera of bacteria was utilized. Bacterial specimens comprising five different genera and 13 classifiable taxonomic groups of species and strains were compiled in a library that was tested using external validation techniques. The importance of utilizing external validation techniques where the library is tested with data withheld from all previous testing and training of the model was revealed by comparing the results against "leave-one-out" cross-validation results. Last, the effect of using sequential models for the classification of a single unknown spectrum was investigated by comparing the misclassification of two closely related bacteria, E. coli and E. cloacae, when the classification was first performed using the five-genus bacterial library and then with a smaller model consisting only of E. coli and E. cloacae specimens. This result shows the utility of using successively more targeted analyses and models that use preliminary classifications from more general models as input.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".