Detection and Classification of Bacterial Cells After Centrifugation and Filtration of Liquid Specimens Using Laser-Induced Breakdown Spectroscopy
Bibliographic record
Abstract
were deposited from suspensions of various titers onto disposable nitrocellulose filter media for analysis by laser-induced breakdown spectroscopy (LIBS). Bacteria were concentrated and isolated in the center of the filter media during centrifugation using a simple and convenient sample preparation step. Summing all the single-shot LIBS spectra acquired from a given bacterial deposition provided perfectly sensitive and specific discrimination from sterile water control specimens in a partial least squares discriminant analysis (PLS-DA). Use of the single-shot spectra provided only a 0.87 and 0.72 sensitivity and specificity, respectively. To increase the statistical validity of chemometric analyses, a library of pseudodata was created by adding Gaussian noise to the measured intensity of every emission line in an averaged spectrum of each bacterium. The normally distributed pseudodata, consisting of 4995 spectra, were used to compare the performance of the PLS-DA with a discriminant function analysis (DFA) and an artificial neural network (ANN). For the highly similar bacterial data, no algorithm showed significantly superior performance, although the PLS-DA performed least accurately with a classification error of 0.21 compared to 0.16 and 0.17 for ANN and DFA, respectively. Single-shot LIBS spectra from all of the bacterial species were classified in a DFA model tested with a tenfold cross-validation. Classification errors ranging from 20% to 31% were measured due to repeatability limitations in the single-shot 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".