A PORTABLE RAMAN SYSTEM FOR THE IDENTIFICATION OF FOODBORNE PATHOGENIC BACTERIA
Bibliographic record
Abstract
ABSTRACT Raman spectroscopy is emerging as an important nondestructive, noninvasive, analytical tool for the analysis of biologic materials. This study presents a procedure to make use of commercial off‐the‐shelf components to construct a portable dispersive Raman system and evaluates it for discrimination of bacteria by surface‐enhanced Raman scattering (SERS). The system consists of a semiconductor laser (784.8 nm), a fiber optic probe (∼135 µm focal spot), a mini spectrometer and a computer. UV‐visible spectroscopy and transmission electron microscopy analysis of four silver colloid preparations produced in this study, together with the SERS spectra of Listeria innocua adsorbed on colloidal particles, indicated that silver colloids with the extinction maximum at >415 nm (particle size >75 nm) and a larger long wavelength tail are capable of promoting SERS of bacteria. The SERS spectra of Listeria monocytogenes, Escherichia coli O157:H7 and Salmonella enterica were acquired with the system, leading to an unambiguous identification of these bacterial foodborne pathogens on the basis of their unique spectral bands. This study demonstrated the feasibility of constructing a low‐cost compact Raman system using commercially available components to perform the SERS analysis of bacteria. PRACTICAL APPLICATIONS Commercial off‐the‐shelf components such as a semiconductor laser, a fiber optic probe and a mini spectrometer can be used to construct a portable, low‐cost dispersive Raman system. Such system allows for the acquisition of SERS spectra of bacteria adsorbed on silver colloidal nanoparticles, as exemplified by three important bacterial foodborne pathogens L. monocytogenes (serotype 4b), E. coli O157:H7 and S. enterica (serotype Typhimurium DT 104). An unambiguous identification of these pathogens was achieved based on their unique spectral bands, indicating that an inexpensive dispersive Raman system such as the one described here may be built for the rapid characterization of bacteria isolates from food, clinical and environment samples using SERS spectral fingerprints.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".