Development of novel methods for the detection of chemical and microbiological contaminants in the agri-food chain
Bibliographic record
Abstract
The first aim of this research was to evaluate recently developed biosensors for their potential to detect chemical contaminants, chloramphenicol and/or nitrofuran metabolites and, overall their potential for the food industry in their present form, or with further development. The biosensors evaluated were the dotLab® system (Axela Inc., Toronto, Canada), SPRi-Lab+™ system (Horiba Scientific, Stanmore, UK) and Octet® RED96 system (ForteBio Inc., California, USA). All three were able to detect the chosen chemical contaminants in the form of either single- or multi-analyte detection, or both, with two systems, the SPRi-Lab+™ and Octet® RED96, achieving sensitivities equivalent to current minimum required performance limits. The ability to use crude matrices with the Octet® RED96 system, and the additional multiplexing features of both the SPRi-LabFM and Octet® RED96 systems, makes them prospective biosensors in their current form. Pending further development of the dotLab® system's multiplexing features this could be applicable to the dotLab® system also. The second aim of this research was to advance detection methods for two major foodborne pathogens, Salmonella spp. and Listeria monocytogenes, by screening for and employing highly bacteria-specific phage. A novel phage display-derived peptide binder, Peptide MSal020417 with sequence NRPDSAQFWLHH, was identified which results suggest is a genus-specific anti-Salmonella antibody mimicking peptide. A novel phage display-derived phage clone was identified which results suggest is highly specific for L. monocytogenes. These highly specific pathogen binders could be employed in •1 any detection method that traditionally employs an antibody, with the aim to advance the speed and specificity of detection of these foodborne pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".