A spectroscopy-based proof-of-concept (POC) for developing loading of pathogen analyzer (LOPA) for dairy products
Bibliographic record
Abstract
Abstract Detection of bacterial contamination in dairy products of daily use is a challenge worldwide. We have utilized Methylene Blue Reduction Test (MBRT) for quantification of the microbial count in dairy products (milk) and developed a proof-of-concept (POC) based on this for in-filed applications. In this study, we have used pasteurized milk contaminated with model bacteria Escherichia coli, and Staphylococcus aureus for the calibration and validation of the developed POC. The conversion of MB to Leuco-MB i.e., the colorimetric change due to the reduction of MB to Leuco-MB in presence of microbes has been utilized as the tool to detect presence of microbes in milk. The absorbance peak for methylene blue (MB) at 664 nm decreases significantly in presence of microbes and the blue color becomes faded. In our study, we have employed methylene blue (MB) discolouration phenomenon to estimate the microbial count in milk samples using our developed spectroscopy based POC. The limit of detection (LOD) and the limit of quantitation (LOQ) of the POC were found to be 0.32 CFU/mL and 0.97 CFU/mL. The end users of the developed POC are primarily those involved in the production, processing, testing, regulation, and research of dairy products to ensure they meet safety standards and protect public health. These include retailers, dairy farmers, dairy processors, quality control laboratories, regulatory agencies and research institutions. In our experiment, we have observed a significant change in MB absorption in the milk contaminated with microbes. The indigenously developed sensor strips designed for the working of the POC turn to colorless Leuco-MB compared to milk without the microbes. The analysis of the strips has been measured in the developed device.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".