TEXTURAL CHARACTERISTICS OF FIVE MICROORGANISMS FOR RAPID DETECTION USING IMAGE PROCESSING
Bibliographic record
Abstract
ABSTRACT A rapid and cost‐effective technique for identification and classification of microorganisms was explored using fluorescence microscopy and image analysis. After staining the microorganisms with fluorescent dyes (diamidino‐2‐phenyl‐indole [DAPI] and acridine orange [AO], images of the microorganisms were captured using a charge‐coupled device camera attached to a light microscope. Textural features were extracted from the images. Fluorescence emission from Bacillus thuringiensis is the highest compared with other microbes, and the emission from Lactobacillus brevis is the lowest. Various microorganisms can be differentiated using various textural features from images using AO or DAPI dye. Many textural features of the images obtained from the two dyes were different. PRACTICAL APPLICATIONS Conventional microbial detection methods take considerable time and are laborious. Rapid methods are required so that pathogens and spoilage microorganisms in foods and water can be identified and counted in a much shorter time. This work investigates image processing techniques particularly based on textural properties of the images of microorganisms. Images of microorganisms in samples can be captured using light microscopes after concentrating using centrifuge or membrane separation devices. This work will assist in developing a commercial method for rapid detection of microbes in food samples.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".