Biosensor for Bacterial Detection Through Color Change in Culture Medium
Bibliographic record
Abstract
Rapid and accurate bacterial detection is essential in medicine, the food industry, and environmental monitoring. This work presents the development of an optical sensor based on color changes in the culture medium that leverages the optical interaction of bacterial metabolic products. The proposed prototype operates on the principle of optical transmittance through mannitol salt agar (ASM), a selective medium for Staphylococcus aureus. As bacterial growth progresses, the medium undergoes changes in thickness and, primarily, color, which is optically measurable at specific wavelengths depending on the type of illumination provided by the simplified light-emitting diodes (LEDs). The sensor demonstrated the ability to detect bacterial growth in approximately 90–120 min, offering a significant reduction in detection time compared to traditional incubation methods. The system is characterized by its simplicity, sensitivity, low reagent consumption (up to 140 fewer reagents per test), and potential for real-time monitoring. These findings support the viability of the proposed sensor as an efficient alternative for early pathogen detection in both clinical and industrial applications. Finally, a proposal for simplifying the sensor in a system composed of a light-emitting diode and a light-dependent resistor is presented.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".