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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

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An Inexpensive, Scalable Behavioral Assay for Measuring Ethanol Sedation Sensitivity and Rapid Tolerance in Drosophila
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In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving

Jose M Celaya-Padilla1,2, Jonathan S Romero-González1, Carlos E Galvan-Tejada1

  • 1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro, Zacatecas 98000, Mexico.

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Summary

This study presents a novel, non-invasive method using MQ3 sensors to detect alcohol inside vehicles, aiming to prevent alcohol-related accidents. The system achieved high accuracy, suggesting its potential for real-world application in road safety.

Keywords:
alcohol detectiondrinking and drivinggenetic algorithmsmart infotainmentsmart vehicle

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Area of Science:

  • Engineering
  • Computer Science
  • Public Health

Background:

  • Motor vehicle accidents are a leading cause of death globally.
  • Alcohol-impaired driving significantly contributes to these fatalities, especially among children.

Purpose of the Study:

  • To develop a novel, non-invasive method for detecting alcohol presence within motor vehicles.
  • To aid in the prevention of alcohol-related traffic accidents.

Main Methods:

  • Utilized low-cost MQ3 alcohol sensors placed inside vehicles.
  • Processed sensor signals through standardization, time adjustment, and 5-second window sampling.
  • Employed genetic algorithms and stepwise selection for feature extraction, followed by Support Vector Machine (SVM) classification.

Main Results:

  • Achieved an Area Under the ROC Curve (AUC) of 0.98.
  • Demonstrated a sensitivity of 0.979 in detecting alcohol presence.
  • Trained the SVM model on 80% of 7200 experimental samples.

Conclusions:

  • The proposed methodology effectively detects alcohol in vehicles.
  • This non-invasive system shows promise for enforcing prevention strategies and enhancing road safety.