Multidimensional calibration spaces in Staphylococcus Aureus detection using chitosan-based genosensors and...

Area of Science:

• Biomedical Engineering
• Biosensing Technology
• Microfluidics

Background:

• Mastitis diagnosis requires sensitive and selective detection of Staphylococcus aureus (S. aureus).
• Current diagnostic methods may lack the necessary sensitivity or selectivity for early detection.
• Novel biosensing approaches are needed to improve mastitis diagnostics.

Purpose of the Study:

• To develop and evaluate microfluidic genosensors and electronic tongues for S. aureus DNA detection.
• To assess the performance of impedance spectroscopy combined with visual analytics and machine learning for mastitis diagnosis.
• To compare the sensitivity and selectivity of genosensors versus electronic tongues for S. aureus detection.

Main Methods:

• Fabrication of microfluidic genosensors using layer-by-layer films functionalized with S. aureus DNA.
• Construction of an electronic tongue with multiple sensing units based on chitosan-based films.
• Impedance spectroscopy measurements analyzed using visual analytics and machine learning algorithms.
• Evaluation of detection limits, sensitivity, selectivity, and accuracy for S. aureus DNA.

Main Results:

• Genosensors achieved a low limit of detection of 5.90 × 10^-19 mol/L for S. aureus DNA.
• Electronic tongue distinguished various concentrations of S. aureus DNA using dimensionality reduction techniques.
• Machine learning-based analysis confirmed S. aureus DNA selectivity with up to 89% accuracy for genosensors and 66% for the electronic tongue.

Conclusions:

• Microfluidic genosensors and electronic tongues, coupled with advanced data analysis, show promise for S. aureus detection in mastitis diagnosis.
• The choice between genosensors and electronic tongues depends on the required sensitivity and cost-effectiveness for specific diagnostic applications.
• These computational methods offer flexible solutions for improving mastitis diagnostic capabilities.