Tailoring Nanoparticle Surfaces Empowered by Multivariate Techniques for Detection of Multiple Antibiotic...

Area of Science:

• Analytical Chemistry
• Nanotechnology
• Environmental Science

Background:

• Antibiotics pose significant environmental and human health risks due to their complex toxicological profiles.
• There is a critical need for sensitive and efficient sensors for antibiotic detection.

Purpose of the Study:

• To develop a straightforward and efficient colorimetric sensor array for identifying and discriminating various antibiotics.
• To assess the sensor array's performance in detecting individual antibiotics and their mixtures.

Main Methods:

• Surface-tailored nanoparticles were synthesized and characterized using DLS, zeta potential, FTIR, XPS, AFM, FESEM, and HR-TEM.
• Antibiotic identification was achieved by analyzing unique colorimetric and UV-visible absorption patterns using machine learning algorithms (LDA, DT, RF, SVM, HCA).
• Prototypes for colorimetric analysis and modified 96-well plate readers were developed for real-time applications.

Main Results:

• The sensor array achieved 100% accuracy in discriminating eight different antibiotics, with detection limits between 1.7 and 8.3 μM.
• Successful discrimination of binary and ternary antibiotic mixtures was demonstrated.
• Developed prototypes enabled on-site sensing with quick response and stable signal output.

Conclusions:

• The developed colorimetric sensor array offers a highly sensitive, selective, and efficient method for antibiotic detection.
• The on-site sensing capabilities hold significant potential for practical applications in food forensics and environmental remediation.