Aptamer-based sensing array coupled with multiple pattern recognition algorithms for accurate β-lactoglobulin...

Area of Science:

• Analytical Chemistry
• Biosensing
• Food Science

Background:

• Beta-Lactoglobulin (β-LG) is a major milk allergen causing health concerns, particularly in infants.
• Existing detection methods struggle with complex food matrices, necessitating improved strategies.
• Effective detection of β-LG is crucial for food safety and allergen management.

Purpose of the Study:

• To develop a novel aptamer-based sensing array for accurate β-LG detection.
• To overcome the challenge of matrix effects in food samples.
• To establish a sensitive and robust method for allergen identification in diverse food matrices.

Main Methods:

• Utilized four aptamers as recognition probes in an aptamer-based sensing array.
• Employed gold nanoparticles (AuNPs) for colorimetric detection, observing salt-induced aggregation.
• Applied multiple pattern recognition algorithms to analyze colorimetric responses and identify β-LG.
• Developed a unique fingerprint pattern based on color changes for β-LG detection.

Main Results:

• The aptamer-based array coupled with pattern recognition achieved over 95% classification accuracy.
• A minimum discriminable concentration of 20 nM was established for reliable β-LG differentiation.
• Linear Discriminant Analysis demonstrated 95.3% accuracy in blind testing of 150 samples.
• The strategy showed robust anti-interference performance in complex food matrices.

Conclusions:

• The proposed aptamer-based sensing array provides a sensitive and reliable method for β-LG detection.
• This approach effectively circumvents matrix effects, enabling accurate allergen identification in complex foods.
• The strategy offers a promising platform for robust allergen detection, addressing limitations of conventional aptasensors.