Microfluidic Optical Aptasensor for Small Molecules Based on Analyte-Tuned Growth of Gold Nanoseeds and Machine Learning-Enhanced Spectrum Analysis: Rapid Detection of Mycotoxins

Abstract

Natural toxins, mainly small molecules, are a category of chemical hazards in agri-food systems that pose threats to both public health and food security. Current standard methods for monitoring these toxins, predominantly based on liquid chromatography–mass spectrometry, are costly, labor-intensive, and complex. This study presents the development of a novel microfluidic optical aptasensor for rapid detection of small molecules based on analyte-tuned growth of gold nanoseeds combined with machine learning-enhanced spectrum analysis. We discovered and optimized a previously unreported growth pattern of aptamer-coated nanoparticles in the presence of different concentrations of analyte, enabling the detection of a major mycotoxin in food. The entire analysis was miniaturized on a customized microfluidic platform, allowing for automated spectral acquisition with precise liquid manipulation. A machine learning model, based on random forest with feature engineering, was developed and evaluated for spectrum analysis, significantly enhancing the prediction of mycotoxin concentrations. This approach extended the detection limit determined by the conventional method (∼72 ppb with high variation) to a wider range of 10 ppb to 100 ppm with high accuracy (overall mean absolute percentage error of 5.7%). The developed analytical tool provides a promising solution for detecting small molecules and monitoring chemical hazards in agri-food systems and the environment.