Machine Learning-Enhanced Chemiresistive Sensors for Ultra-Sensitive Detection of Methanol Adulteration in Alcoholic Beverages

Abstract

Methanol poisoning poses significant health risks, particularly in less developed countries, where adulterated alcoholic beverages often lead to severe morbidity and mortality. Current diagnostic methods, such as gas-liquid chromatography and blood gas analysis, are complex and prohibitively expensive, making them inaccessible in resource-constrained settings. To address this issue, we present a novel, simple, low-cost chemiresistive sensor for the rapid, selective, and ultrasensitive detection of methanol at ultralow concentrations in the presence of high ethanol concentrations. The sensor leverages an extrusion-printed hybrid composite of NU-1000 metal-organic frameworks (MOFs) and graphene, exploiting their unique structural and electronic synergies based on high porosity, functional metal sites of MOFs and graphene's excellent conductivity that enhance sensitivity and selectivity. To further overcome challenges in selectivity, we integrated machine learning algorithms and principal component analysis (PCA), significantly improving the sensor's ability to differentiate methanol from ethanol and other potential interferents. The extrusion printing technique ensures the fabrication of uniform, stable, and durable sensor layers on ceramic substrates, maintaining reproducible performance and stability. Our results demonstrate the sensor's capability to detect methanol vapors at parts-per-billion (ppb) levels in the presence of higher concentration of ethanol (ppm), making it an effective tool for monitoring methanol intoxication through breath analysis. This innovative approach represents a notable advancement in gas sensing technologies, offering a scalable, cost-effective solution for applications in medical diagnostics, industrial monitoring, and consumer safety. This research highlights the potential of extrusion-printed hybrid materials in advancing gas sensing technologies to enhance public health and safety.

Keywords: NU-1000 MOFs; chemiresistive sensor; ethanol adulteration; graphene hybrid materials; methanol detection.