Nanosensor Based on Thermal Gradient and Machine Learning for the Detection of Methanol Adulteration in Alcoholic Beverages and Methanol Poisoning

Sensors (Basel). 2022 Jul 25;22(15):5554. doi: 10.3390/s22155554.

Abstract

Methanol, naturally present in small quantities in the distillation of alcoholic beverages, can lead to serious health problems. When it exceeds a certain concentration, it causes blindness, organ failure, and even death if not recognized in time. Analytical techniques such as chromatography are used to detect dangerous concentrations of methanol, which are very accurate but also expensive, cumbersome, and time-consuming. Therefore, a gas sensor that is inexpensive and portable and capable of distinguishing methanol from ethanol would be very useful. Here, we present a resistive gas sensor, based on tin oxide nanowires, that works in a thermal gradient. By combining responses at various temperatures and using machine learning algorithms (PCA, SVM, LDA), the device can distinguish methanol from ethanol in a wide range of concentrations (1-100 ppm) in both dry air and under different humidity conditions (25-75% RH). The proposed sensor, which is small and inexpensive, demonstrates the ability to distinguish methanol from ethanol at different concentrations and could be developed both to detect the adulteration of alcoholic beverages and to quickly recognize methanol poisoning.

Keywords: ethanol; gas sensor; metal oxide; methanol; nanowires; resistive sensor; tin oxide.

MeSH terms

  • Alcoholic Beverages / analysis
  • Ethanol / analysis
  • Machine Learning
  • Methanol* / chemistry
  • Nanowires*

Substances

  • Ethanol
  • Methanol

Grants and funding

M. Tonezzer acknowledges the University of Trento for the 35th cycle of the PhD Programme in “Agrifood and Environmental Sciences”. F. Gasperi and F. Biasioli acknowledge the support of the Autonomous Province of Trento (Program Agreement ADP 2020). No APC has been paid for this manuscript since it was invited.