Food for thought: optical sensor arrays and machine learning for the food and beverage industry

Peveler, W. J. (2024) Food for thought: optical sensor arrays and machine learning for the food and beverage industry. ACS Sensors, (doi: 10.1021/acssensors.4c00252) (Early Online Publication)

Abstract

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

• Item Type: Articles
• Keywords: Sensing array, cross-reactive, electronic nose, machine learning, food, beverages, smell, taste.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Peveler, Dr William
• Authors: Peveler, W. J.
• College/School: College of Science and Engineering > School of Chemistry
• Journal Name: ACS Sensors
• Publisher: American Chemical Society
• ISSN: 2379-3694
• ISSN (Online): 2379-3694
• Published Online: 10 April 2024
• Copyright Holders: Copyright © 2024 The Author
• First Published: First published in ACS Sensors 2024
• Publisher Policy: Reproduced under a Creative Commons license