Integrating artificial intelligence with miniature mass spectrometry

Abstract

Miniature mass spectrometers are increasingly being employed in various analytical fields due to their portability and low cost. Unlike lab-scale mass spectrometers, miniature mass spectrometers typically operate in environments that demand more automated analytical processes for on-site, real-time analysis. With the successful application of AI across different industries, researchers have started to integrate AI techniques into miniature mass spectrometry to enhance its capabilities. In this review, we provide an overview of the recent advancements in the intelligence of miniature mass spectrometers, focusing on intelligent sample identification and AI methods that enhance the instruments’ performance. These AI methods have not only improved the accuracy and efficiency of analysis but have also expanded the applications of miniature mass spectrometry to critical areas such as food safety, agricultural disease detection, and environmental monitoring. Moreover, we discuss the current challenges in advancing the intelligence of miniature mass spectrometers and explore the complexities involved in integrating AI with these devices. Finally, we offer our insights into future directions and potential solutions for overcoming these challenges.