Integrating artificial intelligence with miniature mass spectrometry
Year: 2025
Type: article
Abstract: • In recent years the process of realizing the implementation of intelligent miniature mass spectrometers by AI methods is reviewed. • Researches on miniature intelligent mass spectrometers are divided into two categories, one for intelligent sample identification and the other for enhancing instrument performance. • Recent researches on miniature intelligent mass spectrometers are presented, including the AI methods used and application scenarios. • The current challenges of intelligent miniature mass spectrometers are presented and future research directions are discussed. 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.
Source: Green Analytical Chemistry
Authors: Jiayi Wang, Lingyan Liu, Ting Jiang
Institutions: Beijing Institute of Technology, Capital Medical University
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,550
OpenAlex ID: W4410476902