Integrating artificial intelligence with miniature mass spectrometry
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Green Analytical Chemistry |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772577425000771 |
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| author | Jiayi Wang Lingyan Liu Ting Jiang |
| author_facet | Jiayi Wang Lingyan Liu Ting Jiang |
| author_sort | Jiayi Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c075aeb15f0b40b0a24e47d08468cc28 |
| institution | Kabale University |
| issn | 2772-5774 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Analytical Chemistry |
| spelling | doaj-art-c075aeb15f0b40b0a24e47d08468cc282025-08-20T03:53:56ZengElsevierGreen Analytical Chemistry2772-57742025-06-011310028110.1016/j.greeac.2025.100281Integrating artificial intelligence with miniature mass spectrometryJiayi Wang0Lingyan Liu1Ting Jiang2School of Medical Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Pharmaceutical Sciences, Capital Medical University, Beijing 100069, China; Corresponding author at: School of Pharmaceutical Sciences, Capital Medical University, Haidian, Beijing 100069, China.School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; Corresponding author at: School of Medical Technology, Beijing Institute of Technology, Haidian, Beijing 100081, China.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.http://www.sciencedirect.com/science/article/pii/S2772577425000771Artificial intelligenceMiniature mass spectrometry |
| spellingShingle | Jiayi Wang Lingyan Liu Ting Jiang Integrating artificial intelligence with miniature mass spectrometry Green Analytical Chemistry Artificial intelligence Miniature mass spectrometry |
| title | Integrating artificial intelligence with miniature mass spectrometry |
| title_full | Integrating artificial intelligence with miniature mass spectrometry |
| title_fullStr | Integrating artificial intelligence with miniature mass spectrometry |
| title_full_unstemmed | Integrating artificial intelligence with miniature mass spectrometry |
| title_short | Integrating artificial intelligence with miniature mass spectrometry |
| title_sort | integrating artificial intelligence with miniature mass spectrometry |
| topic | Artificial intelligence Miniature mass spectrometry |
| url | http://www.sciencedirect.com/science/article/pii/S2772577425000771 |
| work_keys_str_mv | AT jiayiwang integratingartificialintelligencewithminiaturemassspectrometry AT lingyanliu integratingartificialintelligencewithminiaturemassspectrometry AT tingjiang integratingartificialintelligencewithminiaturemassspectrometry |