Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models
Abstract Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically uns...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202407513 |
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| author | Gianmarco Gabrieli Matteo Manica Joris Cadow‐Gossweiler Patrick W. Ruch |
| author_facet | Gianmarco Gabrieli Matteo Manica Joris Cadow‐Gossweiler Patrick W. Ruch |
| author_sort | Gianmarco Gabrieli |
| collection | DOAJ |
| description | Abstract Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero‐shot learning capabilities on various data structures and modalities, in particular for language and vision. Transfer learning from such models is explored by providing a framework to create effective data representations for chemical sensors and ultimately describe a novel, generalizable approach for AI‐assisted chemical sensing. The translation of signals produced by remarkably simple and portable multi‐sensor systems into visual fingerprints of liquid samples under test is demonstrated, and it is illustrated that how a pipeline incorporating pretrained vision models yields >95% average classification accuracy in four unrelated chemical sensing tasks with limited domain‐specific training measurements. This approach matches or outperforms expert‐curated sensor signal features, thereby providing a generalization of data processing for ultimate ease‐of‐use and broad applicability to enable interpretation of multi‐signal outputs for generic sensing applications. |
| format | Article |
| id | doaj-art-4b53d36babc04c8d8ea3f673d0a78006 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-4b53d36babc04c8d8ea3f673d0a780062025-08-20T02:27:57ZengWileyAdvanced Science2198-38442024-11-011144n/an/a10.1002/advs.202407513Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation ModelsGianmarco Gabrieli0Matteo Manica1Joris Cadow‐Gossweiler2Patrick W. Ruch3IBM Research Europe Säumerstrasse 4 Rüschlikon 8803 SwitzerlandIBM Research Europe Säumerstrasse 4 Rüschlikon 8803 SwitzerlandIBM Research Europe Säumerstrasse 4 Rüschlikon 8803 SwitzerlandIBM Research Europe Säumerstrasse 4 Rüschlikon 8803 SwitzerlandAbstract Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero‐shot learning capabilities on various data structures and modalities, in particular for language and vision. Transfer learning from such models is explored by providing a framework to create effective data representations for chemical sensors and ultimately describe a novel, generalizable approach for AI‐assisted chemical sensing. The translation of signals produced by remarkably simple and portable multi‐sensor systems into visual fingerprints of liquid samples under test is demonstrated, and it is illustrated that how a pipeline incorporating pretrained vision models yields >95% average classification accuracy in four unrelated chemical sensing tasks with limited domain‐specific training measurements. This approach matches or outperforms expert‐curated sensor signal features, thereby providing a generalization of data processing for ultimate ease‐of‐use and broad applicability to enable interpretation of multi‐signal outputs for generic sensing applications.https://doi.org/10.1002/advs.202407513chemical sensingmulti‐sensor systemsvision foundation models |
| spellingShingle | Gianmarco Gabrieli Matteo Manica Joris Cadow‐Gossweiler Patrick W. Ruch Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models Advanced Science chemical sensing multi‐sensor systems vision foundation models |
| title | Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models |
| title_full | Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models |
| title_fullStr | Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models |
| title_full_unstemmed | Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models |
| title_short | Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi‐Sensor System with Foundation Models |
| title_sort | digital fingerprinting of complex liquids using a reconfigurable multi sensor system with foundation models |
| topic | chemical sensing multi‐sensor systems vision foundation models |
| url | https://doi.org/10.1002/advs.202407513 |
| work_keys_str_mv | AT gianmarcogabrieli digitalfingerprintingofcomplexliquidsusingareconfigurablemultisensorsystemwithfoundationmodels AT matteomanica digitalfingerprintingofcomplexliquidsusingareconfigurablemultisensorsystemwithfoundationmodels AT joriscadowgossweiler digitalfingerprintingofcomplexliquidsusingareconfigurablemultisensorsystemwithfoundationmodels AT patrickwruch digitalfingerprintingofcomplexliquidsusingareconfigurablemultisensorsystemwithfoundationmodels |