Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening
Abstract: Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision‐language models (VLMs) with chemical analysis techniques. A cutting‐edge VLM is unveiled, utilizing the expansive UMDFood‐90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro‐AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high‐throughput solution for nutritional analysis.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening ; day:08 ; month:07 ; year:2024 ; extent:10
Advanced science ; (08.07.2024) (gesamt 10)
- Creator
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Ma, Peihua
Wu, Yixin
Yu, Ning
Jia, Xiaoxue
He, Yiyang
Zhang, Yang
Backes, Michael
Wang, Qin
Wei, Cheng‐I
- DOI
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10.1002/advs.202403578
- URN
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urn:nbn:de:101:1-2407091437312.350427082939
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 12:19 AM CEST
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Associated
- Ma, Peihua
- Wu, Yixin
- Yu, Ning
- Jia, Xiaoxue
- He, Yiyang
- Zhang, Yang
- Backes, Michael
- Wang, Qin
- Wei, Cheng‐I
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