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Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening

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posted on 2024-07-25, 12:36 authored by Peihua Ma, Yixin WuYixin Wu, Ning Yu, Xiaoxue Jia, Yiyang He, Yang ZhangYang Zhang, Michael BackesMichael Backes, Qin Wang, Cheng‐I Wei
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.

History

Primary Research Area

  • Trustworthy Information Processing

Journal

Advanced Science

Page Range

e2403578-e2403578

Publisher

Wiley

Open Access Type

  • Gold

Sub Type

  • Article

BibTeX

@article{Ma:Wu:Yu:Jia:He:Zhang:Backes:Wang:Wei:2024, title = "Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening", author = "Ma, Peihua" AND "Wu, Yixin" AND "Yu, Ning" AND "Jia, Xiaoxue" AND "He, Yiyang" AND "Zhang, Yang" AND "Backes, Michael" AND "Wang, Qin" AND "Wei, Cheng‐I", year = 2024, month = 7, journal = "Advanced Science", pages = "e2403578--e2403578", publisher = "Wiley", issn = "2198-3844", doi = "10.1002/advs.202403578" }

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