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"
}