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Structure from motion-convolutional neural network model (SfM-CNN) achieved accurate portable Chinese dietary chemical composition estimation for dietary recall

文献类型： 外文期刊

作者： Ma, Peihua 1 ; Hong, Hsuan Chih 3 ; Jia, Xiaoxue 1 ; Chi, Cheng Jan 3 ; Xiao, Ning 4 ; Fan, Bei 1 ; Wang, Fengzhong 1 ; Wei, Cheng-, I 2 ;

作者机构： 1.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Inst Food Sci & Technol, Key Lab Agroprod Proc, Beijing 100193, Peoples R China

2.Univ Maryland, Coll Agr & Nat Resources, Dept Nutr & Food Sci, College Pk, MD 20742 USA

3.Porcupic Ltd, Aurora, ON L4G 7H7, Canada

4.Chinese Acad Med Sci & Peking Union Med Coll, Fuwai Hosp, Hlth Lifestyle Med Ctr, Natl Ctr Cardiovasc Dis,State Key Lab Cardiovasc D, Beijing 100037, Peoples R China

关键词： Structure from motion; Convolutional neural network; Chemical analysis; Dietary recall

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 489 卷

页码：

收录情况： SCI

摘要： Accurately estimating the chemical composition of dietary intake is essential for health and nutrition management, especially in regions with complex culinary diversity like China. This study introduces a novel AI-driven solution using a Structure from Motion-Convolutional Neural Network (SfM-CNN) model to automate chemical composition analysis of Chinese food. By integrating advanced 3D reconstruction techniques with deep learning, specifically the Scale-Invariant Feature Transform (SIFT) algorithm, we achieved superior feature extraction and food volume estimation with less than 4 % error. Our model, trained on the newly developed ChineseDish-100 dataset, demonstrated an R2 of 0.949 for carbohydrate content estimation using the SIFT-ResNet50 architecture. The model's interpretability was enhanced through visualizations, facilitating parameter optimization and reliable chemical composition estimation. These results underscore the potential of AI-powered models in providing efficient, accurate, and culturally relevant dietary analysis tools, marking a significant advancement for nutritional science, food chemistry, and public health initiatives in culturally diverse regions.

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