数字农科院2.0

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： Peihua Ma;Hsuan Chih Hong;Xiaoxue Jia;Cheng Jan Chi;Ning Xiao;Bei Fan;Fengzhong Wang;Cheng I. Wei

作者机构：

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

期刊名称： Food Chemistry

ISSN： 0308-8146

年卷期： 2025 年 489 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

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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