Literature DB >> 32854804

An automatic electronic instrument for accurate measurements of food volume and density.

Ding Yuan1, Xiaohui Hu1, Hong Zhang1, Wenyan Jia2, Zhi-Hong Mao2,3, Mingui Sun2,3,4.   

Abstract

OBJECTIVE: Accurate measurements of food volume and density are often required as 'gold standards' for calibration of image-based dietary assessment and food database development. Currently, there is no specialised laboratory instrument for these measurements. We present the design of a new volume of density (VD) meter to bridge this technological gap.
DESIGN: Our design consists of a turntable, a load sensor, a set of cameras and lights installed on an arc-shaped stationary support, and a microcomputer. It acquires an array of food images, reconstructs a 3D volumetric model, weighs the food and calculates both food volume and density, all in an automatic process controlled by the microcomputer. To adapt to the complex shapes of foods, a new food surface model, derived from the electric field of charged particles, is developed for 3D point cloud reconstruction of either convex or concave food surfaces.
RESULTS: We conducted two experiments to evaluate the VD meter. The first experiment utilised computer-synthesised 3D objects with prescribed convex and concave surfaces of known volumes to investigate different food surface types. The second experiment was based on actual foods with different shapes, colours and textures. Our results indicated that, for synthesised objects, the measurement error of the electric field-based method was <1 %, significantly lower compared with traditional methods. For real-world foods, the measurement error depended on the types of food volumes (detailed discussion included). The largest error was approximately 5 %.
CONCLUSION: The VD meter provides a new electronic instrument to support advanced research in nutrition science.

Entities:  

Keywords:  3D image reconstruction; Dietary assessment; Food; Image; Volume

Year:  2020        PMID: 32854804      PMCID: PMC7914281          DOI: 10.1017/S136898002000275X

Source DB:  PubMed          Journal:  Public Health Nutr        ISSN: 1368-9800            Impact factor:   4.022


  12 in total

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3.  A Method to Determine the Density of Foods using X-ray Imaging.

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7.  Under-reporting remains a key limitation of self-reported dietary intake: an analysis of the 2008/09 New Zealand Adult Nutrition Survey.

Authors:  L Gemming; Y Jiang; B Swinburn; J Utter; C Ni Mhurchu
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Review 9.  The psychosocial and behavioral characteristics related to energy misreporting.

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10.  Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map.

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Journal:  Nutrients       Date:  2018-12-18       Impact factor: 5.717

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Review 2.  A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment.

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