3D reconstruction and morphological characterisation of single wheat grains by X-ray μCT

Abstract The intricate task of achieving three-dimensional (3D) visual reconstruction of wheat kernels represents a notable challenge within the domain of digital grain analysis, playing a pivotal role in the realms of grain storage, processing, and breeding. However, existing investigations focused on individual kernels predominantly encompass dimensions such as length, width, height, and epidermal texture features, with a tendency to be invasive to the internal organisational structure of the kernel. Non-local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X-ray micro-computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains. Furthermore, building upon this foundation, an exhaustive assessment of morphological and structural parameters pertaining to each facet of the internal organisation of the wheat seed grain is conducted. Notably, these calculated parameters align with data generated by prior researchers,with 80% of the volume of the endosperm, 12% of the pericarp, about 2% of the endosperm and scutellum, and 4% of the pores. The established parameters and resultant 3D visual models serve as foundational components for subsequent in-depth examinations into various physicochemical properties, including quality characteristics, heat, and mass transfer attributes, as well as variations in its morphological structure during breeding, which are pertinent to individual grains. This research contributes valuable insights and methodologies that can propel the advancement of studies in wheat kernel analysis.

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Publication Details

Journal
International Journal of Food Science & Technology
Published
2024-10-05
DOI
https://doi.org/10.1111/ijfs.17500
Citations
3
Primary Topic
Food composition and properties
Type
article
Field-Weighted Citation Impact
0.93
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article

3D reconstruction and morphological characterisation of single wheat grains by X-ray μCT

Luyao Bai, Yaping Shi, Yanbo Hui, Ju Gao et al.
3 citations
International Journal of Food Science & Technology
Food composition and properties
0.93
article

3D reconstruction and morphological characterisation of single wheat grains by X-ray μCT

Luyao Bai, Yaping Shi, Yanbo Hui, Ju Gao, Haiyang Ding, Ying Zhou, Xiaoliang Wang, Qiao Wang
article en
3 citations

Abstract

Abstract The intricate task of achieving three-dimensional (3D) visual reconstruction of wheat kernels represents a notable challenge within the domain of digital grain analysis, playing a pivotal role in the realms of grain storage, processing, and breeding. However, existing investigations focused on individual kernels predominantly encompass dimensions such as length, width, height, and epidermal texture features, with a tendency to be invasive to the internal organisational structure of the kernel. Non-local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X-ray micro-computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains. Furthermore, building upon this foundation, an exhaustive assessment of morphological and structural parameters pertaining to each facet of the internal organisation of the wheat seed grain is conducted. Notably, these calculated parameters align with data generated by prior researchers,with 80% of the volume of the endosperm, 12% of the pericarp, about 2% of the endosperm and scutellum, and 4% of the pores. The established parameters and resultant 3D visual models serve as foundational components for subsequent in-depth examinations into various physicochemical properties, including quality characteristics, heat, and mass transfer attributes, as well as variations in its morphological structure during breeding, which are pertinent to individual grains. This research contributes valuable insights and methodologies that can propel the advancement of studies in wheat kernel analysis.

International Journal of Food Science & TechnologyVol. 59(12)
Shenhua Group (China) (CN), Henan University of Technology (CN), First Affiliated Hospital of Xinxiang Medical University (CN), Huanghe Science and Technology College (CN)
Openalex Percentile: Top 27%
Food composition and properties
0.93
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