Grading Factors of Wheat Kernels Based on Their Physical Properties

Ponce-García, Néstor; Ramírez‐Wong, Benjamín; Aburto, Anayansi Escalante; Torres-Chávez, Patricia Isabel; Serna‐Saldívar, Sergio O.

doi:10.5772/67246

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Such properties affect kernel selection by sieve separators and the estimated extraction rate during size reduction processes. The axial dimensions of kernels are essential for calculating the kernel volume for modeling drying, aeration, heating, and cooling [10]. The results of this study showed that the axial dimensions of the three cereal types corresponded to optimal values reported in other studies, which can be used to produce specific food products.…”

Section: Biophysical Propertiessupporting

confidence: 63%

“…Gómez et al [4] demonstrated that the starch structure, enzyme content (amylases), and other soluble components of the pericarp have important effects on the rheological properties of rye dough during the bread-making process. In previous studies, some mechanical and viscoelastic properties of kernels showed a significant correlation with the dough performance and quality of bakery products [10,37]. The bread-making process is very complex, and it involves mixing, sheeting, fermentation, and baking in the oven.…”

Section: Mechanical and Viscoelastic Propertiesmentioning

confidence: 99%

“…The shape and size of kernels are closely related to their mechanical and viscoelastic properties. The viscoelasticity of kernels can be used as a practical approach to recognize and predict the potential use and industrial performance of different cereal types [9,10]. However, the mechanical properties of conditioned wheat kernels have not been investigated sufficiently, let alone those of rye and triticale kernels, despite the critical technological implications.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Analysis on the Properties of Intact Cereal Kernels and Their Association with Viscoelasticity at Different Moisture Contents

Escalante‐Aburto

Figueroa‐Cárdenas

Domínguez-López

et al. 2023

Foods

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The viscoelastic properties of cereal kernels are strongly related to their quality, which can be applied to the development of a more selective and objective classification process. In this study, the association between the biophysical and viscoelastic properties of wheat, rye, and triticale kernels was investigated at different moisture contents (12% and 16%). A uniaxial compression test was performed under a small strain (5%), and the increase in viscoelasticity at 16% moisture content corresponded to proportional increases in biophysical properties such as the appearance and geometry. The biophysical and viscoelastic behaviors of triticale were between those of wheat and rye. A multivariate analysis showed that the appearance and geometric properties significantly influenced kernel features. The maximum force showed strong correlations with all viscoelastic properties, and it can be used to distinguish between cereal types and moisture contents. A principal component analysis was performed to discriminate the effect of the moisture content on different types of cereals and to evaluate the biophysical and viscoelastic properties. The uniaxial compression test under a small strain and the multivariate analysis can be considered a simple and non-destructive tool for assessing the quality of intact cereal kernels.

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Section: Biophysical Propertiessupporting

confidence: 63%

Section: Mechanical and Viscoelastic Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate Analysis on the Properties of Intact Cereal Kernels and Their Association with Viscoelasticity at Different Moisture Contents

Escalante‐Aburto

Figueroa‐Cárdenas

Domínguez-López

et al. 2023

Foods

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

Zhou,

Wang,

Shi

et al. 2024

Int J of Food Sci Tech

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SummaryThe 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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Development of a novel image-based grain counting setup for thousand-grain weight estimation in wheat

Krishnan,

Kumar,

Kaur

et al. 2023

IJGPB

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Thousand-grain weight (TGW) is one of the major yield-contributing traits routinely used as a selection criterion by plant breeders. Itis also an important grain quality trait that determines milling yield. Accurate phenotyping of TGW is imperative to dissect its geneticsfor yield improvement. The traditional approach to TGW estimation involves manual grain counting and weighing, which is laborious,tedious and less accurate for large sample sizes. As an alternative, we propose a customized grain counting setup for accurate estimationof TGW in wheat by assembling a photo lighting tent and a smartphone for image acquisition of grain samples. A popular open-sourcesoftware, ‘imageJ’ was used to process the images to estimate the grain count. The counted grain samples were weighed to calculatethe TGW. The TGW estimate derived from the proposed grain counting setup displayed a high degree of correlation with the manuallyestimated TGW data (r = 0.99, p <0.05). It took significantly less time to count the grain samples using the proposed setup comparedto manual counting with better accuracy and minimal labor. The error rate in grain counting using the imaging-based setup was verylow (<1%) and 30 to 40 grain samples can be imaged per hour. This setup can be extended to estimate the TGW of different crops,excluding those having spherical seeds.

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Grading Factors of Wheat Kernels Based on Their Physical Properties

Cited by 4 publications

References 33 publications

Multivariate Analysis on the Properties of Intact Cereal Kernels and Their Association with Viscoelasticity at Different Moisture Contents

Multivariate Analysis on the Properties of Intact Cereal Kernels and Their Association with Viscoelasticity at Different Moisture Contents

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

Development of a novel image-based grain counting setup for thousand-grain weight estimation in wheat

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