Inheritance of kernel hardness in spring wheat as measured by near-infrared reflectance spectroscopy

Seka, D.; Richard, Frohberg C.

doi:10.1007/s10681-016-1658-6

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Tolerant Len/Butte*2/ND507/3/ND593 (Dagou and Richard 2016) High grinding requirements and cooking quality (Mergoum et al 2006), adult plant resistance to leaf rust disease based on Lr13 and Lr34 genes (Liu and Kolmer 1997) as well as used in multiple crosses (Mergoum et al 2005 According to Singh et al (2021), back-cross breeding can be effectively applied for the introgression of identified major quantitative trait loci (QTLs) for PHS tolerance. The importance of identifying QTLs with the potential to enhance PHS resistance in spring wheat has been highlighted by Liton et al (2021).…”

Section: Grandinmentioning

confidence: 99%

Pre-Harvest Sprouting Tolerance in 36 Bread Wheat Genotypes

2022

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Pre-harvest sprouting (PHS), promoted by rainfall during crop maturity, is a high problem in many wheat-producing regions of the world. Considering its importance in Brazil, 36 national and international varieties and advanced lines of wheat were evaluated for their tolerance to PHS. For this purpose, two experiments were conducted over three years. Seed pericarp rupture was used as an indicator of the beginning of germination. The data were analyzed using analysis of variance, the Scott-Knott test, and the Lin and Binns method. The wide range of germination percentage values allowed the genotypes to be classified as tolerant (in experiment 1 - ND 674 and Grandin*2/RL 4137 and experiment 2 - Frontana and Grandin) and moderately tolerant (Alsen, CD 114, and Milan/3/Attila//Fang 69/CIMMYT 3 in Experiment 1; Avante, BRS 177, IAC 5-Maringá, Onix, OR 1, RL 4137, and Rubi in Experiment 2). Because tolerance to PHS is under genetic control and can be improved through breeding programs, the challenge for wheat breeders is to combine increased PHS tolerance with other requirements to meet market demands.

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Section: Grandinmentioning

confidence: 99%

Pre-Harvest Sprouting Tolerance in 36 Bread Wheat Genotypes

2022

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“…The use of near-infrared reflectance technology can also study the heritability of grain hardness. Four separate populations were cultivated from three cross combinations, and the heritability estimate of wheat hardness was significant [ 38 ]. The prediction of wheat grain hardness is also an important aspect.…”

Section: Application Of Nondestructive Testing With Sensor Technolmentioning

confidence: 99%

Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data

Tian

Zhang²,

Tian

2020

Journal of Analytical Methods in Chemistry

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Nondestructive testing with sensor technology is one of the fastest growing and most promising wheat quality information analysis technologies. Nondestructive testing with sensor technology benefits from the latest achievement of many disciplines such as computer, optics, mathematics, chemistry, and chemometrics. It has the advantages of simplicity, speed, low cost, no pollution, and no contact. It is widely used in wheat quality analysis and testing research. This article summarizes nondestructive testing with sensor technology for wheat quality, including the mechanical model, hyperspectral technology, Raman spectroscopy, and near-infrared techniques for wheat mechanical properties, storage properties, and physical and chemical properties (such as moisture, ash, protein, and starch) in the past decade. Based on the current research progress, big data technology needs a lot of research in spectral data mining, modeling algorithm optimization, model robustness, etc. to provide more data support and method reference for the research and application of wheat quality.

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“…For there are relationship between chemical substance and the absorption, the NIRS technology could be used to quantitative analyze the chemical composition rich of C, H, O, and N (Niu, Huang, Liu, Chen, & Han, 2014; Yang et al, 2017). So far, it had been widely applied in many areas, such as agriculture (Dagou & Richard, 2016; Wang, Wang, & Guo, 2017), food industry (Cortés et al, 2016; Hu, Ma, Liu, Wu, & Ouyang, 2017; Zhou, Wu, Li, Wang, & Zhang, 2012), pharmacology (Chong, Zou, Yao, & Hu, 2017; Wang et al, 2017), life science (Jouquet, Capowiez, Bottinelli, & Traore, 2014), and the petrochemical industry (Li et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Rapid prediction of Apparent Amylose, total starch, and crude protein by near‐infrared reflectance spectroscopy for foxtail millet (Setaria italica)

Zhang

Wang

et al. 2020

Cereal Chem

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Background and objectives The aim of this study was to construct near‐infrared reflectance spectroscopy (NIRS) models to test the contents of apparent amylose, total starch, and crude protein for both foxtail millet grain and dehusked millet grain. The construction of models is based on chemical values and optimized spectral data of 111 core foxtail millet germplasm materials after analyzation of partial least square (PLS) method with cross‐validation. Findings Model calibration showed that there was better fitting degree of dehusked millet grain than that of foxtail millet. The coefficient determination for calibration (Rc2) of former is >0.843, while that of latter is >0.823. The coefficient determination of prediction (Rp2) of each component ranged from 0.794 to 0.888 for foxtail millet grain and 0.833 to 0.970 for dehusked millet grain. Conclusions The constructed NIRS models of both samples can be used to rapidly test foxtail millet quality, and the nondestructive nature of foxtail millet grain‐based test (which maintains seed vigor) better meets the requirements of the breeding, although the accuracy needs to be further improved. Significance and novelty This study developed a rapid method to detect the main nutritional quality for foxtail millet using two different samples, which can be used in germplasm resource innovation and contribute to the acceleration of cereal breeding.

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Inheritance of kernel hardness in spring wheat as measured by near-infrared reflectance spectroscopy

Cited by 3 publications

References 15 publications

Pre-Harvest Sprouting Tolerance in 36 Bread Wheat Genotypes

Pre-Harvest Sprouting Tolerance in 36 Bread Wheat Genotypes

Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data

Rapid prediction of Apparent Amylose, total starch, and crude protein by near‐infrared reflectance spectroscopy for foxtail millet (Setaria italica)

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