Identification of earl millet cultivars using both microsatellites and enzymatic markers

Neto, Rodrigues; Pinho, E V R Von; Carvalho, Branco; Pereira, Gabriela Santos

doi:10.4238/2013.january.7.1

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Therefore, the identification of seed varieties is an urgent problem to be solved in agricultural production, seed sales, and food processing. The common methods of seed identification in China and abroad include morphological methods, the gel electrophoresis of soluble seed proteins [4,5], direct analysis with real-time mass spectrometry [6], isoenzyme electrophoresis [7], liquid chromatography [8], and simple sequence repeat (SSR) analysis [9]. Morphological methods are highly demanding of experience for appraisers and the identification accuracy is easy subject to subjective factors.…”

Section: Introductionmentioning

confidence: 99%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.

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Section: Introductionmentioning

confidence: 99%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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“…For the enzyme alcohol dehydrogenase, polymorphism was verified between millet cultivars by Mendonça Neto et al (2013) when using this enzyme as a marker, although the same fact was observed by Vieira et al (2009), working with soybean and Menezes et al (2008) working with breeding lines and hybrids of maize. Using esterases as markers, by Vieira et al (2009), polymorphism was observed for soybean cultivar separation, as well as by Ferreira et al (2009) in Gladiolus and Mendonça Neto et al (2013) in millet crop. The same fact is reported by Vieira et al (2009) using the enzymes superoxide dismutase and peroxidase in studies with soybean cultivar polymorphisms observed for cultivar separation.…”

Section: Resultsmentioning

confidence: 61%

Genetic diversity in soybean genotypes using phenotypic characters and enzymatic markers

Zambiazzi

Bruzi

Sales

et al. 2017

gmr

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The objective of this study was to evaluate the genetic diversity of soybean cultivars by adopting phenotypic traits and enzymatic markers, the relative contribution of agronomic traits to diversity, as well as diversity between the level of technology used in soybean cultivars and genetic breeding programs in which cultivars were inserted. The experiments were conducted on the field at the Center for Scientific and Technological Development in crop-livestock production and the Electrophoresis Laboratory of Lavras Federal University. The agronomic traits adopted were grain yield, plant height, first legume insertion, plant lodging, the mass of one thousand seeds, and days for complete maturation, in which the Euclidean distance, grouped by Tocher and UPGMA criteria, was obtained. After electrophorese gels for enzymatic systems, dehydrogenase alcohol, esterase, superoxide dismutase, and peroxidase were performed. The genetic similarity estimative was also obtained between genotypes by the Jaccard coefficient with subsequent grouping by the UPGMA method. The formation of two groups was shown using phenotypic characters in the genetic diversity study and individually discriminating the cultivar 97R73 RR. The character with the greatest contribution to the genetic divergence was grain yield with contribution higher than 90.0%. To obtain six different groups, individually discriminating the cultivars CG 8166 RR, FPS Jupiter RR, and BRS MG 780 RR, enzymatic markers were used. Cultivars carrying the RR technology presented more divergence than conventional cultivars and IPRO cultivars.

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“…A total of 25 SSR primers described by Neto et al [ 9 ] shown in Table 1 were obtained from Inqaba Biotechnical Industries and were screened for polymorphism. This was done by randomly selecting 2 DNA samples from each of the regions (Zambezi, Karas, Kunene, Ohangwena, Okavango [East and West], Omusati, Oshana, Oshikoto, and Otjozondjupa) involved in the origin of all the accessions.…”

Section: Methodsmentioning

confidence: 99%

Genetic Diversity of NamibianPennisetum glaucum(L.) R. BR. (Pearl Millet) Landraces Analyzed by SSR and Morphological Markers

McBenedict

Chimwamurombe

Kwembeya

et al. 2016

The Scientific World Journal

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Current Pennisetum glaucum (L.) R. BR. cultivars in Namibia have overall poor performance posing a threat to the nation's food security because this crop is staple for over 70% of the Namibian population. The crop suffers from undesirable production traits such as susceptibility to diseases, low yield, and prolonged reproductive cycle. This study aimed to understand the genetic diversity of the crop in Namibia by simple sequence repeats (SSRs) and morphology analysis. A total of 1441 genotypes were collected from the National Gene Bank representing all the Namibian landraces. A sample of 96 genotypes was further analyzed by SSR using Shannon-Wiener diversity index and revealed a value of 0.45 indicating low genetic diversity. Ordination using Principal Coordinate Analysis (PCoA) on SSR data confirmed clusters generated by UPGMA for the 96 P. glaucum accessions. UPGMA phenograms of 29 morphological characterized genotypes were generated for SSR and morphology data and the two trees revealed 78% resemblance. Lodging susceptibility, tillering attitude, spike density, fodder yield potential, early vigour, and spike shape were the phenotypic characters upon which some clusters were based in both datasets. It is recommended that efforts should be made to widen the current gene pool in Namibia.

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Identification of earl millet cultivars using both microsatellites and enzymatic markers

Cited by 4 publications

References 15 publications

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Genetic diversity in soybean genotypes using phenotypic characters and enzymatic markers

Genetic Diversity of NamibianPennisetum glaucum(L.) R. BR. (Pearl Millet) Landraces Analyzed by SSR and Morphological Markers

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