Application of targeted1H NMR profiling to assess the seed vitality of soybean [Glycine max (L.) Merr.]

2017 ◽  
Vol 9 (11) ◽  
pp. 1792-1799 ◽  
Author(s):  
Wen-Ting Qin ◽  
Cai-qiong Yang ◽  
Nasir Iqbal ◽  
Jun-cai Deng ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Glycine Max ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Soybean Seed ◽  
Multivariate Statistical ◽  
H Nmr ◽  
Glycine Max L

A targeted1H NMR profiling method was developed for the simultaneous qualitation and quantitation of metabolites in soybean seed, and coupled PLS-DA multivariate statistical analysis helped us to assess seed vitality.

scholarly journals EMERGÊNCIA E SOBREVIVÊNCIA DE PLANTAS DE SOJA A CAMPO EM RELAÇÃO ÀS PROFUNDIDADES E ÉPOCAS DE SEMEADURA

2020 ◽  
Vol 35 (2) ◽  
pp. 150-157
Author(s):  
Patrícia Pereira Dias ◽  
Saulo Fernando Gomes de Sousa ◽  
Paulo Roberto Arbex Silva ◽  
Tiago Pereira da Silva Correia ◽  
Anderson Ravanny de Andrade Gomes
Keyword(s):  
Glycine Max ◽  
Statistical Analysis ◽  
Field Experiment ◽  
Analysis Of Variance ◽  
Early Stage ◽  
Soybean Seed ◽  
Sowing Depth ◽  
Tukey Test ◽  
Glycine Max L ◽  
Soil Deposition

A PROFUNDIDADE DE SEMEADURA DA SOJA NA PLANTABILIDADE   PATRÍCIA PEREIRA DIAS1*, SAULO FERNANDO GOMES DE SOUSA2, PAULO ROBERTO ARBEX SILVA3, TIAGO PEREIRA DA SILVA CORREIA4 e ANDERSON RAVANNY DE ANDRADE GOMES5   * Parte do texto foi extraído da tese da autora 1 Departamento de Produção Vegetal, Universidade Estadual Paulista ‘Júlio de Mesquita Filho’ - Av. Universitária, 3780 - Altos do Paraíso, 18610-034, Botucatu - SP, Brasil. [email protected] 2 Agroefetiva, Rua Lourival Ferreira, 11 - Distrito Industrial III, 18608-853, Botucatu - SP, Brasil.  [email protected] 3 Departamento de Engenharia Rural e Socioeconomia, Universidade Estadual Paulista ‘Júlio de Mesquita Filho’ - Av. Universitária, 3780 - Altos do Paraíso, 18610-034, Botucatu - SP, Brasil.  [email protected] 4 Faculdade de Agronomia e Medicina Veterinária, Universidade de Brasília, Caixa - Campos Universitário Darci Ribeiro ICC – Asa Norte, 04508, Brasília - DF, Brasil. [email protected] 5 Faculdade Regional da Bahia, Rodovia AL 220, 3630 - Senador Arnon De Melo - Planalto, Arapiraca – AL, Brasil. [email protected]   RESUMO: O objetivo deste trabalho foi avaliar a cultura da soja semeada a campo em diferentes profundidades das sementes e épocas de semeadura e, dessa maneira, saber o quanto esses fatores influenciam a emergência e sobrevivência das plantas. O experimento foi conduzido em dois anos agrícolas, 2015/16 e 2016/17, com sementes de soja cultivar 5D634, na Fazenda Lageado, da Faculdade de Ciências Agronômicas – UNESP, Botucatu (SP), Brasil. O delineamento foi de blocos ao acaso em esquema fatorial 6 × 2, com seis tratamentos referentes às profundidades das sementes na mesma linha de semeadura: 0,02, 0,05 e 0,08 m do nível do solo, e combinação e alternância entre eles: 0,02 e 0,05, 0,02 e 0,08, 0,05 e 0,08 m e duas épocas de semeadura: outubro e novembro, com quatro repetições, totalizando 48 parcelas. Para a análise estatística, os dados foram submetidos à análise de variância (ANOVA), utilizando o teste de Tukey a 5% de probabilidade. A perda foi acima de 10% no estande de plantas na semeadura mais profunda (0,08 m), aliada à menor precipitação (outubro/2016), que contribuiu significativamente para esse resultado. Desta forma, a plantabilidade é prejudicada com o erro da profundidade de semeadura, aqui representada pela emergência e sobrevivência de plantas.   Palavras-chave: Glycine max L., Plantabilidade, Estande de plantas.   THE SOYBEAN SOWING DEPTH IN PLANTABILITY   ABSTRACT: The aim of this work was evaluate the sowing of soybean seed with error in soil deposition and different month of sowing. The field experiment was carried out during two years (2015 and 2016) at Fazenda Lageado, Faculty of Agronomic Sciences - UNESP / Botucatu - SP, Brazil. The plots were randomized blocks in 6 × 2 factorial, six treatments referring to the depth of the seeds in soil: 0.02 (T1); 0.05 (T2 - control) and 0.08 (T3); 0.02 and 0.05 (T4); 0.02 and 0.08 (T5); 0.05 and 0.08 m (T6) and two sowing times: October and November. For statistical analysis the data were submitted to analysis of variance (ANOVA) using Tukey test at 5% probability. The number of plants in early stage decreased by over 10% in the deep seeding (0.08 m), added to the less rainfall (October / 2016) which contributed significantly to this result. Therefore, plantability is damaged by the error of sowing depth, represented here by the emergence and survival of plants.   Keywords: Glycine max L., Plantability, Early stage.

Metabolite profiling of isoflavones and anthocyanins in black soybean [Glycine max (L.) Merr.] seeds by HPLC-MS and geographical differentiation analysis in Southwest China

2017 ◽  
Vol 9 (5) ◽  
pp. 792-802 ◽  
Author(s):  
Hai-jun Wu ◽  
Jun-cai Deng ◽  
Cai-qiong Yang ◽  
Jing Zhang ◽  
Qing Zhang ◽  
...  
Keyword(s):  
Glycine Max ◽  
Statistical Analysis ◽  
Metabolite Profiling ◽  
Southwest China ◽  
Geographical Origin ◽  
Soybean Seeds ◽  
Geographical Differentiation ◽  
Multivariate Statistical ◽  
Black Soybean ◽  
Glycine Max L

Twelve isoflavones and eight anthocyanins were quantified in black soybean seeds by HPLC-MS analyses; the coupled OPLS-DA multivariate statistical analysis helped us to determine their geographical origin.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

2021 ◽  
Author(s):  
Maria‐Eleni Dimitrakopoulou ◽  
Konstantina Matzarapi ◽  
Styliani Chasapi ◽  
Apostolos Vantarakis ◽  
Georgios A. Spyroulias
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
H Nmr

Multivariate statistical analysis of elements content in snow cover in Blagoveshchensk

2018 ◽  
Vol 52 (2) ◽  
pp. 15
Author(s):  
V. I. Radomskaya ◽  
D. V. Yusupov ◽  
L. М. Pavlova ◽  
А. G. Sеrgееvа ◽  
N. А. Bоrоdinа ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Snow Cover ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical

Multivariate Statistical Analysis Regarding the Formulation of Oxicam-Based Pharmaceutical Hydrogels

2017 ◽  
Vol 68 (4) ◽  
pp. 726-731
Author(s):  
Lenuta Maria Suta ◽  
Anca Tudor ◽  
Colette Roxana Sandulovici ◽  
Lavinia Stelea ◽  
Daniel Hadaruga ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Pharmaceutical Formulations ◽  
Analysis Data ◽  
Multivariate Statistical ◽  
Predictive Variables ◽  
Rapid Release ◽  
Computational Data ◽  
Experimental Protocols ◽  
Formulation Factors

In this paper, it was analysed the influence of formulation factors over obtaining oxicam hydrogels, using the statistical analysis. Data analysis and predictive modeling by multivariate regression offers a large number of possible explanatory/predictive variables. Therefore, variable selection and dimension reduction is a major task for multivariate statistical analysis, especially for multivariate regressions. The statistical analysis and computational data processing of responses obtained from different pharmaceutical formulations, via different experimental protocols, lead to the optimization of the formulation process. It was found that the most suitable pharmaceutical formulations based on oxicams with the possibility of rapid release contained cyclodextrin, in particular 2-hydroxypropyl-b-cyclodextrin.

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