Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program-Reference-Cited by-同舟云学术

Non-destructive genotypes classification and oil content prediction using near-infrared spectroscopy and chemometric tools in soybean breeding program

Published:2020-08 Issue: Volume:91 Page:103536
ISSN:0889-1575
Container-title:Journal of Food Composition and Analysis
language:en
Short-container-title:Journal of Food Composition and Analysis

Author:

Leite Daniel Carvalho,Corrêa Aretha Arcenio Pimentel,Cunha Júnior Luis Carlos,Lima Kássio Michell Gomes de,Morais Camilo de Lelis Medeiros de,Vianna Viviane Formice,Teixeira Gustavo Henrique de Almeida,Di Mauro Antonio Orlando,Unêda-Trevisoli Sandra Helena

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

Elsevier BV

Subject

Food Science

Reference26 articles.

1. Official Methods of Analysis of the Association of Official Analytical Chemists;AOAC,1997

2. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra;Barnes;Appl. Spectrosc.,1989

3. Agronomical aspects of the development of cultivars;Bezerra,2017

4. Multiblock PLS as an approach to compare and combine NIR and MIR spectra in calibrations of soybean flour;Brás;Chemometr. Intell. Lab. Syst.,2005

5. Ministério da Agricultura, Pecuária e Abastecimento. Regras para Análises de Sementes / Ministério da Agricultura Pecuária e Abastecimento. Secretaria de Defesa Agropecuária;Brasil,2009

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