Gee-logit model corrected biplots with harvest effects on coffee beans grading
Biosci. j. (Online)
; 37: e37044, Jan.-Dec. 2021. ilus, graf, tab
Article
em En
| LILACS
| ID: biblio-1358930
Biblioteca responsável:
BR396.1
ABSTRACT
In a granulometric analysis of coffee beans with different categories of defects, the data can be organized in contingency tables, and when considering the discrimination by harvest, they may have a structure that suggest a more complex model, by means of the counting of defective coffee beans compared to different crops interacting with the classification of defects and percentages of sieve grains, which characterizes a block design with multivariate responses. However, due to the techniques based on the analysis of variance, considering the uniform correlation structure for all plots, it becomes feasible to propose a model that allows contemplating different structures between the plots, associating the effects of the crops to the defects in the granulometric procedure applied to the coffee beans. Thus, the hypothesis of incorporating the effects of crops associated with defects arises using the biplot multivariate technique. This work aims to propose the use of corrected biplots by predictions obtained trhough the fit to the Generalized Linear Model in the coffee grain size classification, broken down by components of the effect of the harvests. In conclusion, the use of GEE models with the corrected biplot technique by the predictions is feasible for application to be applied to the granulometric analysis of defective coffee beans, presenting discrimination regarding the effects of harvests.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
LILACS
Assunto principal:
Café
/
Produtos Agrícolas
Idioma:
En
Revista:
Biosci. j. (Online)
Assunto da revista:
Agricultura
/
Disciplinas das Cincias Biol¢gicas
/
Pesquisa Interdisciplinar
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Brasil