Data/hora: 21/06/2024 13:24:58

Biblioteca(s): | Embrapa Arroz e Feijão.
Data corrente: | 29/06/2017
Data da última atualização: | 29/06/2017
Autoria: | OLIVEIRA, G.; VON BORRIES, G.; BASSINELLO, P. Z.
Afiliação: | GEIZIANE OLIVEIRA, SAS Institute, Brasil; GEORGE VON BORRIES, UNIVERSIDADE DE BRASÍLIA; PRISCILA ZACZUK BASSINELLO, CNPAF.
Título: | Comparing priors in bayesian logistic regression for sensorial classification of rice.
Ano de publicação: | 2017
Fonte/Imprenta: | In: SAS GLOBAL FORUM 2017, Orlando, FL, 2017.
Idioma: | Inglês
Conteúdo: | The present study use Proc MCMC to estimate rice stickiness (sticky or loose) in binomial logistic regression. Rice quality can be primarily assessed by evaluating its texture after cooking. The classical sensory evaluation is expensive and a time-consuming method since it requires training, capability and availability of people. Therefore, the present study investigated Bayesian binomial logistic models to replace sensory evaluation of stickiness by analyzing the relationship between sensory and principal components of viscosity measurements of rice. Proc MCMC was used to produce models based on different priors, as (1) noninformative prior; (2) default prior (Gelman et al., 2008); (3) prior based on odds ratio (Sullivan and Greenland, 2012) and (4) power priors (Ibrahim and Chen, 2000). SAS MCMC showed to be easy to implement and to compare results.
Palavras-Chave: | Classificação.
Thesagro: | Análise organoleptica; Arroz.
Thesaurus Nal: | Bayesian theory; Glutinous rice; Rice; Sensory evaluation; Viscosity.
Categoria do assunto: | Q Alimentos e Nutrição Humana
Registro original: | Embrapa Arroz e Feijão (CNPAF)
Exemplares: | CNPAF - (34794-1) - UPC - AA - DD - 2017 -  - 2017 - DIS