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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo |
Biblioteca(s): |
Embrapa Arroz e Feijão. |
Data corrente: |
23/01/2018 |
Data da última atualização: |
02/05/2018 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
VON BORRIES, G.; BASSINELLO, P. Z.; RIOS, E. S.; KOAKUZU, S. N.; CARVALHO, R. N. |
Afiliação: |
GEORGE VON BORRIES, UNB; PRISCILA ZACZUK BASSINELLO, CNPAF; ERICA S. RIOS, pós-graduação UFV; SELMA NAKAMOTO KOAKUZU, CNPAF; ROSANGELA NUNES CARVALHO, CNPAF. |
Título: |
Prediction models of rice cooking quality. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Cereal Chemistry, v. 95, n. 1, p. 158-166, Jan./Feb. 2018. |
DOI: |
10.1002/cche.10017 |
Idioma: |
Inglês |
Conteúdo: |
Background and objectives: Rice quality can be primarily assessed by evaluating its texture after cooking. The classical sensory evaluation is an expensive and time-consuming method as it requires training, capability, and availability of people. Therefore, this study investigated the possibility of replacing sensory evaluation by analyzing the relationship between sensory and instrumental texture and viscosity measurements. Findings: Models predicting the sensory evaluation were developed by applying statistical methods such as principal component analysis and polytomous logistic regression. The level of prediction efficiency of these models was obtained by estimating the apparent misclassification error rate and also using the ROC curve graph. The results indicated that the instrumental texture measurements were consistently related to sensory analysis. Similarly, viscosity measurements enabled the prediction of results obtained by sensory texture evaluation. Conclusions: Principal component analysis together with polytomous logistic regression is an efficient method to predict sensorial stickiness of rice using viscosity measures of texture as predictors. Significance and novelty: The current study was able to correctly predict sensory stickiness in about 86% of cases using just one principal component formed by a combination of measures of apparent amylose content, gelatinization temperature, and RVA parameters. |
Palavras-Chave: |
Polytomous logistic regression; Principal components; Texture analyzer. |
Thesagro: |
Análise organoleptica; Arroz; Oryza sativa. |
Thesaurus Nal: |
Cooking quality; Rice; Sensory evaluation. |
Categoria do assunto: |
Q Alimentos e Nutrição Humana |
Marc: |
LEADER 02248naa a2200289 a 4500 001 2086072 005 2018-05-02 008 2018 bl uuuu u00u1 u #d 024 7 $a10.1002/cche.10017$2DOI 100 1 $aVON BORRIES, G. 245 $aPrediction models of rice cooking quality.$h[electronic resource] 260 $c2018 520 $aBackground and objectives: Rice quality can be primarily assessed by evaluating its texture after cooking. The classical sensory evaluation is an expensive and time-consuming method as it requires training, capability, and availability of people. Therefore, this study investigated the possibility of replacing sensory evaluation by analyzing the relationship between sensory and instrumental texture and viscosity measurements. Findings: Models predicting the sensory evaluation were developed by applying statistical methods such as principal component analysis and polytomous logistic regression. The level of prediction efficiency of these models was obtained by estimating the apparent misclassification error rate and also using the ROC curve graph. The results indicated that the instrumental texture measurements were consistently related to sensory analysis. Similarly, viscosity measurements enabled the prediction of results obtained by sensory texture evaluation. Conclusions: Principal component analysis together with polytomous logistic regression is an efficient method to predict sensorial stickiness of rice using viscosity measures of texture as predictors. Significance and novelty: The current study was able to correctly predict sensory stickiness in about 86% of cases using just one principal component formed by a combination of measures of apparent amylose content, gelatinization temperature, and RVA parameters. 650 $aCooking quality 650 $aRice 650 $aSensory evaluation 650 $aAnálise organoleptica 650 $aArroz 650 $aOryza sativa 653 $aPolytomous logistic regression 653 $aPrincipal components 653 $aTexture analyzer 700 1 $aBASSINELLO, P. Z. 700 1 $aRIOS, E. S. 700 1 $aKOAKUZU, S. N. 700 1 $aCARVALHO, R. N. 773 $tCereal Chemistry$gv. 95, n. 1, p. 158-166, Jan./Feb. 2018.
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Embrapa Arroz e Feijão (CNPAF) |
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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Clima Temperado. Para informações adicionais entre em contato com cpact.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Clima Temperado. |
Data corrente: |
25/09/2001 |
Data da última atualização: |
26/12/2018 |
Autoria: |
VERNETTI, F. de J.; FAGUNDES, P. R. R.; GASTAL, M. F da C.; BRANCAO, N. |
Título: |
Soja: linhagem Pel8710. |
Ano de publicação: |
1996 |
Fonte/Imprenta: |
Pelotas: EMBRAPA-CPACT, 1996. |
Páginas: |
16p. |
Série: |
EMBRAPA-CPACT. Documentos, 25). |
ISSN: |
0104-3323 |
Idioma: |
Português |
Palavras-Chave: |
Cultivar; Melhoramento genetico. |
Thesagro: |
Soja. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00519nam a2200205 a 4500 001 1743533 005 2018-12-26 008 1996 bl uuuu u0uu1 u #d 022 $a0104-3323 100 1 $aVERNETTI, F. de J. 245 $aSoja$blinhagem Pel8710. 260 $aPelotas: EMBRAPA-CPACT$c1996 300 $a16p. 490 $aEMBRAPA-CPACT. Documentos, 25). 650 $aSoja 653 $aCultivar 653 $aMelhoramento genetico 700 1 $aFAGUNDES, P. R. R. 700 1 $aGASTAL, M. F da C. 700 1 $aBRANCAO, N.
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