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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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Biblioteca(s): |
Embrapa Florestas. |
Data corrente: |
03/01/2013 |
Data da última atualização: |
21/10/2013 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
ALFENAS, A. C.; GUIMARÃES, L. M. da S.; RESENDE, M. D. V. de. |
Afiliação: |
Acelino Couto Alfenas, UFV; Lúcio Mauro da Silva Guimarães, UFV; MARCOS DEON VILELA DE RESENDE, CNPF. |
Título: |
Genetic basis of resistance in Eucalyptus spp. pathosystems. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
In: INTERNATIONAL WORKSHOP ON GENETICS OF HOST-PARASITE INTERACTIONS IN FORESTRY, 4., 2011, Eugene. Proceedings... Albany: USDA - Forest Service, 2012. |
Idioma: |
Português |
Notas: |
Coordenadores técnicos: Richard A. Sniezko, Alvin D. Yanchuk, John T. Kliejunas, Katharine M. Palmieri, Janice M. Alexander, Susan J. Frankel. |
Palavras-Chave: |
Chrysoporthe cubensis. |
Thesagro: |
Ceratocystis Fimbriata; Eucalipto; Melhoramento genético vegetal; Puccinia Psidii. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/73013/1/2012-M.Deon-IWGHPIF-Genetics.pdf
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Marc: |
LEADER 00839nam a2200193 a 4500 001 1943853 005 2013-10-21 008 2012 bl uuuu u00u1 u #d 100 1 $aALFENAS, A. C. 245 $aGenetic basis of resistance in Eucalyptus spp. pathosystems.$h[electronic resource] 260 $aIn: INTERNATIONAL WORKSHOP ON GENETICS OF HOST-PARASITE INTERACTIONS IN FORESTRY, 4., 2011, Eugene. Proceedings... Albany: USDA - Forest Service$c2012 500 $aCoordenadores técnicos: Richard A. Sniezko, Alvin D. Yanchuk, John T. Kliejunas, Katharine M. Palmieri, Janice M. Alexander, Susan J. Frankel. 650 $aCeratocystis Fimbriata 650 $aEucalipto 650 $aMelhoramento genético vegetal 650 $aPuccinia Psidii 653 $aChrysoporthe cubensis 700 1 $aGUIMARÃES, L. M. da S. 700 1 $aRESENDE, M. D. V. de
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