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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
23/01/2018 |
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Data da última atualização: |
02/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
VON BORRIES, G.; BASSINELLO, P. Z.; RIOS, E. S.; KOAKUZU, S. N.; CARVALHO, R. N. |
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Afiliação: |
GEORGE VON BORRIES, UNB; PRISCILA ZACZUK BASSINELLO, CNPAF; ERICA S. RIOS, pós-graduação UFV; SELMA NAKAMOTO KOAKUZU, CNPAF; ROSANGELA NUNES CARVALHO, CNPAF. |
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Título: |
Prediction models of rice cooking quality. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Cereal Chemistry, v. 95, n. 1, p. 158-166, Jan./Feb. 2018. |
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DOI: |
10.1002/cche.10017 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Polytomous logistic regression; Principal components; Texture analyzer. |
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Thesagro: |
Análise organoleptica; Arroz; Oryza sativa. |
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Thesaurus Nal: |
Cooking quality; Rice; Sensory evaluation. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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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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| 3. |  | PESSOA, D. D. V.; VIDAL, M. S.; BALDANI, J. I.; ARAUJO, J. L. S. de. The effect of sugarcane apoplastic fluidon the global gene expression pattern of strain HRC54 of Herbaspirillum seropedicae. In: SYMPOSIUM ON BIOLOGICAL NITROGEN FIXATION, 16., LATINAMERICAN WORKSHOP OF PGPR, 4., RELARE, 19., 2018, Foz do Iguaçu. Book of abstract....Foz do Iguaçu: Emprapa, 2018. p. 55 Session 5, Short Note V.III| Tipo: Resumo em Anais de Congresso |
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| 7. | | PESSOA, D. D. V.; SANTOS, C. M. dos; VIDAL, M. S.; BALDANI, J. I.; TADRA-SFEIR, M. Z.; SOUZA, E. M. de; ARAUJO, J. L. S. de. Herbaspirillum seropedicae strain HRC54 expression profile in response to sugarcane apoplastic fluid. 3 Biotech, v. 11, Article number 292, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
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| 8. | | POLESE, V.; SILVA, P. R. A.; PESSOA, D. D. V.; TADRA-SFEIR, M. Z.; SOUZA, E. M.; ARAUJO, J. L. S. de; BALDANI, J. I.; VIDAL, M. S. Transcriptional profile of Herbaspirillum rubrisubalbicans in response of sugarcane apoplastic fluid. In: WORLD CONGRESS ON GENETICS APPLIED TO LIVESTOCK PRODUCTION, 11., 2018, Auckland, New Zealand. Proceedings... Massey University, 2018.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agrobiologia. |
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