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Registro Completo |
Biblioteca(s): |
Embrapa Agrobiologia. |
Data corrente: |
05/11/2013 |
Data da última atualização: |
02/03/2015 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
BOURNAUD, C.; FARIA, S. M. de; SANTOS, J. M. F. dos; TISSEYRE, P.; SILVA, M.; CHANITREUIL, C.; GROSS, E.; JAMES, E. K.; PRIN, Y.; MOULIN, L. |
Afiliação: |
SERGIO MIANA DE FARIA, CNPAB; UESC, BA; IRD, FR; UFRRJ; IRD, FR; UESC, BA; JAMES HUTTON INSTITUTE, UK; CIRAD, FR; IRD, FR. |
Título: |
Burkholderia species are the most common and preferred nodulating symbionts of the Piptadenia group (Tribe mimoseae). |
Ano de publicação: |
2013 |
Fonte/Imprenta: |
Plos One, v. 8, n, 5, p. 1-10, may, 2013. |
DOI: |
DOI: 10.1371/journal.pone.0063478 |
Idioma: |
Inglês |
Palavras-Chave: |
Análise filogeográfica; Philogenetic analysis; Pylogeography; Rizóbio. |
Thesagro: |
Biodiversidade. |
Thesaurus Nal: |
Biodiversity; Bradyrhizobium. |
Categoria do assunto: |
-- |
Marc: |
LEADER 00907naa a2200313 a 4500 001 1970336 005 2015-03-02 008 2013 bl uuuu u00u1 u #d 024 7 $aDOI: 10.1371/journal.pone.0063478$2DOI 100 1 $aBOURNAUD, C. 245 $aBurkholderia species are the most common and preferred nodulating symbionts of the Piptadenia group (Tribe mimoseae). 260 $c2013 650 $aBiodiversity 650 $aBradyrhizobium 650 $aBiodiversidade 653 $aAnálise filogeográfica 653 $aPhilogenetic analysis 653 $aPylogeography 653 $aRizóbio 700 1 $aFARIA, S. M. de 700 1 $aSANTOS, J. M. F. dos 700 1 $aTISSEYRE, P. 700 1 $aSILVA, M. 700 1 $aCHANITREUIL, C. 700 1 $aGROSS, E. 700 1 $aJAMES, E. K. 700 1 $aPRIN, Y. 700 1 $aMOULIN, L. 773 $tPlos One$gv. 8, n, 5, p. 1-10, may, 2013.
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Embrapa Agrobiologia (CNPAB) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Agroindústria de Alimentos. Para informações adicionais entre em contato com ctaa.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Agroindústria de Alimentos; Embrapa Caprinos e Ovinos. |
Data corrente: |
30/04/2020 |
Data da última atualização: |
30/04/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
GALDINO, I. K. C. P. de O.; SALLES, H. O.; SANTOS, K. M. O. dos; VERAS, G.; BURITI, F. C. A. |
Afiliação: |
ISADORA KALINE CAMELO PIRES DE OLIVEIRA GALDINO, Universidade Estadual da Paraíba (UFPB) - Campina Grande, PB, Brazil; HEVILA OLIVEIRA SALLES, CNPC; KARINA MARIA OLBRICH DOS SANTOS, CTAA; GERMANO VERAS, Universidade Estadual da Paraíba (UFPB) - Campina Grande, PB, Brazil; FLÁVIA CAROLINA ALONSO BURITI, Universidade Estadual da Paraíba (UFPB) - Campina Grande, PB, Brazil. |
Título: |
Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS). |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
PeerJ, v. 8, e8619, Feb. 2020. |
DOI: |
10.7717/peerj.8619 |
Idioma: |
Inglês |
Conteúdo: |
Background: In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods: Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky?Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results:The average whey composition values obtained using the referencedmethods were in accordance with the consulted literature. The composition did notdiffer significantly (p> 0.05) between the summer and winter whey samples.The PLSR models were made available using the followingfigures of merit:coefficients of determination of the calibration and prediction models (R2cal andR2pred, respectively) and the Root Mean Squared Error Calibration and Prediction(RMSEC and RMSEP, respectively). The best models used raw data for fat andprotein determinations and the values obtained by NIRS for both parameters wereconsistent with their referenced methods. Consequently, NIRS can be used todetermine fat and protein in goat whey. MenosBackground: In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods: Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky?Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results:The average whey composition values obtained using the referencedmethods were in accordance with the consulted literature. The composition did notdiffer significantly (p> 0.05) between the summer and winter whey samples.The PLSR models were made available using the followingfigures of merit:coefficients of determination of the calibration and prediction models (R2cal andR2pred, respectively) and the Root Mean Squared Error Calibration and Prediction(RMSEC and RMSEP, respectively... Mostrar Tudo |
Palavras-Chave: |
By-product upgrading; Chemometric analysis; Proteína do soro de leite; Seasonal composition. |
Thesagro: |
Análise de Alimento; Produto Derivado do Leite; Tecnologia de Alimento. |
Thesaurus NAL: |
Biochemistry; Food analysis; Food science; Food technology; Whey cheeses; Whey protein. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
Marc: |
LEADER 02737naa a2200337 a 4500 001 2121974 005 2020-04-30 008 2020 bl uuuu u00u1 u #d 024 7 $a10.7717/peerj.8619$2DOI 100 1 $aGALDINO, I. K. C. P. de O. 245 $aProximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS).$h[electronic resource] 260 $c2020 520 $aBackground: In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods: Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky?Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results:The average whey composition values obtained using the referencedmethods were in accordance with the consulted literature. The composition did notdiffer significantly (p> 0.05) between the summer and winter whey samples.The PLSR models were made available using the followingfigures of merit:coefficients of determination of the calibration and prediction models (R2cal andR2pred, respectively) and the Root Mean Squared Error Calibration and Prediction(RMSEC and RMSEP, respectively). The best models used raw data for fat andprotein determinations and the values obtained by NIRS for both parameters wereconsistent with their referenced methods. Consequently, NIRS can be used todetermine fat and protein in goat whey. 650 $aBiochemistry 650 $aFood analysis 650 $aFood science 650 $aFood technology 650 $aWhey cheeses 650 $aWhey protein 650 $aAnálise de Alimento 650 $aProduto Derivado do Leite 650 $aTecnologia de Alimento 653 $aBy-product upgrading 653 $aChemometric analysis 653 $aProteína do soro de leite 653 $aSeasonal composition 700 1 $aSALLES, H. O. 700 1 $aSANTOS, K. M. O. dos 700 1 $aVERAS, G. 700 1 $aBURITI, F. C. A. 773 $tPeerJ$gv. 8, e8619, Feb. 2020.
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