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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. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agroindústria de Alimentos; Embrapa Caprinos e Ovinos. |
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Data corrente: |
30/04/2020 |
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Data da última atualização: |
30/04/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
GALDINO, I. K. C. P. de O.; SALLES, H. O.; SANTOS, K. M. O. dos; VERAS, G.; BURITI, F. C. A. |
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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. |
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Título: |
Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS). |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
PeerJ, v. 8, e8619, Feb. 2020. |
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DOI: |
10.7717/peerj.8619 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
By-product upgrading; Chemometric analysis; Proteína do soro de leite; Seasonal composition. |
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Thesagro: |
Análise de Alimento; Produto Derivado do Leite; Tecnologia de Alimento. |
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Thesaurus Nal: |
Biochemistry; Food analysis; Food science; Food technology; Whey cheeses; Whey protein. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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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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Embrapa Caprinos e Ovinos (CNPC) |
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| 6. |  | SOUZA, K. X. S. de; DAVIS, J. Aligning ontologies and evaluating concept similarities. In: OTM CONFEDERATED INTERNATIONAL CONFERENCES, 3.; COOPERATIVE INFORMATION SYSTEMS; DISTRIBUTED OBJECTS AND APPLICATIONS; ONTOLOGIES, DATABASES AND APPLICATIONS OF SEMANTICS, 2014, Agia Napa. On the move to meaningful internet systems 2004: CoopIS, DOA, and ODBASE: proceedings. Berlin: Springer, 2004. part. II, p. 1012-1029. (Lecture notes in computer science, 3291). Editores: Robert Meersman, Zahir Tari. CoopIS, DOA, ODBASE 2004.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 8. | | SOUZA, K. X. S. de; DAVIS, J. Using an aligned ontology to process user queries. In: INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: METHODOLOGY, SYSTEMS, APPLICATIONS, 11., 2004, Varna. Proceedings... Berlin: Springer, 2004. p. 44-53. (Lecture notes in artificial intelligence, 3192). Editores: Christoph Bussler, Dieter Fensel. AIMSA 2004.| Biblioteca(s): Embrapa Agricultura Digital. |
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