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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
10/11/2023 |
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Data da última atualização: |
10/11/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, Y. J. S.; SILVA, A. C. A.; CARVALHO, R. A. de; COLNAGO, L. A.; VANIN, F. M. |
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Afiliação: |
University of Sao Paulo, Faculty of Animal Science and Food Engineering (USP/FZEA); University of Sao Paulo, Faculty of Animal Science and Food Engineering (USP/FZEA); University of Sao ˜ Paulo, Faculty of Animal Science and Food Engineering (USP/FZEA); LUIZ ALBERTO COLNAGO, CNPDIA; University of Sao ˜ Paulo, Faculty of Animal Science and Food Engineering (USP/FZEA). |
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Título: |
Rapid quantification of phenolic content and antioxidant activity in cookies produced with amazonian palm fruit flour using Micro-NIR spectrometer and PLS regression. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Microchemical Journal, v. 195, 109398, 2023. |
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Páginas: |
1 - 7 |
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ISSN: |
0026-265X |
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DOI: |
https://doi.org/10.1016/j.microc.2023.109398 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT There are several reports of the potential benefits of phenolic compound (PC) in food products, due to their antioxidant activities (AC). However, in recent years, new research results have demonstrated that PC has potential health risks due to the reduction in absorption of protein nutrients and cytotoxic effects. The PC and AC quantifications are laborious and time-consuming methods, therefore it is necessary to develop simple, fast and precise method to determine these parameters, not only in the raw materials, but also in food products. Therefore, this study focused on the potential of Micro-NIR spectrometer data modeled with partial least square regression to predict PC and AC in processed food (cookies) prepared with peach palm (PP), that is rich in PC. The cookies were prepared using 12.5 to 100 % of PP flour in substitution to wheat flour (WF). The NIR model for AC, determined by the ferric reducing antioxidant power (FRAP) method, shows R2 cv = 0.93 (regression coefficient of cross-validation step); RMSECV = 0.05; R2 p = 0.87 (regression coefficient of prediction step); RMSEP = 0.04; RPD = 2.73, and by 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) radical capture (ABTS) exhibit R2 cv = 0.83; RMSECV = 3.72; R2 p = 0.70; RMSEP = 4.12; RPD = 1.76, and for PC, determined by FolinCiocalteu, shows R2 cv = 0.86; RMSECV = 0.44; R2 p = 0.80; RMSEP = 0.43; RPD = 2.04. These excellent results, mainly for FRAP and PC, demonstrated that portable NIR spectrometers could be a fast, simple and reliable method to predict PC and AC in cookies prepared with different proportion of PP flour and WF. Similar models can also be developed to predict PC and AC in other food products. MenosABSTRACT There are several reports of the potential benefits of phenolic compound (PC) in food products, due to their antioxidant activities (AC). However, in recent years, new research results have demonstrated that PC has potential health risks due to the reduction in absorption of protein nutrients and cytotoxic effects. The PC and AC quantifications are laborious and time-consuming methods, therefore it is necessary to develop simple, fast and precise method to determine these parameters, not only in the raw materials, but also in food products. Therefore, this study focused on the potential of Micro-NIR spectrometer data modeled with partial least square regression to predict PC and AC in processed food (cookies) prepared with peach palm (PP), that is rich in PC. The cookies were prepared using 12.5 to 100 % of PP flour in substitution to wheat flour (WF). The NIR model for AC, determined by the ferric reducing antioxidant power (FRAP) method, shows R2 cv = 0.93 (regression coefficient of cross-validation step); RMSECV = 0.05; R2 p = 0.87 (regression coefficient of prediction step); RMSEP = 0.04; RPD = 2.73, and by 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) radical capture (ABTS) exhibit R2 cv = 0.83; RMSECV = 3.72; R2 p = 0.70; RMSEP = 4.12; RPD = 1.76, and for PC, determined by FolinCiocalteu, shows R2 cv = 0.86; RMSECV = 0.44; R2 p = 0.80; RMSEP = 0.43; RPD = 2.04. These excellent results, mainly for FRAP and PC, demonstrated that portable NIR spectrometer... Mostrar Tudo |
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Palavras-Chave: |
Peach palm; Portable NIR. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02470naa a2200229 a 4500
001 2158183
005 2023-11-10
008 2023 bl uuuu u00u1 u #d
022 $a0026-265X
024 7 $ahttps://doi.org/10.1016/j.microc.2023.109398$2DOI
100 1 $aSANTOS, Y. J. S.
245 $aRapid quantification of phenolic content and antioxidant activity in cookies produced with amazonian palm fruit flour using Micro-NIR spectrometer and PLS regression.$h[electronic resource]
260 $c2023
300 $a1 - 7
520 $aABSTRACT There are several reports of the potential benefits of phenolic compound (PC) in food products, due to their antioxidant activities (AC). However, in recent years, new research results have demonstrated that PC has potential health risks due to the reduction in absorption of protein nutrients and cytotoxic effects. The PC and AC quantifications are laborious and time-consuming methods, therefore it is necessary to develop simple, fast and precise method to determine these parameters, not only in the raw materials, but also in food products. Therefore, this study focused on the potential of Micro-NIR spectrometer data modeled with partial least square regression to predict PC and AC in processed food (cookies) prepared with peach palm (PP), that is rich in PC. The cookies were prepared using 12.5 to 100 % of PP flour in substitution to wheat flour (WF). The NIR model for AC, determined by the ferric reducing antioxidant power (FRAP) method, shows R2 cv = 0.93 (regression coefficient of cross-validation step); RMSECV = 0.05; R2 p = 0.87 (regression coefficient of prediction step); RMSEP = 0.04; RPD = 2.73, and by 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) radical capture (ABTS) exhibit R2 cv = 0.83; RMSECV = 3.72; R2 p = 0.70; RMSEP = 4.12; RPD = 1.76, and for PC, determined by FolinCiocalteu, shows R2 cv = 0.86; RMSECV = 0.44; R2 p = 0.80; RMSEP = 0.43; RPD = 2.04. These excellent results, mainly for FRAP and PC, demonstrated that portable NIR spectrometers could be a fast, simple and reliable method to predict PC and AC in cookies prepared with different proportion of PP flour and WF. Similar models can also be developed to predict PC and AC in other food products.
653 $aPeach palm
653 $aPortable NIR
700 1 $aSILVA, A. C. A.
700 1 $aCARVALHO, R. A. de
700 1 $aCOLNAGO, L. A.
700 1 $aVANIN, F. M.
773 $tMicrochemical Journal$gv. 195, 109398, 2023.
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Embrapa Instrumentação (CNPDIA) |
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| 3. |  | SANTOS, Y. J. S.; FACCHINATTO, W. M.; ROCHETTI, A. L.; CARVALHO, R. A.; Le FEUNTEUN; FUKUMASU, H.; MORZEL, M.; COLNAGO, L. A.; VANIN, F. M. Systemic characterization of pupunha (Bactris gasipaes) flour with views of polyphenol content on cytotoxicity and protein in vitro digestion. Food Chemistry, v. 405, 134888, 2023. 9 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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