Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/56434
Título: | Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy |
Autor(es): | Encina-Zelada, Christian René Cadavez, Vasco Pereda, Jorge Gómez-Pando, Luz Salvá-Ruíz, Bettit Ibañez, Martha Teixeira, J. A. Gonzales-Barron, Ursula |
Palavras-chave: | Quinoa Flour Calibration Chemometrics Bootstrap |
Data: | 2018 |
Editora: | Springer International Publishing AG |
Citação: | Encina-Zelada, C.; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Ibañez, Martha; Teixeira, José A.; Gonzales-Barron, Ursula, Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy. INCREaSE 2017 - Proceedings of the 1st International Congress on Engineering and Sustainability in the XXI Century. Faro, Portugal, Oct 11-13, 2017, Springer International Publishing, 227-235, 2018. ISBN: 978-3-319-70272-8 |
Resumo(s): | The aim of this study was to develop chemometric models for protein, fat, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour obtained from grains of 70 different cultivars were scanned while dietary constituents were determined by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. Next, the Canonical Powered Partial Least Squares (CPPLS) algorithm was applied, and models were compared in terms of accuracy and predictability. For all models, root mean square errors of cross-validation (RMSECV), root meat square errors of prediction (RMSEP) and coefficient of correlation of cross-validation (RCV) were computed. Robust models were obtained when quinoa spectra were pre-processed using EMSC of polynomial degree 2 for both fat (RMSECV: 0.268% and RMSEP: 0.256%) and carbohydrates (RMSECV: 0.641% and RMSEP: 0.643%) following extraction of five CPPLS latent variables. Good coefficients of correlation of prediction (RP: 0.6900.821) were found for all constituents when models were validated on a test data set consisting of 13 quinoa flour spectra. Thus, good predictions of the dietary constituents of quinoa flour could be achieved by using NIT technology, as implied by the low coefficient of variation of prediction (CVP): 5.64% for protein, 3.88% for fat 7.32% for ashes and 0.80% for carbohydrates contents. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/56434 |
ISBN: | 978-3-319-70272-8 |
DOI: | 10.1007/978-3-319-70272-8_18 |
Versão da editora: | https://link.springer.com/book/10.1007/978-3-319-70272-8 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Artigos em Livros de Atas / Papers in Proceedings |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
document_47800_1.pdf | 247,77 kB | Adobe PDF | Ver/Abrir |