Abstract
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: 0.690–0.821) 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.
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References
Jacobsen, S.E., Mujica, A., Jensen, C.R.: The resistance of quinoa (Chenopodium quinoa, Willd.) to adverse abiotic factors. Food Rev. Int. 19(1–2), 99–109 (2003)
Oliveri, P., Di Egidio, V., Woodcock, T., Downey, G.: Application of class-modelling techniques to near infrared data for food authentication purposes. Food Chem. 125, 1450–1456 (2011)
Sudar, R., Jurković, Z., Galonja, M., Turk, I., Arambašić, M.: Application of Near Infrared Transmission for the determination of ash in wheat flour. Agriculturae Conspectus Scientificus 72(3), 233–238 (2007)
Mevik, B.H., Wehrens, R., Liland, K.H.: Pls: partial least squares and principal component regression. R package version 2.5-0. Available online at https://cran.r-project.org/web/packages/pls/ (2015). Accessed 04 Feb 2017
AOAC: Official methods of analysis of the association of analytical chemists international. In Horwitz, W. (ed.), 17th ed., AOAC International, Gaithersburg, MD, USA (2000)
Panero, P.S., Panero, F.S., Panero, J.S., Silva, H.E.B.: Application of extended multiplicative signal correction to short-wavelength near infrared spectra of moisture in marzipan. J. Data Anal. Inf. Process. 1(3), 30–34 (2013)
Mevik, B.H., Wehrens, R.: The pls package: principal component and partial least squares regression in R. J. Stat. Softw. 18(2), 1–24 (2007)
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T.: Classification and regression training. Package “caret”. Date 2016-11-10. Repository CRAN. Available online at https://cran.r-project.org/web/packages/caret/index.html (2016). Accessed 04 Feb 2017
Ripley, B., Venables, B., Bates, D.M., Hornik, K., Gebhardt, A., Firth, D.: Modern applied statistics with s. package “caret”. Date 2016-04-21. Repository CRAN. Available online at https://cran.r-project.org/web/packages/MASS/index.html (2016). Accessed 04 Feb 2017
Liland, K.H.: Extended multiplicative signal correction. package “EMSC”. Date 2016-04-24. Repository CRAN. Available online https://cran.r-project.org/web/packages/EMSC/index.html (2016). Accessed 04 Feb 2017
Stevens, A., Ramirez-Lopez, L.: An introduction to the prospectr package. Vignette R package version 0.1.3. Available online at https://github.com/antoinestevens/prospectr (2013). Accessed 04 Feb 2017
R Core Team: R, a language and environment for statistical computing. R Foundation for Stastistical Computing, Vienna, Austria. Available online at http://www.R-project.org/ (2016). Accessed 04 Feb 2017
Ferreira, D.S., Pallone, J.A.L., Poppi, R.J.: Direct analysis of the main chemical constituents in Chenopodium quinoa grain using Fourier transform near-infrared spectroscopy. Food Control 48, 91–95 (2015)
González-Martín, M.I., Moncada, G.W., Fischer, S., Escuredo, O.: Chemical characteristics and mineral composition of quinoa by near-infrared spectroscopy. J. Sci. Food Agric. 94(5), 876–881 (2014)
Vega-Gálvez, A., Miranda, M., Vergara, J., Uribe, E., Puente, L., Martínez, E.: Nutrition facts and functional potential of quinoa (Chenopodium quinoa willd.), an ancient Andean grain: a review. J. Sci. Food Agric., 90(15), 2541–2547 (2010)
Repo-Carrasco-Valencia, R., Hellström, J.K., Pihlava, J.M., Mattila, P.H.: Flavonoids and other phenolic compounds in Andean indigenous grains: Quinoa (Chenopodium quinoa), kañiwa (Chenopodium pallidicaule) and kiwicha (Amaranthus caudatus). Food Chem. 120(1), 128–133 (2010)
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Encina-Zelada, C. et al. (2018). Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy. In: Mortal, A., et al. INCREaSE . INCREaSE 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-70272-8_18
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DOI: https://doi.org/10.1007/978-3-319-70272-8_18
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