Browsing by Author "Liland, Kristian H."
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- Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopyPublication . Encina-Zelada, Christian; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, José; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, UrsulaThe aim of this study was to develop robust chemometric models for the routine determination of dietary constituents of quinoa (Chenopodium quinoa Willd.) using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa grains of 77 cultivars were acquired while dietary constituents were determined by reference methods. Spectra were subjected to multiplicative scatter correction (MSC) or extended multiplicative signal correction (EMSC), and were (or not) treated by Savitzky-Golay (SG) filters. Latent variables were extracted by partial least squares regression (PLSR) or canonical powered partial least squares (CPPLS) algorithms, and the accuracy and predictability of all modelling strategies were compared. Smoothing the spectra improved the accuracy of the models for fat (root mean square error of cross-validation, RMSECV: 0.319–0.327%), ashes (RMSECV: 0.224–0.230%), and particularly for protein (RMSECV: 0.518–0.564%) and carbohydrates (RMSECV: 0.542–0.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.248–0.335%) and ashes (RMSEP: 0.137–0.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.376–0.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.651–0.901] ), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.650–0.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.478–0.654] ) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.658–0.833]).
- Estimation of proximate composition of quinoa (Chenopodium quinoa Willd.) flour by near-infrared transmission spectroscopy modelsPublication . Encina-Zelada, Christian; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Teixeira, José; Ibañez, Martha; Liland, Kristian H.; Gonzales-Barron, UrsulaThe aim of this study was to develop chemometric models for protein, fat, moisture, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour originated from grains of 77 different cultivars were scanned while dietary constituents were determined in duplicate by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. The performance of two algorithms, partial least squares regression (PLSR) and Canonical Powered Partial Least Squares (CPPLS), was compared in terms ofaccuracy and predictability. For all dietary constituents,as opposed to PLSR, the CPPLS regression produced lower root meat square errors of cross-validation (RMSECV), lower root meat square errors of prediction (RMSEP) and higher coefficient of correlation of cross-validation (RCV) while retaining fewer number of components. More robust models were obtained when quinoa flour spectra were pre-processed using EMSC of polynomial degree 2 for moisture (RMSECV: 0.564 and RMSEP: 0.648), fat (RMSECV: 0.268 and RMSEP: 0.256) and carbohydrates (RMSECV: 0.641 and RMSEP: 0.643) following extraction of five CPPLS latent variables. High coefficients of correlation of prediction (RP: 0.7-0.8) were found when models were validated on a test data set consisting of 15 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): 6.1% for moisture, 5.6% for protein, 3.9% for fat 7.4% for ashes and 0.8% for carbohydrates contents.