Percorrer por autor "Pereda, Jorge"
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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 spectroscopyPublication . Encina-Zelada, Christian; Cadavez, Vasco; Pereda, Jorge; Gómez-Pando, Luz; Salvá-Ruíz, Bettit; Ibañez, Martha; Teixeira, José; Gonzales-Barron, UrsulaThe 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.
- 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.
- Is the CAPM a proper method to be used in the process of computing the value in use? The estimation period effect (using monthly returns)Publication . Pereda, Jorge; Lopes, José CarlosIn the impairment process, IAS 36 establishes that the asset (or group of assets) recoverable amount is the highest from the fair value less costs to sell and the value in use. To compute the value in use amount, IAS 36 determines the use of present value technique and establishes guidelines for it, namely for cash flows estimation and the discount rate calculation. The last IAS 36 update is strongly based on the FASB SFAC 7. If we compare the guidelines regarding the use of the present value technique, we will find the text very similar. However, regarding the guidelines of the discount rate to be used in the process of computing the present value, a particular difference can be seen, SFAC 7 determines that the Capital Assets Pricing Model (CAPM) is, in general, not a proper method to be used to compute a discount rate. On the contrary, IASB, and despite the IAS 36 update, kept the use of CAPM as a good solution as a starting point to compute a proper discount rate. CAPM results depend on how the beta element is computed. Bloomberg, Value Line, Baseline and Ibbotson are among those companies, which provide betas based on the CAPM or its enhanced versions. The most important differences in computing betas arise from the type of returns used (daily or monthly), the index used as a proxy for the market portfolio and the estimate period (5 years, 2 years or 1 year). In this paper, applying CAPM (using monthly returns and the index S&P 500 as a proxy for the market portfolio) to a dotcom sample and considering an acceptable materiality level, we analyse the effect of the estimate period on the results (i.e. the discount rate) provided by the model. The results show high discount rates variations (according to the estimation period used), and consequently material variations in the “value in use” indicating that they are not reliable to be used for reliable accounting measurements.