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    Study of adulteration of extra virgin olive oil with peanut oil using FTIR spectroscopy and chemometrics
    2015 - Vasconcelos, Magda; Coelho, Luís; Barros, Ana Isabel Ramos Novo Amorim de; Almeida, José Manuel Marques Martins de
  • ItemAcesso Aberto
    Investigation of adulteration of sunflower oil with thermally deteriorated oil using Fourier transform mid-infrared spectroscopy and chemometrics
    2015-03-13 - Vilela, Joana; Coelho, Luís; Almeida, José Manuel Marques Martins de
    Fourier transform infrared spectroscopy based on attenuated total reflectance sampling technique, combined with multivariate analysis methods was used to monitor the adulteration of pure sunflower oil (SO) with thermally deteriorated oil (TDO). Contrary to published research, in this work, SO was thermally deteriorated in the absence of foodstuff. SO samples were exposed to temperatures between 125 and 225°C from 6 to 24 h. Quantification of adulteration of SO with TDO, based on principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) applied to mid-infrared spectra and to their first and second derivatives is reported for the first time. Infrared frequencies associated with the biochemical differences between TDO samples deteriorated in different conditions were investigated by principal component analysis (PCA). LDA was effective in the twofold classification presence/absence of TDO in adulterated SO (with 5% V/V of less of TDO). It provided 93.7% correct classification for the calibration set and 91.3% correct classification when cross-validated. A detection limit of 1% V/V of TDO in SO was determined. Investigation of an external set of samples allowed the evaluation of the predictability of the models. The regression coefficient (R2) for prediction was 0.95 and 0.96 and the RMSE was 2.1 and 1.9% V/V when using the PCR or PLS-R models, respectively, and the first derivative of spectra. To the best of our knowledge, no investigation of adulteration of SO with TDO based on PCR, PLS-R, and LDA has been reported so far.
  • ItemAcesso Aberto
    Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions
    2020 - Reis, Samuel; Fraga, Helder; Carlos, Cristina; Silvestre, José; Eiras-Dias, José; Rodrigues, Pedro; Santos, J. A.
    Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, Dão, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties × 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models (GSM) were constructed (budburst: d = −0.6, e = 8.6; flowering: d = −0.6, e = 13.7; veraison: d = −0.5, e = 13.2). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of varietyspecific parameters. Overall, the RMSE (root-mean-squared-error) is < 7 days, while the EF (efficiency coefficient) is > 0.5. Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. These results can be used to more accurately monitor and predict grapevine phenology during a given season, thus supporting decision making in the Portuguese wine sector.