Examinando por Autor "Eskildsen, Carl Emil"

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  • Miniatura
    Publicación
    Non-destructive fluorescence spectroscopy combined with second-order calibration as a new strategy for the analysis of the illegal Sudan I dye in paprika powder
    (Elsevier, 2020-05) Eskildsen, Carl Emil; Muñoz de la Peña, Arsenio; Galeano Díaz, Teresa; Wold, Jens Petter; Monago Maraña, Olga
    This paper presents a novel strategy for determination of the illegal dye Sudan I in paprika powder. The method is based on fluorescence spectroscopy combined with second-order calibration, which was employed for the first time for this purpose. The method is non-destructive and requires no sample preparation. It was probed that Sudan I exhibited fluorescence; however, the color of paprika samples affected the signal and it was not possible to quantify this adulterant by means of univariate and first-order calibration. To model the effect of variability of color in samples, a central composite experimental design was performed with varying ASTA (American Spices Trade Association) color values and Sudan I concentrations. Different second-order algorithms were tried for quantification. The best results for calibration and validation were obtained from Unfolded-Partial Least-Squares (U-PLS) and Multi-way Partial Least-Squares (N-PLS). The level of detection ranges were 0.4 – 3 mg/g and 0.5 – 3 mg/g for U-PLS and N-PLS, respectively. This was lower than other methods found in the literature.
  • Miniatura
    Publicación
    Non-destructive Raman spectroscopy as a tool for measuring ASTA color values and Sudan I content in paprika powder
    (Elsevier, 2019-02-15) Eskildsen, Carl Emil; Afseth, Nils Kristian; Galeano Díaz, Teresa; Muñoz de la Peña, Arsenio; Wold, Jens Petter; Monago Maraña, Olga
    The aim of this study was developing a non-destructive method for the determination of color in paprika powder as well as for detecting possible adulteration with Sudan I. Non-destructive Raman spectroscopy was applied directly to paprika powder employing a laser excitation of 785 nm for the first time. The fluorescence background was estimated, by fitting a polynomial to each spectrum, and then subtracted. After preprocessing the spectra, some peaks were clearly identified as characteristic from pigments present in paprika. The preprocessed Raman spectra were correlated with the ASTA color values of paprika by partial least squares regression (PLSR). Twenty-five paprika samples were adulterated with Sudan I at different levels and the PLSR model was also obtained. The coefficients of determination (R2) were 0.945 and 0.982 for ASTA and Sudan I concentration, respectively, and the root mean square errors of prediction (RMSEP) were 8.8 ASTA values and 0.91 mg/g, respectively. Finally, different approaches were applied to discriminate between adulterated and non-adulterated samples. Best results were obtained for partial least squares – discriminant analysis (PLS-DA), allowing a good discrimination when the adulteration with Sudan I was higher than 0.5%.
  • Miniatura
    Publicación
    Untargeted classification for paprika powder authentication using visible – Near infrared spectroscopy (VIS-NIRS)
    (Elsevier, 2021-03) Eskildsen, Carl Emil; Galeano Díaz, Teresa; Muñoz de la Peña, Arsenio; Wold, Jens Petter; Monago Maraña, Olga
    This paper describes a non-destructive screening method for authentication of paprika belonging to the Spanish Protected Designation of Origin (PDO) “Pimentón de La Vera”. Different multivariate classification models were developed in order to differentiate PDO and non-PDO samples, using visible-near infrared spectra as fingerprint for each paprika sample. Sample treatment was not required. Principal component analysis (PCA) was applied in different spectral ranges: 400–2500, 400–800 and 800–2500 nm. In all spectral ranges, PCA was largely able to differentiate PDO from non-PDO samples. Partial least-squares - discriminant analysis (PLS-DA), PCA-linear discriminant analysis (LDA) and PCA-quadratic discriminant analysis (QDA) were used as classification methods in the different spectral ranges. All methods were able to differentiate PDO from non-PDO samples, with error rates (ER) lower than 0.15. The best models were those obtained with PLS-DA in the NIR range (800–2500 nm), showing ERs lower than 0.07 and error indexes (IERROR) (false positives) lower than 0.05.