TY - JOUR
T1 - Authentication of paprika using HPLC-UV fingerprints
AU - Cetó, Xavier
AU - Sánchez, Cristina
AU - Serrano, Nuria
AU - Díaz-Cruz, José Manuel
AU - Nuñez, Oscar
PY - 2020
Y1 - 2020
N2 - In this work we combine simple extraction and HPLC-UV methodologies with chemometric pattern-recognition strategies in order to obtain characteristic fingerprints of phenolic compounds that allow the authentication of paprika samples. To illustrate the potential of the proposed approach, two different adulteration scenarios were considered, namely adulteration of paprika based on its type (sweet, bittersweet and spicy) as well as on its region (Murcia, la Vera and Czech Republic). Upon preparation of a proper set of samples, those were analysed using a C18 reversed-phase column and registered chromatograms were then compressed employing fast Fourier transform (FFT) to reduce the large dimensionality of the data set, while preserving all relevant features. Next, data were analysed using linear discriminant analysis (LDA) for the qualitative discrimination of adulterated samples, or by partial least-squares regression (PLS) modelling to quantitatively assess the adulteration degree.
AB - In this work we combine simple extraction and HPLC-UV methodologies with chemometric pattern-recognition strategies in order to obtain characteristic fingerprints of phenolic compounds that allow the authentication of paprika samples. To illustrate the potential of the proposed approach, two different adulteration scenarios were considered, namely adulteration of paprika based on its type (sweet, bittersweet and spicy) as well as on its region (Murcia, la Vera and Czech Republic). Upon preparation of a proper set of samples, those were analysed using a C18 reversed-phase column and registered chromatograms were then compressed employing fast Fourier transform (FFT) to reduce the large dimensionality of the data set, while preserving all relevant features. Next, data were analysed using linear discriminant analysis (LDA) for the qualitative discrimination of adulterated samples, or by partial least-squares regression (PLS) modelling to quantitatively assess the adulteration degree.
KW - Paprika
KW - Food authentication
KW - Adulteration
KW - Liquid chromatography
KW - Partial least-squares regression
UR - https://www.scopus.com/pages/publications/85079662315
U2 - 10.1016/j.lwt.2020.109153
DO - 10.1016/j.lwt.2020.109153
M3 - Article
SN - 0023-6438
VL - 124
JO - LWT - Food Science and Technology
JF - LWT - Food Science and Technology
M1 - 109153
ER -