Authentication of PDO paprika powder (Pimentón de la Vera) by multivariate analysis of the elemental fingerprint determined by ED-XRF. A feasibility study

Abstract

Products with a Protected Denomination of Origin (PDO) are vulnerable to misdescription of their true geographical origin. In this work a method has been developed that allows the authentication of La Vera paprika powder (Pimentón de la Vera), a PDO product from the central-west Spanish region, Extremadura. The mass fractions of Br, Ca, Cr, Cl, Cu, Fe, K, Mn, Ni, P, Rb, S, Sr and Zn determined by energy dispersive X-ray fluorescence (ED-XRF) are used for classification purposes by multivariate analysis using Soft Independent Modelling of Class Analogy (SIMCA) (PCA-Class) and Partial Least Square-Discriminant Analysis (PLS-DA). Sixty-seven paprika samples purchased in supermarkets around Europe and on-line via the official web-site of Pimentón de La Vera, were used to build up the models for prediction purposes. The PCA-class model of La Vera paprika powder had a sensitivity of 82%, a specificity of 100% and an accuracy of 91%, whereas the PLS-DA model had a sensitivity of 100%, a specificity of 91% and an accuracy of 96%.

Amino Acid Profiling with Chemometric Analysis as a Feasible Tool for the Discrimination of Marine-Derived Peptide Powders

“…The four submodels could classify all the 36 samples from the prediction dataset into respective groups with a classification accuracy rate of 100% (Figure 3F). It has been reported that a PCA-Class model built for the authentication of the protected denomination of origin for paprika powder has had an accuracy of 91%, and the PLS-DA model had an accuracy of 96% [30]. These values are lower than those in the present study.…”

“…The Mahalanobis distance (DModX PS+) was used to detect outliers in the four PCA-Class submodels. The sample in which the Mahalanobis distance was larger than Dcrit (95% confidence interval) of certain submodel was considered an outlier [30]. Accuracy was used to determine the PCA-Class model's classification performance, which was defined as the proportion of correctly classified samples to total samples.…”

X-Ray Fluorescence Analysis in Biology and Medicine

“…Пряности классифицируют на группы в зависимости от того, какая часть растения находит практическое применение: семена -горчица, мускатный орех; плоды -анис, тмин, кориандр, кардамон, перец, ваниль, бадьян; цветы и их части -гвоздика, шафран; листья -лавровый лист, розмарин; кора -корица; корниимбирь. Информацию об особенностях применения РФА при исследованиях пряностей можно найти в работах [354][355][356][357][358][359]…”

Geographical Discrimination of Bell Pepper (Capsicum annuum) Spices by (HS)-SPME/GC-MS Aroma Profiling and Chemometrics

“…Unlike detection of adulterants (illegal artificial colorants or bulking agents), false origin labelling or contamination of origin-certified spices with low-quality products cultivated elsewhere cannot be unveiled by conventional targeted analytical methods due to the lack of specific markers directly related to the product origin [6]. Various fingerprinting or profiling methods, based on vibrational spectroscopies [7,8], high-or ultrahigh-performance liquid-chromatography coupled to different detector systems [9][10][11][12], and energy dispersive X-ray fluorescence [13], have been proposed to identify the origin of bell pepper spices. In this context, it has been found that the profiles of phenolic acids, polyphenolic compounds and capsacinoids are promising indicators for geographical traceability purposes.…”