Geographical characterization of Spanish PDO paprika by multivariate analysis of multielemental content

• Published in: Talanta (Oxford) Vol. 128; pp. 15 - 22
• Main Authors: Palacios-Morillo, Ana, Jurado, José Marcos, Alcázar, Ángela, de Pablos, Fernando
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.10.2014
• Subjects: Artificial neural network; Boron - analysis; Capsicum - chemistry; Discriminant Analysis; Geography; Hydrogen Peroxide - chemistry; Inductively coupled plasma atomic emission spectrometry; Linear discriminant analysis; Metals - analysis; Multivariate Analysis; Nitric Acid - chemistry; Paprika; Pattern recognition; Perchlorates - chemistry; Phosphorus - analysis; Reproducibility of Results; Soft independent modeling of class analogy; Spain; Spectrophotometry, Atomic; Support Vector Machine; Support vector machines; Spain; Support vector machines; Paprika; Soft independent modeling of class analogy; Pattern recognition; Linear discriminant analysis; Artificial neural network; Inductively coupled plasma atomic emission spectrometry
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2014.04.025

A multielemental analytical method has been proposed to determine the contents of Al, B, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr and Zn in paprika samples from the two Protected Designations of Origin recognized in Spain, such as Murcia and La Vera (Extremadura). The samples are mineralized by acid wet digestion using a mixture of perchloric and nitric acids and analyzed by means of inductively coupled plasma atomic emission spectroscopy. The method performance has been checked studying the absence of matrix effect, trueness, precision, linearity, limit of detection and limit of quantification. The proposed method has been applied to analyze samples of sweet, hot and hot/sweet paprika from the considered production areas. Differences between paprika samples from Murcia and Extremadura were found and pattern recognition methods, such as linear discriminant analysis, linear support vector machines, soft independent modeling of class analogy and multilayer perceptrons artificial neural networks, has been used to obtain classification models. Sweet and hot/sweet paprika types were differentiated by means of linear models and hot paprika was differentiated by using artificial neural networks. A model based on artificial neural networks is proposed to differentiate the geographical origin of paprika, with independence of the type, leading to an overall classification performance of 99%. [Display omitted] •Spanish paprika characterized according to their elemental content.•Spanish PDOs are compared by means of pattern recognition techniques.•LDA, SVM and SIMCA perform well for sweet and hot/sweet paprika.•Hot paprika samples are better differentiated by ANN.