A method based on the measurement of color is proposed to differentiate paprika samples from different
geographical origin. ASTA scale and coordinates in the CIELAB color space were obtained from UV–Vis
spectra of acetone extracts of samples. Samples of sweet, hot/sweet and hot paprika from the two recognized
Spanish Protected Designation of Origin, Murcia and Extremadura, were analyzed. Two strategies
were considered with the aim of building classification models. The first used the computed color parameters
as input data, whilst the second used the scores of the samples obtained after applying principal
component analysis to reduce the dimensionality of the data matrix from the absorbance spectrum. The
developed pattern recognition models were based on linear discriminant analysis, support vector machines
and multilayer perceptron artificial neural networks. In both considered strategies, the best results
were obtained in the case of artificial neural networks, with classification efficiencies ranging from 92%
to 95% for the different varieties of paprika.