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A highly informative chemometrical method using linear discriminant analysis was employed in this paper for the characterisation and classification of hoarfrost samples collected in Poland. From the classification matrix, it was concluded that good discrimination (an overall 69% correct classification and 93% correct classification for the samples originated from Gdańsk) between hoarfrost samples of different origin could be achieved by using the ion concentrations retained in the model. Discriminant analysis was not only used for classifying the hoarfrost samples according to origin and location but also for detecting the most important variables that discriminate between the groups. It was found, according to different statistical parameters, that the highest contribution to the discriminatory power of the model was given by NO3– (λ*=0814; F=5.54), PO43– (λ*=0.888; F=3.06) and F- (λ*= 0.892; F = 2.92). The smallest contribution was observed for Mg2+ (λ*=0.961; F=0.98). The results obtained in this study illustrate that discriminant analysis allows a rational classification and grouping of hoarfrost samples using chemical composition for their characterisation.
