Supervised pattern recognition applied to the discrimination of the floral origin of six types of Italian honey samples

Classifying honeys from the Soria Province of Spain via multivariate analysis

A total of 73 different honeys from seven botanical origins [ling (Calluna vulgaris L.), heather (Erica sp.), rosemary (Rosmarinus officinalis L.), thyme (Thymus vulgaris L.), honeydew (Quercus sp.), spike lavender (Lavandula latifolia M.) and french lavender (Lavandula stoechas L.)] have been classified by applying discriminant analysis to their metal content data and other common physicochemical parameters. Fifteen minerals were identified and quantified using atomic emission spectroscopy (AES) for K and Na, and inductively coupled plasma atomic emission spectrometry (ICP-AES) for Mg, Ca, Al, Fe, Mn, Zn, B, Cu, Co, Cr, Ni, Cd and Pb. Moreover, eight physicochemical parameters were analysed following the Harmonised Methods of the International Honey Commision: ash content, moisture, insoluble matter, reducing sugars, apparent sucrose, diastase activity, free acidity and hydroxymethylfurfural. The honeys analysed were characterised and distinguished using chemometrics. ANOVA highlighted significant differences between the honeys in terms of the mean contents of all variables except apparent sucrose, HMF, Fe and Zn. Principal component analysis was used as a descriptive tool to visualise the data structure in two dimensions, finding relationships between variables and types of honey. Likewise, discriminant analysis, together with various methods (stepwise, forward and backward), was used to select the variables with the highest discriminating power, which allowed us to classify all of the botanical origins considered in this work, achieving a global success rate close to 90% following cross-validation.