Novel approach for the characterisation of Sicilian honeys based on the correlation of physico-chemical parameters and artificial senses

Abstract

The aim of this work was to characterise some of the most representative Sicilian honeys. Sugars, pH and minerals were determined with conventional analyses. Chestnut honeys showed the lowest sugar content, with a fructose and glucose sum of 62.31 g/100g. Citrus and Eucalyptus honeys showed the highest fructose content (38.08 and 38.04 g/100g), while Citrus and Sulla honeys had the highest sucrose content (3.16 and 3.92 g/100g). The highest fructose to glucose ratio was 1.59, found for Chestnut honeys, which had also the highest pH-value of 4.98. Potassium is the most abundant element in honey and the highest values were found for Chestnut and Eucalyptus honey (4.412 and 1.956 mg/g). Among micro-minerals, Zinc showed the highest concentration, ranging from 4.64 to 7.16 µg/g. Alongside physicochemical analyses, E-tongue and computer vision was used to estimate the organoleptic proprieties of honey. In particular, Pearson’s correlation was used to study the relationship between the electrical E-tongue’ signals, pH and sugars content, which have a major influence on the main taste attributes investigated in honey. Chestnut honeys scored the lowest values for the sweet and sour taste, being the bitterest among the samples evaluated. On the other hand, Sulla and Citrus honeys were the sweetest and the sourest. The colour of honey was examined with machine vision and the weight of the different minerals on the colour parameters was disclosed, resulting in dark colours correlated to sodium and microelements, and in a light colour that showed a negative correlation with potassium and magnesium.Highlights A novel instrumental approach was used to characterise Sicilian honey. Physicochemical parameters of Sicilian honeys were determined. Electronic senses were used to perform a human-like sensory evaluation. Correlation between physicochemical and organoleptic properties has been disclosed.

Keywords

• honey
• physicochemical parameters
• e-tongue
• computer vision
• data fusion