EVALUATING BASIL VARIETIES: SENSORY, CHEMICAL, AND VISUAL ANALYSES FOR PESTO INDUSTRY APPLICATIONS

Basil (Ocimum spp., L.), known for its aromatic properties, is cultivated worldwide and is a cornerstone of Mediterranean cuisine. Ocimum basilicum L. is an important ingredient in the famous Pesto alla Genovese sauce and it is recognised by the EU as a Traditional Speciality Guaranteed (TSG). Pesto sensory quality, critical for the food industry, is determined by its organoleptic properties, which include colour, aroma, taste and trigeminal/chemaesthetic perception. These properties are influenced by a complex interplay of factors, including genetic, ontogenetic and morphogenetic variations in basil, biotic elements and abiotic factors. Cultivar and variety also play a key role in defining the quality characteristics of basil, and their characterisation can lead to the identification and improvement of stable, high quality cultivars with excellent flavour and yield, as well as the selection of basil cultivars suitable for commercial use.To date, the organoleptic properties of basil have been analysed independently of its chemical composition. The aim of this study was to establish correlations between sensory and visual analyses and specific chemical components of some basil cultivars grown in different Italian regions during their entire seasonal cultivation, in order to obtain a comprehensive objective characterisation. Headspace solid phase microextraction coupled with gas chromatography mass spectrometry (HS-SPME-GC-MS) was used to analyse the volatilome of different varieties ofOcimum basilicum L., while colour determination was made using a stand-alone spectrophotometer to quantify colour and monitor its changes in both raw materials and finished products, from the harvesting stage through the production process to the final product. During the sampling of the raw material, several important data were recorded, including the month of harvesting, the geographical area, the number of mowings previously carried out, the abundance of foliage and stems in relation to the total weight, the state of the plant, the presence of inflorescences, as well as other visual and olfactory characteristics. The integration of these data provided a more accurate and complete overview. The application of machine learning to the data collected was used to identify high quality raw materials, define differences at the molecular level between the different varieties analysed and in order to link sensory aspects to chemical composition.