LAUSR

Abstract Although hazelnuts are mostly consumed after toasting and mixed with other ingredients, for manufactures it is important to have efficient quality control tests on the raw product that they purchase from farmers and suppliers. This study explores the possibility to predict sensory quality of raw hazelnuts, classified according to industrial sensory evaluation, using volatilome analysis through Proton Transfer Reaction Mass Spectrometry (PTR-MS) rapid fingerprinting. Firstly, the link between volatile markers for different visual and sensory defects was investigated. Uncompliant hazelnuts showed higher concentrations for a larger number of volatile organic compounds (VOCs) than compliant samples, including some key hazelnuts odorants like 5-methyl-4-heptanone, 5-propyldihydro-2(3H)-furanone, octanal, 2,4-nonadienal and hexanal. Secondly, by mixing defective and good quality hazelnuts, the method sensitivity in recognizing defects percentage was determined. For about 13% of the detected mass peaks, the method was able to discriminate samples containing 20% of hazelnuts with unacceptable quality from good quality samples. Finally, unsupervised data clustering of VOCs fingerprints obtained with different precursor ions (H3O+, NO+ and O2+) provided a correct classification rate higher than 90% for all ions. The applied methodology is suitable to support sensory quality control programs of raw hazelnuts in confectionary industries.