Application of artificial neural network on mono- and sesquiterpenes compounds determined by headspace solid-phase microextraction-gas chromatography-mass spectrometry for the Piedmont ricotta cheese traceability.
Mono- and sesquiterpenes were used for the traceability of a typical Piedmont (Italy) mountain ricotta cheese produced by nine mountain farms. For each farm a sample of ricotta cheese was collected every 7 days during mountain grazing and analysed using headspace solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). Obtained results showed the presence of about 20 monoterpenes (above all alpha-pinene, beta-pinene, camphene, p-cymene, beta-myrcene and limonene) and about 15 sesquiterpenes such as alpha-caryophyllene, alpha-copaene and 9-epi-caryophyllene. Despite a wide concentration variability due to the stages of plant development and the pastured area, there are not able differences between the ricotta cheeses analysed so it is possible with the artificial neural network (ANN) technique to distinguish between different mountain farms.
Giuseppe
Zeppa
Z
University of Turin, Agricultural Faculty, Department of Exploitation and Protection of the Agricultural and Forestry Resources, Food Technology Sector, Via L. da Vinci 44, 10095 Grugliasco (TO), Italy. giuseppe.zeppa@unito.it
Manuela
Giordano
G
Marta
Bertolino
B
Vincenzo
Gerbi
G
D002611:Cheese / Q000032:analysis*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D016571:Neural Networks, Computer; D013729:Terpenes / Q000032:analysis*
D013729:Terpenes