classification of two dimensional gc-ms based data applying kohenen self organizing maps: classification based on geographical origin of saffron samples

Abstract: crocus sativus or saffron is one of the most valuable indigenous herbs in iran and is known as the most expensive spice in red gold. classification of different saffron samples based on their geographical distribution using gas chromatography-mass spectroscopy was used to determine the metabolites [1].in this study, 86 saffron samples were collected from 7 regions in khorasan razavi province including kashmar, taybad, torbat-e jam, torbat heydariyeh, zaveh, neishabour and rashtkhar cities to show if they can be discriminated based on their volatile metabolites. after coding saffron samples, these samples were placed in a sealed container in a dark place. the selection of samples in this experiment is completely random and the number of samples varies from city to city. for example, the number of samples was kashmar 9, taybad 17, torbat jam 15, torbat heydariyeh 10, zaveh, 13 neishabour 10 and rashtkhar 12 samples. saffron extract will be extracted using diethyl ether and injected into gc-ms. the data is then analyzed in the matlab environment. area data were analyzed applying different preprocessing methods on data in matlab environment and kohenen toolbox [2]. findings show that saffron in different cities of iran, in spite of their many similarities ,have differences, and these differences cause the separation of saffron samples of different classes from each other. this means that in these samples the level of volatile metabolites in different geographical regions are different. however, saffron of some regions were similar but some of them were different. metabolites which they are responsible for discrimination of these saffron were determined. these differences can be achieved using chemometrics methods and the relationship between data [3].