Bottle-neck type of neural network as a mapping device towards food specifications

A novel methodology is proposed for food specifications associated with the origin of food. The methodology was tested on honey samples collected within the TRACE EU project. The data were sampled in various regions in Europe and analysed for the trace elements content. The sampling sites were characterized by different geological origins, such as limestone, shale, or magmatic. We have chosen 14 elements, B, Na, Mg, A, K, Ca, Mn, Co, Ni, Cu, Zn, Rb, Sr, and Ba, due to their influence on the separation of samples regarding the geology of the sampling sites. A special architecture of an error back-propagation neural network, so called bottle-neck type of neural network was used to project the data into a 2D plane. The data were fed into the 14-nodes input layer and then transferred through the 2-nodes hidden layer (compared to a bottle-neck) to the 14-nodes output layer. The two hidden nodes representing the two coordinates of the projection plane enable us to map the samples used for training of the bottle-neck network. With the knowledge about the classes of individual samples we determine the clusters in the projection plane and consequently obtain the coordinates of the centroid (gravity point) of a particular cluster. The clusters are characterized with an ellipse shape borders spanning the length of up to 3sigma in each dimension. Since the data were classified as regard to the geology, three main clusters were sought: (i) limestone, (ii) shale/mudstone/clay/loess, and (iii) acid-magmatic origin of honey samples. The novel methodology proposed for food specifications was demonstrated on a reduced set of samples, which shows good clustering of all three classes in the projection plane, and on the third class of the original data set.