Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry

Abstract

Weighted Voting Superposition (WeVoS) is a novel summarization algorithm that may be applied to the results of an ensemble of topology preserving maps in order to identify the lowest topographical error in a map and thereby, to calculate the best possible visualization of the internal structure of its datasets. It is applied in this research to the food industry field that is studying the olfactory properties of Spanish dry-cured ham. The datasets used for the analysis are taken from the readings of an electronic nose, a device that can be used to recognize the sensory smellprints of Spanish dry-cured ham samples. They are then automatically analyzed using the previously mentioned techniques, in order to detect those batches with an anomalous smell (acidity, rancidity and different type of taints).. The Weighted Voting Superposition of ensembles of Self-Organising Maps (SOMs) is used here for visualization purposes, and is compared with the simple version of the SOM. The results clearly demonstrate how the WeVoS-SOM outperforms the simple SOM method.