Classification of surimi gel strength patterns using backpropagation neural network and principal component analysis

Abstract

This paper Proposes two practically and efficiently supervised and unsupervised classifications for surimi gel strength patterns. An supervised learning method, backpropagation neural network with three layers of 17-34-4 neurons for each later, is used. An unsupervised classification method consists of the data dimensionality reduction step via the PCA algorithm and classification step using correlation coefficient similarity measure. In the similarity measure step, each surimi gel strength pattern is compared with the surimi eigen-gel patterns, produced by the PCA step. In this paper, we consider a datum pattern as a datum dimension. The training data sets (12 patterns or 12 data dimensions) of surimi gel strength are collected from 4 experiments having different fixed setting temperature at 35°C, 40°C, 45°C, and 50°C, respectively. Testing data sets (48 patterns) are including original training set and their added Gaussian noise with 1, 3 and 5 points, respectively. From the experiments, two proposed methods can classify all testing data sets into its proper class. © Springer-Verlag Berlin Heidelberg 2006.