Review Paper on Efficient Quality Inspection of Food Products Using Neural Network Classification
With the increasing competiveness in the field of food production growing at a faster pace across the globe, quality is one of the most desirable features that a product should possess. The ability to produce quality as well as safety food is the most prerequisite factors for both, national and international market. This paper explains the recently developed approaches and latest research efforts related to the assessment of the quality of different food products through comparison of multivariate techniques and examine the potential for their deployment. Here, there is a comparison of the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP). The overall results sufficiently demonstrate the fact that the probabilistic neural network (PNN) method has the potential to determine the quality of various food products significantly with high accuracy.
Keywords: Food quality inspection, artificial intelligence techniques, wavelet transform, probabilistic neural network (PNN) approaches
Cite this Article
Syed Sumera Ali, Sayyad Ajij D. Review Paper on Efficient Quality Inspection of Food Products Using Neural Network Classification. Journal of Artificial Intelligence Research & Advances. 2017; 4(1): 1–14p.
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