Estimation of Storage Time of Yogurt with Artificial Neural Network Modeling

Machine vision system used for color analysis of yogurt. MVS = machine vision system; CAS = color analysis software; CCD charge-coupled device camera; ANN = artificial neural network.

Detail of the function of a neuron. The inputs (y) into a neuron are multiplied by their corresponding connection weights (w) and summed. This sum is then transformed through the sigmoid function to produce a single output that may be passed on to other neurons.

In the developed model, ∼73% of the parameters were used for training and ∼27% for validation.

Selected neural network structure: 16 inputs were used for the artificial neural network (ANN): 12 inputs corresponding to color blocks resulting from machine vision system analysis; 1 input corresponding to pH values; 3 inputs corresponding to total aerobic count (TAC), yeast and mold counts (YMC), and coliform counts (CC) resulting from microbial analysis. For the ANN used to predict the shelf life, 1 output was used and the output value was the “shelf-life”.

Correlation of experimental vs. neural network values of time with training data set using the optimal network, with 1 hidden layer, 5 neurons per hidden layer, and a training data set of 22 cases.

Correlation of experimental vs. neural network values of time with validation data set using the optimal network, with 1 hidden layer, 5 neurons per hidden layer, and a validation data set of 8 cases.