Artificial neural network model in predicting the quality of fresh tomato genotypes

Abstract

Sensory analysis is the best mean to precisely describe the eating quality of fresh foods. However, it is expensive and time-consuming method which cannot be used for measuring quality properties in real time. The aim of this paper was to contribute to the study of the relationship between sensory and instrumental data, and to define a proper model for predicting sensory properties of fresh tomato through the determination of the physicochemical properties. Principal Component Analysis (PCA) was applied to the experimental data to characterize and differentiate among the observed genotypes, explaining 73.52% of the total variance, using the first three principal components. Artificial neural network (ANN) model was used for the prediction of sensory properties based on the results obtained by basic chemical and instrumental determinations. The developed ANN model predicts the sensory properties with high adequacy, with the overall coefficient of determination of 0.859.

Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review

“…Neural Network is a parallel information processing system consisting of a number of neurons (nodes or units), which are arranged in layers and connected by links. Neural Network mimics the highly interconnected structure of the human brain and nerves [13], [20]. According to [21], Neural Network is a computational method that builds multiple processing units based on interconnected connections.…”