ARTIFICIAL NEURAL NETWORK MODEL IN PREDICTING THE QUALITY OF FRESH TOMATO GENOTYPES

Mladenka Pestorić; Jasna Mastilović; Žarko Kevrešan; Lato Pezo; Miona Belović; Svetlana Glogovac; Dubravka Škrobot; Nebojša Ilić; Adam Takač

doi:10.5937/ffr0-29661

Mladenka Pestorić University of Novi Sad, Institute of Food Technology
Jasna Mastilović University of Novi Sad, Institute of Food Technology
Žarko Kevrešan University of Novi Sad, Institute of Food Technology, Novi Sad, Serbia
Lato Pezo University of Belgrade, Institute of General and Physical Chemistry
Miona Belović University of Novi Sad, Institute of Food Technology
Svetlana Glogovac Institute of Field and Vegetable Crops, Laboratory for Biotechnology, Novi Sad, Serbia
Dubravka Škrobot University of Novi Sad, Institute of Food Technology, Novi Sad, Serbia
Nebojša Ilić University of Novi Sad, Institute of Food Technology, Novi Sad, Serbia
Adam Takač Institute of Field and Vegetable Crops, Novi Sad, Serbia

DOI: https://doi.org/10.5937/ffr0-29661

Keywords: fresh tomato quality, sensory evaluation, physicochemical properties, artificial neural network model

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.

Author Biography

Miona Belović, University of Novi Sad, Institute of Food Technology

Naučni saradnik

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