Open AccessARTIFICIAL NEURAL NETWORK MODEL IN PREDICTING THE QUALITY OF FRESH TOMATO GENOTYPES
Author(s) -
Mladenka Pestorić,
Mastilović Jasna,
Žarko Kevrešan,
Milada Pezo,
Miona Belović,
Svetlana Glogovac,
Dubravka Škrobot,
Nebojša Ilić,
Takač Adam
Publication year - 2021
Publication title -
food and feed research
Language(s) - English
Resource type - Journals
eISSN - 2217-5660
pISSN - 2217-5369
DOI - 10.5937/ffr0-29661
Subject(s) - principal component analysis , sensory system , artificial neural network , biological system , pattern recognition (psychology) , artificial intelligence , variance (accounting) , quality (philosophy) , mathematics , statistics , computer science , machine learning , biology , philosophy , accounting , epistemology , neuroscience , business
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.