Academic Journal
Veštačke neuronske mreže za predviđanje kvaliteta različitih genotipova paradajza ; Artificial neural network model in predicting the quality of fresh tomato genotypes
| العنوان: | Veštačke neuronske mreže za predviđanje kvaliteta različitih genotipova paradajza ; Artificial neural network model in predicting the quality of fresh tomato genotypes |
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| المؤلفون: | Pestorić, Mladenka, Mastilović, Jasna S., Kevrešan, Žarko, Pezo, Lato, Belović, Miona, Glogovac, Svetlana K., Škrobot, Dubravka, Ilić, Nebojša, Takač, Adam J. |
| المصدر: | Food and Feed Research |
| بيانات النشر: | Univerzitet u Novom Sadu - Naučni institut za prehrambene tehnologije, Novi Sad |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | senzorska ocena, model veštačkih neuronskih mreža, kvalitet svežeg paradajza, fizičko-hemijska svojstva, sensory evaluation, physicochemical properties, fresh tomato quality, artificial neural network model |
| الوصف: | Senzorska analiza predstavlja najbolje sredstvo za precizno opisivanje kvaliteta svežih namirnica. Međutim, to je skupa i dugotrajna metoda koja se ne može koristiti za merenje pokazatelja kvaliteta u realnom vremenu. Cilj ovog rada bio je da doprinese proučavanju odnosa između podataka dobijenih primenom senzorske analize i instrumentalnih metoda i da definiše odgovarajući model za predviđanje senzorskih svojstava svežeg paradajza pomoću određivanja fizičko-hemijskih svojstava. Analiza glavnih komponenti (RSA) primenjena je na eksperimentalne podatke da bi se okarakterisali i diferencirali posmatrani genotipovi, objašnjavajući 73,52% od ukupne varijanse, koristeći prve tri glavne komponente. Model veštačke neuronske mreže (ANN) korišćen je za predviđanje senzorskih svojstava na osnovu rezultata dobijenih osnovnim hemijskim i instrumentalnim određivanjima. Razvijeni ANN model predviđa senzorska svojstva sa visokom adekvatnošću, sa ukupnim koeficijentom determinacije od 0,859. ; 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| تدمد: | 2217-5369 |
| Relation: | info:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/46001/RS//; info:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/31030/RS//; info:eu-repo/grantAgreement/MESTD/inst-2020/200222/RS//; info:eu-repo/grantAgreement/MESTD/inst-2020/200032/RS//; https://riofh.iofh.bg.ac.rs/handle/123456789/788; http://riofh.iofh.bg.ac.rs/bitstream/id/362/785.pdf; conv_138; 2-s2.0-85142924275 |
| DOI: | 10.5937/ffr48-29661 |
| الاتاحة: | https://riofh.iofh.bg.ac.rs/handle/123456789/788 https://doi.org/10.5937/ffr48-29661 http://riofh.iofh.bg.ac.rs/bitstream/id/362/785.pdf |
| Rights: | openAccess ; https://creativecommons.org/licenses/by/4.0/ ; BY |
| رقم الانضمام: | edsbas.D35CBB8C |
| قاعدة البيانات: | BASE |
| تدمد: | 22175369 |
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| DOI: | 10.5937/ffr48-29661 |