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2021, vol. 48, br. 1, str. 9-21
Veštačke neuronske mreže za predviđanje kvaliteta različitih genotipova paradajza
aInstitut za prehrambene tehnologije, Novi Sad, Srbija
bInstitut za opštu i fizičku hemiju, Beograd, Srbija
cNaučni institut za ratarstvo i povrtarstvo, Novi Sad, Srbija

e-adresamiona.belovic@fins.uns.ac.rs
Projekat:
Razvoj i primena novih i tradicionalnih tehnologija u proizvodnji konkurentnih prehrambenih proizvoda sa dodatom vrednošću za evropsko i svetsko tržište - Stvorimo bogatstvo iz bogatstva Srbije (MPNTR - 46001)
Stvaranje sorata i hibrida povrća za gajenje na otvorenom polju i u zaštićenom prostoru (MPNTR - 31030)
Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije (institucija: Institut za prehrambene tehnologije, Novi Sad) (MPNTR - 451-03-68/2020-14/200222)
Ministarstvo prosvete, nauke i tehnološkog razvoja Republike Srbije (institucija: Naučni institut za ratarstvo i povrtarstvo, Novi Sad) (MPNTR - 451-03-68/2020-14/200032)

Ključne reči: kvalitet svežeg paradajza; senzorska ocena; fizičko-hemijska svojstva; model veštačkih neuronskih mreža
Sažetak
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.
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O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.5937/ffr48-29661
primljen: 02.12.2020.
revidiran: 02.02.2021.
prihvaćen: 05.02.2021.
objavljen onlajn: 15.02.2021.
objavljen u SCIndeksu: 10.07.2021.
metod recenzije: jednostruko anoniman
Creative Commons License 4.0

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