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Food and Feed Research
2018, vol. 45, br. 2, str. 193-201
Detoksifikacija koekstrudata lanenog semena i suncokretove sačme - predviđanje procesa
Čolović Dušica S.a, Pezo Lato L.b ORCID, Čolović Radmilo R.a ORCID, Banjac Vojislav V.a, Đuragić Olivera M.a ORCID, Kavallieratos Nickolas G.c, Spasevski Nedeljka J.a
aInstitut za prehrambene tehnologije, Novi Sad, Srbija
bInstitut za opštu i fizičku hemiju, Beograd, Srbija
cAgricultural University of Athens, Athens, Greece

e-adresa: dusica.colovic@fins.uns.ac.rs
Projekat:
Istraživanje savremenih biotehnoloških postupaka u proizvodnji hrane za životinje u cilju povećanja konkurentnosti, kvaliteta i bezbednosti hrane (MPNTR - 46012)
COST CA 15118 project

Sažetak
Već dugi niz godina laneno seme privlači veliku pažnju u ishrani životinja zbog svog izuzetno povoljnog masnokiselinskog sastava i visokog sadržaja α-linelonske kiseline. Ipak, njegova primena u je ograničena zbog prisustva antinutritivnih materija - cijanogenih glikozida. Do sada je u literaturi obrađena nekolicina postupaka za detoksifikaciju lanenog semena, a ekstrudiranje se ističe kao najefikasnije među njima. U prikazanom eksperimentu, ispitivana je primena veštačkih neuronskih mreža sa ciljem da se predvidi uticaj procesa na razaranje cijanogenih glikozida tokom postupka ekstrudiranja koektrudata lanenog semena i suncokretove sačme. Kao indikator količine prisutnih cijanogenih glikozida u proizvodu određivan je sadržaj cijanovodonične kiseline (HCN), u skladu sa AOAS metodom. Ekstrudiranje materijala izvedeno je na laboratorijskom jednopužnom ekstruderu. Funkcionisanje modela veštačke neuronske mreže upoređeno je sa eksperimentalnim rezultatima kako bi se razvio brz i tačan metod za predviđanje sadržaja HCN u ko-ekstrudatu. Kako su eksteprimentalni rezultati pokazali, najviši sadržaj HCN(126 mg/kg), izmeren je pri najnižem sadržaju vlage (7%) i najmanjoj brzini obrtanja puža eksturdera (240 obrtaja/ minutu). Sa porastom sadržaja vlage i temperature tokom ekstrudiranja, sadržaj HCN je naglo opadao. Model veštačkih neuronskih mreža pokazao je visoku tačnost predviđanja (r2> 0.999), što ukazuje na to da bi model mogao vrlo lako da bude primenjen i u praksi.
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O članku

jezik rada: engleski
vrsta rada: originalan članak
DOI: 10.5937/FFR1802193C
objavljen u SCIndeksu: 01.02.2019.
Creative Commons License 4.0

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