Modelling of hot-air and vacuum drying of persimmon fruit (Diospyros kaki) using computational intelligence methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F20%3A81706" target="_blank" >RIV/60460709:41310/20:81706 - isvavai.cz</a>

Výsledek na webu

<a href="https://agronomy.emu.ee/wp-content/uploads/2020/05/AR2020_Vol18SI2_Khaled.pdf#abstract-7515" target="_blank" >https://agronomy.emu.ee/wp-content/uploads/2020/05/AR2020_Vol18SI2_Khaled.pdf#abstract-7515</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15159/ar.20.095" target="_blank" >10.15159/ar.20.095</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling of hot-air and vacuum drying of persimmon fruit (Diospyros kaki) using computational intelligence methods

Popis výsledku v původním jazyce

The study evaluated the feasibility of applying computational intelligence methods as a non destructive technique in describing the drying behaviour of persimmon fruit using vacuum drying (VD) and hot air drying (HAD) methods and to compare the results with thin layer mathematical models. Drying temperatures were 50, 60 and 70 C. Kinetic models were developed using semi theoretical thin layer models and computational intelligence methods: multi layer feed forward artificial neural network (ANN) and support vector regression (SVR). The statistical indicators of coefficient of determination (R2 ) and root mean square error (RMSE) were used to assess the suitability of the models. The thin layer mathematical models namely page and logarithmic accurately described the drying kinetics of persimmon slices with the highest R2 of 0,9999 and lowest RMSE of 0,0031. ANN showed R2 and RMSE values of 1,0000 and 0,0003, while SVR showed R2 of 0,9999 and RMSE of 0,0004. The validation results indicated good agreeme

Název v anglickém jazyce

Modelling of hot-air and vacuum drying of persimmon fruit (Diospyros kaki) using computational intelligence methods

Popis výsledku anglicky

The study evaluated the feasibility of applying computational intelligence methods as a non destructive technique in describing the drying behaviour of persimmon fruit using vacuum drying (VD) and hot air drying (HAD) methods and to compare the results with thin layer mathematical models. Drying temperatures were 50, 60 and 70 C. Kinetic models were developed using semi theoretical thin layer models and computational intelligence methods: multi layer feed forward artificial neural network (ANN) and support vector regression (SVR). The statistical indicators of coefficient of determination (R2 ) and root mean square error (RMSE) were used to assess the suitability of the models. The thin layer mathematical models namely page and logarithmic accurately described the drying kinetics of persimmon slices with the highest R2 of 0,9999 and lowest RMSE of 0,0031. ANN showed R2 and RMSE values of 1,0000 and 0,0003, while SVR showed R2 of 0,9999 and RMSE of 0,0004. The validation results indicated good agreeme

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

40401 - Agricultural biotechnology and food biotechnology

Projekt

<a href="/cs/project/EF16_027%2F0008366" target="_blank" >EF16_027/0008366: Podpora rozvoje mezinárodních mobilit výzkumných pracovníků ČZU v Praze</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Agronomy Research

ISSN

1406-894X

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

S2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

1323-1335

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85086325507