An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

被引：25

作者：

Di Scala, Karina ^{[1
,2
]}

Meschino, Gustavo ^{[3
]}

Vega-Galvez, Antonio ^{[4
]}

Lemus-Mondaca, Roberto ^{[4
]}

Roura, Sara ^{[1
,2
]}

Mascheroni, Rodolfo ^{[2
,5
,6
]}

机构：

[1] UNMdP, Food Engn Res Grp, Mar Del Plata, Buenos Aires, Argentina

[2] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina

[3] UNMdP, Lab Bioengn, Mar Del Plata, Buenos Aires, Argentina

[4] ULS, Dept Food Engn, La Serena, Chile

[5] UNLP, CONICET, CCT, CIDCA, La Plata, Buenos Aires, Argentina

[6] UNLP, Fac Ingn, MODIAL, La Plata, Buenos Aires, Argentina

来源：

FOOD SCIENCE AND TECHNOLOGY | 2013年 / 33卷 / 03期

关键词：

artificial neural networks; quality attributes; genetic algorithm; process optimization; dried apple; GENETIC ALGORITHM; MOISTURE-CONTENT; DRYING PROCESS; TEMPERATURE; KINETICS;

D O I：

10.1590/S0101-20612013005000064

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 degrees C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 degrees C and 1.0 m/s using genetic algorithm.

引用

页码：411 / 416

页数：6

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