A NEURAL-NETWORK APPROACH FOR THERMAL-PROCESSING APPLICATIONS

被引：38

作者：

SABLANI, SS ^{[1
]}

RAMASWAMY, HS ^{[1
]}

PRASHER, SO ^{[1
]}

机构：

[1] MCGILL UNIV,DEPT FOOD SCI & AGR CHEM,ST ANNE BELLEVUE,PQ H9X 3V9,CANADA

来源：

JOURNAL OF FOOD PROCESSING AND PRESERVATION | 1995年 / 19卷 / 04期

关键词：

D O I：

10.1111/j.1745-4549.1995.tb00295.x

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

The use of a neural network approach in thermal processing applications is presented, A four layer neural network with 3 inputs and 3 outputs was trained using a back-propagation algorithm. A finite difference computer program was used to predict nodal temperature responses of conduction hearing model foods under thermal processing conditions. Equivalent lethality processes were obtained for a range of input variables (can size, food thermal diffusivity and kinetic parameters of quality factors) for sterilization temperatures between 110 and 134 C (at 2 C intervals). The computed optimum conditions and their associated quality changes were used as input variables for training and evaluation of the neural network. The trained network was found to predict optimal sterilization temperatures with an accuracy of +/- 0.5 C and other responses with less than 5% associated errors.

引用

页码：283 / 301

页数：19

共 15 条

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