干非洲鲶鱼吸附特性的数学和神经网络建模

Q4 Agricultural and Biological Sciences

International Journal of Postharvest Technology and Innovation Pub Date : 2016-01-01 DOI:10.1504/IJPTI.2016.10004627

R. R. Dinrifo

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引用次数: 0

摘要

在水活度0.11 ~ 0.93范围内，采用标准静态失重法测定了非洲鲶鱼(Clarias gariepenus)干鱼片在23℃、30℃和45℃条件下的吸湿等温线。从实验中获得的数据被拟合到四种流行的数学模型[布鲁纳尔，埃米特和泰勒(BET)，奥斯温，亨德森和改进的古根海姆-安德森-德波尔(GAB)]和一组人工神经网络(ANN)模型。改进的GAB模型在预测干鲶鱼水分吸附等温线方面优于其他数学模型[决定系数R2 = 0.96，平均相对误差(MRE) = 6.64%]，而ann模型的预测精度更高(R2 = 0.988，平均相对误差(MRE) = 3.56%)。吸附等温线的知识将在包装的设计，储存稳定性的预测和计算在储存过程中可能发生的水分变化非常有用。

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Mathematical and neural network modelling of the adsorption characteristics of dried African catfish (clarias gariepenus)

Moisture adsorption isotherms of dried fillets of African catfish (Clarias gariepenus) were determined at 23, 30 and 45°C in the range 0.11 to 0.93 of water activity, using the standard gravimetric static method. The data obtained from the experiments were fitted to four popular mathematical models [the Brunauer, Emmett and Teller (BET), Oswin, Henderson and the modified Guggenheim-Anderson-De Boer (GAB)] and to a set of artificial neural network (ANN) models. The modified GAB outperformed the other mathematical models [with coefficient of determination R2 = 0.96, mean relative error (MRE) = 6.64%] but the ANNs were more accurate (with the best giving R2 = 0.988 and MRE = 3.56%) in predicting the moisture adsorption isotherms of the dried catfish. The knowledge of the adsorption isotherms would be very useful in the design of packages, prediction of storage stability and for calculating moisture changes that may occur during storage.

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来源期刊

International Journal of Postharvest Technology and Innovation Agricultural and Biological Sciences-Food Science

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Technology is an increasingly crucial input in the industrialisation and development of nations and communities, particularly in the current era of globalisation, trade liberalisation and emphasis on competitiveness. The shared technologies and innovations of today are giving birth to the radically different agrifood industries and communities of tomorrow. There is mounting evidence that investments in postharvest research and infrastructure yield high rates of return that are comparable and often higher than investments in on-farm production alone.