基于响应面和神经网络模型优化鱼油脱色工艺

Optimization of Fish Oil Decolorization Process Based on Response Surface Methodology and Artificial Neural Network Models

为优化鱼油的脱色工艺,首先通过单因素试验确定脱色效果最佳的固体吸附剂,其次探究脱色温度、固体吸附剂添加量、脱色时间对鱼油脱色率的影响。在此基础上,采用Box-Behnken(BB)试验设计对鱼油脱色条件进行优化,并对BB试验结果进行响应面法(RSM)和人工神经网络(ANN)分析。结果表明:活性白土的脱色效果最佳,随着脱色温度、固体吸附剂添加量、脱色时间的增加,脱色率呈先上升后下降的趋势;RSM和ANN模型的相关系数r、决定系数R^(2)、均方根误差RMSE、均方差MSE值分别为0.9647,0.9307,1.1000,1.2100和0.9927,0.9855,0.4952,0.2452。相较于与RSM模型,ANN模型拟合程度更高,实测值与预测值之间误差更小,更适合作为鱼油脱色率的预测模型。本试验选用RSM和ANN模型共同优化鱼油脱色工艺。通过RSM模型选取的最佳脱色条件是:脱色温度93.79℃,固体吸附剂添加量4.80%,脱色时间9.69min。将上述条件带入ANN模型,获得鱼油的最大脱色率为99.53%。说明RSM-ANN模型具有较强的准确性和适用性。

In this study,the best solid adsorbent for decolorization was first determined by single-factor test,and then the effects of decolorization temperature,solid adsorbent addition and decolorization time on the decolorization rate of fish oil were investigated.On this basis,the Box-Behnken(BB)experimental design was used to optimize the decolorization conditions of fish oil,and the results of the BB test were analyzed by response surface methodology(RSM)and artificial neural network(ANN).The results showed that the activated white clay had the best decolorization effect,and the decolorization rate tended to increase and then decrease with the increase of decolorization temperature,solid adsorbent addition and decolorization time;the correlation coefficient r,determination coefficient R^(2),root mean square error RMSE and mean square error MSE values of RSM and ANN models were 0.9647,0.9307,1.1000,1.2100 and compared with the RSM model,the ANN model fits better and has less error between the measured and predicted values,so it is more suitable as a prediction model for the decolorization rate of fish oil.Therefore,the RSM and ANN models were selected to jointly optimize the fish oil decolorization process.Firstly,the optimal decolorization conditions were selected by the RSM model,with decolorization temperature of 93.79℃,solid adsorbent addition of 4.80%,and decolorization time of 9.69 min.Subsequently,the above conditions were brought into the ANN model,and the maximum decolorization rate of fish oil was finally obtained as 99.53%.The results of this study showed that the RSM-ANN model has strong accuracy and applicability,which provides a new idea for process optimization in food research.