利用粒子群算法优化反向传播人工神经网络模型预测熏肠中4种多环芳烃含量

构建基于粒子群优化(particle swarm optimization,PSO)算法的反向传播人工神经网络(back propagation artificial neural network,BP-ANN)预测模型,对熏肠中4种多环芳烃(polycyclic aromatic hydrocarbons,PAHs)...

Prediction of the Contents of Four Polycyclic Aromatic Hydrocarbons in Smoked Sausage Using Back Propagation Neural Network Optimized by Particle Swarm Optimization Algorithm

A predictive model based on a back propagation artificial neural network (BP-ANN) optimized by particle swarm optimization (PSO) algorithm was developed to predict the contents of four polycyclic aromatic hydrocarbons (PAHs) (benzo(a)pyrene, benzo(a)anthracene, benzo(b)fluoranthene, and chrysene) in smoked sausage. Smoking temperature, smoking time, fat/lean meat ratio and smoked sausage color (a* and b* values) were used as input layer parameters, and the measured contents of four PAHs as output layer parameters. The PSO-BP-ANN model was used to optimize the initial weight and threshold to obtain the best parameters. The results showed that the mean square error (MSE) of the proposed predictive model was 0.018, and the correlation coefficients (R2) for training, validation, test and global data sets were 0.951, 0.929, 0.933 and 0.940 respectively. All these parameters were better that those of the BP-ANN model, indicating that the PSO-BP-ANN model had better accuracy and robustness.