In order to optimize the subcritical butane extraction process for dehulled tiger nut oil,single factor experiment was taken to determine the levels of the factor,central composite face-centered design(CCF)was used to optimize the subcritical extraction conditions,based on which response surface methodology(RSM)and back propagation artificial neural network(BP-ANN)models were constructed,respectively.The BP-ANN was optimized by particle swarm optimization(PSO),and the optimization results of the RSM model and PSO-BP-ANN model were compared.The optimal extraction conditions optimized by RSM and PSO-BP-ANN models were as follows:solid-liquid ratio(dehulled tiger nut:butane)was 1:10.36 g/mL,incubation time for 45 min,extraction temperature was 30℃,the rolling thickness was 0.5 mm;the solid-liquid ratio was 1:10.67 g/mL,the extraction time was 40.10 min,the extraction temperature was 34℃,and the thickness of the rolled billet was 0.5 mm.Under the optimal con-ditions,the predicted extraction rate of the RSM model was 91.63%,the experimental result was 94.27%,and the relative error was 2.56%.The prediction value of the PSO-BP-ANN model was 95.58%,the validation value was 95.14%,and the relative error was 0.46%.The artificial neural network coupled particle swam optimization(PSO-BP-ANN)was used to optimize the subcritical extraction process of tiger nut oil,which had advantages of high ex-traction rate and small error.This study can provide a reference for the application of subcritical extraction technolo-gy in the efficient production of tiger nut oil.
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