To study the influence of process parameters on the enzymatic hydrolysis of Antarctic krill, enzymatic hydrolysis experiments under multiple factors were carried out and a process model based on BP neural network was built. In the model, after 83 epochs with 78 training samples, the mean square error (MSE) of model reached a minimum value of 0.002 242, and the correlation coefficient of the model samples reached a maximum value of 0.956 9, which confirmed that the accuracy of the model was optimal. MSE of 0.003 889 and R of 0.985 6 was obtained for nine tested data sets, which indicated that the model could accurately describe and predict the results of enzymatic hydrolysis of Antarctic krill under different process parameters. Finally, the optimal parameters were found by solving the maximum degree of hydrolysis in the model, i.e., enzyme addition amount 4.73%, pH 6.99, enzyme hydrolysis time 201.0 min, and enzyme hydrolysis temperature 54.0 ℃. With the above parameters, the degree of hydrolysis determined by experiment was 41.20%. which was close to the predicted value of 41.36%.
ZHU Lanlan
,
HOU Zhongling
,
CUI Yafei
,
MIAO Junkui
,
LENG Kailiang
. Optimization of enzymatic hydrolysis of Antarctic krill by BP neural network[J]. Food and Fermentation Industries, 2020
, 46(21)
: 121
-126
.
DOI: 10.13995/j.cnki.11-1802/ts.024351
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