The moisture content in solid fermentation is a key factor for different fermentation stages of Daqu. In order to detect the moisture content and distribution in the fermentation process rapidly and quantitatively, the hyperspectral imaging technology was applied and the back propagation neural network(BPNN) data model was established. According to the hyperspectral data of Daqu fermentation, principle component analysis (PCA) and experimental methods were firstly used to extract the PC1, PC2, PC3, 1 200 and 1 450nm band images corresponding to the water characteristic spectrum during Daqu fermentation, the GLCM algorithm was then used to extract the water texture features, PLSR, BPNN and SVR were finally used to model and predict the water content of Daqu fermentation according to the extracted image texture features, and then select the best prediction model. The results showed that the effect of BPNN and 1 450 nm spectral image texture features on water content modeling and prediction was the best. The training set determination coefficient (R2) and root mean square error (RMSE) were 0.826 9 and 0.033 5, respectively. The prediction set R2 and RMSE were 0.848 4 and 0.028 7, respectively. The results show that the texture information of hyperspectral characteristic spectrum can be used to predict the water content, which provides a theoretical basis for the detection of water content in Daqu fermentation process.
YE Jianqiu
,
HUANG Danping
,
TIAN Jianping
,
HUANG Dan
,
LUO Huibo
,
WANG Xin
,
ZHANG Li
. Detection of water content in Daqu during fermentation usinghyperspectral image technology[J]. Food and Fermentation Industries, 2020
, 46(9)
: 250
-254
.
DOI: 10.13995/j.cnki.11-1802/ts.023368
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