Acidity value is an important index for quality evaluation of Daqu, a method based on hyperspectral imaging technology for rapid detection of the acidity value during the fermentation of Daqu was proposed. Hyperspectral images of Daqu samples and average spectrum of regions of interest (ROIs) were collected, original spectrum was pretreated by three methods including multivariate scattering correction (MSC), standard normal variable correction (SNV) and Savitzky-Golay first-order derivative (SGFD). Optimal characteristic wavelengths were selected by successive projection algorithm (SPA), then partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) models were established. The results showed that the LS-SVM model built with 8 wavelengths performed better in prediction set, with determination coefficient of prediction (R2P) is 0.913 2 and root mean square error of prediction (RMSEP) is 0.008 1. By inputting the spectrum of each pixel into the optimal SNV+ SPA + LS-SVM model, the visualization of the distribution map of acidity value in Daqu was obtained, the visualization of the acidity value and its distribution in different fermentation periods was realized.
SUN Ting
,
HU Xinjun
,
TIAN Jianping
,
WANG Kaizhu
,
HUANG Dan
,
PENG Xinghui
. Prediction and visualization of Daqu acidity based on hyperspectral imaging technology[J]. Food and Fermentation Industries, 2020
, 46(17)
: 226
-231
.
DOI: 10.13995/j.cnki.11-1802/ts.024080
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