The total acid distribution in pit mud was rapidly assessed using visible (VIS)/near-infrared (NIR) hyperspectral technology. The hyperspectral data of pit mud in the near-infrared and visible wavebands were analyzed by stoichiometry combined with computer technology. Two prediction models, the partial least squares regression (PLSR) and a least squares-support vector machine (LS-SVM) were constructed based on these measurements. The model performances indicated that the optimal model was the Standard Normal Variable Correction-Successive Projection Algorithm-SVM (SNV-SPA-SVM) in the visible region. The coefficient of determination R2cal of the calibration set was 0.998 5, root mean square error of calibration (RMSEC) was 0.004 9 g/kg, and the coefficient of the determination R2pre of the prediction set was 0.999 1. Furthermore, the root mean square error of prediction (RMSEP) was 0.003 8 g/kg and a visual distribution map of the total acid in pit mud was obtained. The results showed that it was feasible to employ hyperspectral technology for rapid and non-destructive detection of the total acid in pit mud, enabling baijiu enterprises to identify problems quickly, adjust processes in a timely manner, prevent pit mud acidification and aging, as well as provide strong technical support for the transformation and upgrading of traditional technology in the Chinese baijiu industry and intelligent online real-time monitoring of pit mud quality.
ZHU Min
,
SUN Ting
,
BAI Zhizhen
,
LUO Huibo
,
TIAN Jianping
,
HUANG Dan
. Distribution of total acid in pit mud based on VIS/NIR hyperspectral technology[J]. Food and Fermentation Industries, 2020
, 46(8)
: 111
-117
.
DOI: 10.13995/j.cnki.11-1802/ts.023006
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