Document Detail


Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools.
MedLine Citation:
PMID:  22868133     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.
Authors:
Quansheng Chen; Jiao Ding; Jianrong Cai; Jiewen Zhao
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Publication Detail:
Type:  Journal Article     Date:  2012-05-11
Journal Detail:
Title:  Food chemistry     Volume:  135     ISSN:  0308-8146     ISO Abbreviation:  Food Chem     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-08-07     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7702639     Medline TA:  Food Chem     Country:  England    
Other Details:
Languages:  eng     Pagination:  590-5     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 Elsevier Ltd. All rights reserved.
Affiliation:
School of Food & Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, PR China.
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