Journal
FOOD CHEMISTRY
Volume 135, Issue 2, Pages 590-595Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2012.05.011
Keywords
Vinegar; Total acid content; Near infrared spectroscopy; Variables selection; Nonlinear regression
Funding
- China Postdoctoral Science Foundation [201003559]
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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 0.2486 g/100 mL, 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. (C) 2012 Elsevier Ltd. All rights reserved.
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