Journal
JOURNAL OF FOOD ENGINEERING
Volume 215, Issue -, Pages 78-83Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2017.07.026
Keywords
Multispectral imaging; Tomato paste; Sucrose; Rapid detection; Chemometric methods
Categories
Funding
- National Key Research and Development Plan of China [2016YFD0401104]
- National Natural Science Foundation of China [31401544]
- Key Science & Technology Specific Projects of Anhui Province [15czz03117]
- Hefei University of Technology [407-037019]
- Fundamental Research Funds for the Central Universities [JZ2016HGTB0712, 2015HGCT0001]
Ask authors/readers for more resources
Confirmation of the authenticity of tomato paste is an increasing challenge for food processors and regulatory authorities. This study focuses on the rapid qualitative and quantitative detection of sucrose adulteration in tomato paste using multispectral imaging (405-970 nm) combined with chemometric methods. Partial least squares (PLS), least squares-support vector machines (LS-SVM), and back propagation neural network (BPNN) were used to develop quantitative models. Compared with PLS and BPNN, LS-SVM considerably improved the prediction performance with coefficient of determination in prediction (4) of 0.936 and 0.966, the root-mean-square error of prediction (RMSEP) of 0.521% and 0.445%, and residual predictive deviation (R-P(2)) of 5.014 and 5.865 for batch 1 and batch 2 of tomato paste, respectively. Besides, multispectral imaging was feasible to detect sucrose adulteration in tomato paste at very low proportions (1%) with no misclassification using chemometric methods. It was concluded that multispectral imaging has an excellent potential for rapid determination of sucrose adulteration in tomato paste. (C) 2017 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available