期刊
ANALYTICA CHIMICA ACTA
卷 841, 期 -, 页码 68-76出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2014.06.001
关键词
Instrumental intelligent test; Multi-sensors; Data fusion; Human panel test; Rice wine
资金
- National Natural Science Foundation of China [31271875]
- Program Sponsored for Scientific Innovation Research of College Graduate in Jiangsu Province [CXZZ12_0702]
Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (> 90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers. (C) 2014 Elsevier B. V. All rights reserved.
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