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[期刊论文]

Study on Diversified Adulteration of Ganoderma Lucidum Spore Oil by RVM and New Clustering Algorithms

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author:

Wang, Wu (Wang, Wu.) [1] | Wang, Jian-Ming (Wang, Jian-Ming.) [2] | Li, Ying (Li, Ying.) [3] | Unfold

Indexed by:

EI PKU CSCD

Abstract:

In recent years, food adulteration of various kinds has become a severe problem in food safety detection. In order to get rid of the limitations of traditional qualitative identification of new food adulteration, Fourier transform near-infrared spectroscopy (FT-NIR) was used to collect the spectrum ranging from 12 400 to 4 000 cm-1. The pure ganoderma lucidum spore oil adulterated with peanut oil, corn oil, coix seed oil, and hogwash oil were investigated in this study, where the ganoderma lucidum spore oil adulterated with hogwash oil was taken as the new category of food adulteration. Then, Multiple Relevance Vector Machine (RVM) classifiers were constructed with calibration samples of the first 4 categories. The prediction samples and ganoderma lucidum spore oil adulterated with hogwash oil were discriminated by the 4 kinds of classifier. In addition, the discriminated results were further verified with new clustering algorithm. Results showed that the discriminant accuracy of the first four categories was close to 93.75% with RVM classifier, but the ganoderma lucidum spore oil adulterated with hogwash oil was mistaken for pure ganoderma lucidum spore oil because of the limitations of model. So a new clustering algorithm based on local density and distance decision graph was applied to verify that. It was found that the cluster centers were 1 when the samples only contained pure ganoderma lucidum spore oil, however, the cluster centers were 2 when the samples mixed with pure ganoderma lucidum spore and adulterated with hogwash oil. The results demonstrated the FT-NIR in combination with RVM classifier and new clustering algorithm could be used for the identification of the adulterant in the pure ganoderma lucidum spore oil and qualitatively identify new category of food adulteration, providing a new method to solve the problem of food diversified adulteration. © 2017, Peking University Press. All right reserved.

Keyword:

Classifiers Clustering algorithms Infrared devices Near infrared spectroscopy Oilseeds Vegetable oils

Community:

  • [ 1 ] [Wang, Wu]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Wang, Wu]Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Fuzhou; 350002, China
  • [ 3 ] [Wang, Jian-Ming]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 4 ] [Wang, Jian-Ming]Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Fuzhou; 350002, China
  • [ 5 ] [Li, Ying]College of Biological Science and Engineering, Fuzhou University, Fuzhou; 350116, China
  • [ 6 ] [Li, Xiang-Hui]Medical Technology and Engineering College, Fujian Medical University, Fuzhou; 350004, China
  • [ 7 ] [Li, Yu-Rong]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 8 ] [Li, Yu-Rong]Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Fuzhou; 350002, China

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Source :

Spectroscopy and Spectral Analysis

ISSN: 1000-0593

Year: 2017

Issue: 4

Volume: 37

Page: 1064-1068

0 . 3 2 6

JCR@2017

0 . 7 0 0

JCR@2023

ESI HC Threshold:226

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

30 Days PV: 2

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