Design of fuzzy clustering system for FTIR spectroscopy of tea

WU Bin; CHEN Bo-wen; WU Xiao-hong; JIA Hong-wen

doi:10.3788/OPE.20172513.0081

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Design of fuzzy clustering system for FTIR spectroscopy of tea

更新时间：2020-08-13

- Design of fuzzy clustering system for FTIR spectroscopy of tea
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 81-86(2017)
- 作者机构：
  
  1. 滁州职业技术学院信息工程系,安徽滁州,239000
  2. 江苏大学京江学院,江苏镇江,212013
  3. 江苏大学电气信息工程学院,江苏镇江,212013
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0081
  CLC： TS272.7
- Received：28 May 2017，
  
  Revised：11 July 2017，
  
  Published：25 November 2017
- 稿件说明：
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武斌, 陈博文, 武小红等. 茶叶傅里叶红外光谱的模糊聚类系统设计[J]. 光学精密工程, 2017,25(10s): 81-86

WU Bin, CHEN Bo-wen, WU Xiao-hong etc. Design of fuzzy clustering system for FTIR spectroscopy of tea[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 81-86
武斌, 陈博文, 武小红等. 茶叶傅里叶红外光谱的模糊聚类系统设计[J]. 光学精密工程, 2017,25(10s): 81-86 DOI： 10.3788/OPE.20172513.0081.

WU Bin, CHEN Bo-wen, WU Xiao-hong etc. Design of fuzzy clustering system for FTIR spectroscopy of tea[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 81-86 DOI： 10.3788/OPE.20172513.0081.

摘要

为了实现茶叶品种的分类，利用傅里叶红外光谱技术和模糊聚类建立了茶叶傅里叶红外光谱的模糊聚类系统。该系统采用模糊C-均值聚类算法（FCM），可能性C-均值聚类算法（PCM）以及可能性模糊C-均值聚类算法（PFCM）进行茶叶傅里叶红外光谱的模糊聚类研究。首先，采集不同品种茶叶的傅里叶红外光谱。接着，茶叶红外光谱数据进行多元散射校正（MSC）预处理。然后，用主成分分析（PCA）和线性判别分析（LDA）对数据进行降维。最后，使用聚类算法对茶叶数据进行聚类分析处理。实验结果表明：随着权重值

的不断增加，FCM算法以及PFCM算法的准确率明显提升，而PCM没有产生明显的改变，聚类准确率始终维持在75.76%。利用傅里叶红外光谱技术，结合MSC，PCA，LDA，FCM或PFCM可设计茶叶品种鉴别的模糊聚类系统，该系统检测速度快且准确率高。

Abstract

In order to classify tea varieties

a fuzzy clustering system was established using Fourier transform infrared spectroscopy (FTIR) technology and its fuzzy clustering algorithms such as fuzzy C-means clustering algorithm (FCM)

possibilistic C-means clustering (PCM) and possibilistic fuzzy C-means clustering(PFCM) were investigated in clustering FTIR spectra of tea. Firstly

FTIR spectra of different varieties of tea samples were collected. Secondly

multiplicative scatter correction (MSC) was applied to preprocess the spectra. Thirdly

dimensionality of FTIR spectra was reduced by Principal Component Analysis (PCA) and linear discriminant analysis (LDA). Finally

the spectral data of tea samples were analyzed by fuzzy clustering algorithms. Experimental results indicate that as the weight value

increases

the clustering accuracies of FCM and PFCM clustering algorithms increases remarkably while that of PCM has no obvious change with its accuracy remaining at 75.76% in the whole process. A fuzzy clustering system can be designed to classify tea varieties by FTIR technology coupled with MSC

PCA

LDA

FCM or PFCM with high detection speed and accuracy.

关键词

Keywords

references

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