茶叶傅里叶红外光谱的模糊聚类系统设计

武斌; 陈博文; 武小红; 贾红雯

doi:10.3788/OPE.20172513.0081

您当前的位置：

首页 >

文章列表页 >

茶叶傅里叶红外光谱的模糊聚类系统设计

现代应用光学 | 更新时间：2020-08-13

- 茶叶傅里叶红外光谱的模糊聚类系统设计
- Design of fuzzy clustering system for FTIR spectroscopy of tea
- 光学精密工程 2017年25卷第10s期页码：81-86
- 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：
  
  国家自然科学基金资助项目（No.31471413）();安徽省高等教育振兴计划人才项目“高校优秀青年人才支持计划”资助项目（皖教秘人[2014]181号）()
- DOI：10.3788/OPE.20172513.0081
  中图分类号： TS272.7
- 收稿日期：2017-05-28，
  
  修回日期：2017-07-11，
  
  纸质出版日期：2017-11-25
- 稿件说明：
移动端阅览
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

何芳,王榕,于强,等. 加权空谱局部保持投影的高光谱图像特征提取[J]. 光学精密工程,2017,25(1):263-273. HE F, WANG R, YU Q, et al.. Feature extraction of hyperspectral images of weighted spatial and spectral locality preserving projection (WSSLPP)[J]. Opt. Precision Eng., 2017,25(1):263-273. (in Chinese)

李玉,徐艳,赵雪梅,等. 利用高斯混合模型的多光谱图像模糊聚类分割[J]. 光学精密工程,2017,25(2):509-518. LI Y, XU Y, ZHAO X M, et al.. Multispectral image segmentation by fuzzy clustering algorithm used gaussian mixture model[J]. Opt. Precision Eng., 2017,25(2):509-518. (in Chinese)

刘燕德,靳昙昙,王海阳. 基于拉曼光谱的三组分食用调和油快速定量检测[J]. 光学精密工程,2015,23(9):2490-2496. LIU Y D, JIN T T, WANG H Y. Rapid quantitative determination of components in ternary blended edible oil based on Raman spectroscopy[J]. Opt. Precision Eng., 2015,23(9):2490-2496.(in Chinese)

WU X H, WU B, SUN J, et al.. Discrimination of apples using near infrared spectroscopy and sorting discriminant analysis[J]. International Journal of Food Properties, 2016, 19(5):1016-1028.

李晓丽,张裕莹,何勇. 基于中红外光谱技术检测茶叶中非法添加滑石粉的研究[J]. 光谱学与光谱分析,2017,37(4):1081-1085. LI X L, ZHANG Y Y, HE Y. Study on detection of talcum powder in green tea based on Fourier transform infrared (FTIR) transmission spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017,37(4):1081-1085.(in Chinese)

ZHUANG X G, WANG L L, CHEN Q, et al.. Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools[J]. Science China-Technological Sciences, 2017, 60(1):84-90. (in Chinese)

武斌,崔艳海,武小红,等. 一种广义噪声聚类的红外光谱茶叶品种鉴别研究[J]. 光谱学与光谱分析,2016, 36(7):2094-2097. WU B CUI Y H, WU X H, et al.. Discrimination of tea varieties by using infrared spectroscopy with a novel generalized noise clustering[J]. Spectroscopy and Spectral Analysis, 2016, 36(7):2094-2097.(in Chinese)

SHI W M, LIU Y, KONG W et al.. Tea classification by near infrared spectroscopy with projection discriminant analysis and gene expression programming[J]. Analytical Letters, 2015, 48(18):2833-2842.

ZHANG Y, DONG Y, XIANG BR et al.. Feasibility research on rapid detection of prochloraz in green tea soft drink by near-infrared spectroscopy[J]. Food Analytical methods,2015,8(2):343-351.

ZHAO Y M, ZHANG M M. Research on matching method for case retrieval process in CBR based on FCM[J]. Procedia Engineering, 2017, 174:267-274.

TUDU B, GHOSH S, BAG A K. Incremental FCM technique for black tea quality evaluation using an electronic nose[J]. Fuzzy Information and Engineering, 2015, 7(3):275-289.

BIJU V G, MYTHILI P. Fuzzy clustering algorithms for cDNA microarray image spots segmentation[J]. Procedia Computer Science, 2015, 46:417-424.

KRISHNAPURAM R, KELLER J. A possibilistic approach to clustering[J]. IEEE Transactions on Fuzzy Systems, 1993, 1(2):98-110.

PAL N R, PAL K, BEZDEK J C. A possibilistic fuzzy c-means clustering algorithm[J]. IEEE Transactions on Fuzzy Systems, 2005, 13(4):517-530.

KRISHNAPURAM R, KELLER J. The possibilistic c-means algorithm:insights and recommendations[J]. IEEE Transactions on Fuzzy Systems, 1996, 4(3):385-393.

浏览量

487

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

散斑运动模糊复原的直线电机动子位置检测

应用改进的粒子群优化模糊聚类实现点云数据的区域分割

融合对比度和分布性的图像显著性区域检测

用于目标识别的PCA-SC形状匹配算法

主成分分析在光全散射特征波长选择中的应用