噪声鉴别C均值聚类的滁菊花茶品质等级鉴别研究  

Discrimination of Chuzhou Chrysanthemum Tea Grades Using Noise Discriminant C-Means Clustering

作　　者：武斌[1] 谢晨傲 陈勇[2] 武小红[2] 贾红雯[1] WU Bin;XIE Chen-ao;CHEN Yong;WU Xiao-hong;JIA Hong-wen(School of Information Engineering,Chuzhou Polytechnic,Chuzhou 239000,China;School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]滁州职业技术学院信息工程学院,安徽滁州239000 [2]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《光谱学与光谱分析》2024年第8期2202-2207,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(31471413);安徽省高校自然科学研究重大项目(2022AH040333)资助。

摘　　要：近红外光谱检测技术可以通过探测近红外区域的光谱特征,反映所测样品内部有机物化学成分和结构信息。在分析物质成分时,近红外光谱通常会涉及到大量的波长数据,因此其维数往往比较高。同时,光谱会出现重叠和冗余等现象,会影响模型的性能。提出一种噪声鉴别C均值聚类(NDCM)算法。NDCM将一种快速广义噪声聚类(FGNC)和模糊线性判别分析(FLDA)相结合,可实现模糊聚类过程中进行数据鉴别信息的提取和数据空间维度的压缩,以达到更高的聚类准确率。对滁菊花茶近红外光谱数据进行模糊C均值聚类(FCM)得到的模糊隶属度和聚类中心作为噪声鉴别C均值聚类(NDCM)的初始模糊隶属度和初始聚类中心,使NDCM具有聚类速度快,准确率高等优点。FCM算法对光谱噪声数据敏感,而NDCM算法在处理含噪声的光谱数据时能够表现出较好的性能。该研究选取特级滁菊、一级滁菊、二级滁菊三种品质等级的滁菊花茶作为实验样本,共计240个样本。实验使用便携式近红外光谱仪(NIR-M-F1-C)采集滁菊花茶的近红外光谱数据。用Savitzky-Golay滤波和多元散射校正(MSC)对滁菊花茶近红外光谱进行预处理,以减少光谱中掺杂的噪声和重叠信息。通过主成分分析(PCA)对采集到的400维光谱数据进行维度压缩降至6维。该研究使用线性判别分析(LDA)提取滁菊花茶光谱数据中的鉴别信息,并将数据空间维度进一步转换为2维。分别用FCM,FGNC和NDCM三种算法对处理后的数据进行聚类分析,以实现对滁菊花茶的准确分类。实验结果显示:当权重指数m=2.5时,FCM,FGNC,NDCM的聚类准确率分别为92.42%,98.48%,100%。NDCM聚类时间略长于FGNC。FCM算法需要进行27次迭代才能收敛,而FGNC算法和NDCM算法分别只需要13次和10次迭代就能达到收敛。采用近红外光谱技术结合MSC、Savitzky-Golay滤波、PCA、LDA和NDCM算法,建立了一种精准鉴别滁�Near-infrared(NIR)spectroscopy detection technology can reflect the measured sample's organic chemical composition and structural information by detecting the spectral features in the NIR region.During the material composition analysis,NIR spectroscopy often involves a significant amount of wavelength data,resulting in relatively high data dimensions.Furthermore,spectra are susceptible to phenomena such as overlap and redundancy,which impact the model's performance.Therefore,we proposed a noise discriminant C-means clustering(NDCM)algorithm that combined fast generalized noise clustering(FGNC)and fuzzy linear discrimination analysis(FLDA).NDCM can realize the extraction of data identification information and data space compression in the fuzzy clustering process,which can achieve higher clustering accuracy.The fuzzy membership degree and the cluster centers obtained by fuzzy C-means clustering(FCM)on the near-infrared spectral data of Chuzhou chrysanthemum tea are used as the initial fuzzy membership degree and initial clustering centers of NDCM,respectively,so that NDCM has the advantages of fast clustering speed and high accuracy.The FCM algorithm is sensitive to noisy data,while the NDCM algorithm can perform better when dealing with noisy data in spectra.In this study,240 samples of Chuzhou chrysanthemum tea with three quality grades,namely special grade,first grade and second grade,were selected as experimental samples.A portable NIR spectrometer(NIR-M-F1-C)was used to collect the NIR spectra of Chuzhou chrysanthemum tea,and they are the 400-dimensional data.At first,the NIR spectra were pretreated with Savitzky-Golay filtering and multivariate scattering correction(MSC)to reduce spectral scattering and noise.Secondly,the dimensionality of the spectral data was reduced by principal component analysis(PCA),and the dimensionality of the data after PCA reduction was 6.Next,linear discriminant analysis(LDA)was applied to extract the discriminant information in the spectral data of Chuzhou Chrysanthemum tea and f

关 键 词：噪声鉴别C均值聚类 近红外光谱 无损检测 线性判别分析 

分 类 号：O657.3[理学—分析化学]