光谱学与光谱分析
光譜學與光譜分析
광보학여광보분석
SPECTROSCOPY AND SPECTRAL ANALYSIS
2014年
4期
972-976
,共5页
近红外光谱%主成分分析%最小二乘支持向量机%聚丙烯酰胺
近紅外光譜%主成分分析%最小二乘支持嚮量機%聚丙烯酰胺
근홍외광보%주성분분석%최소이승지지향량궤%취병희선알
Near infrared spectroscopy%Principal component analysis%Least square support vector machine%Polyacrylamide
提出了一种基于近红外光谱分析技术和最小二乘支持向量机的鉴别方法，能够快速、无损鉴别聚丙烯酰胺的三种类型。获取非离子，阴离子和阳离子等三种类型的聚丙烯酰胺样本的近红外漫反射光谱，用主成分分析方法对样本光谱数据进行降维，并提取主成分。基于前三个主成分对三种类型的聚丙烯酰胺样本进行聚类分析，并将主成分作为最小二乘支持向量机的输入。通过基于网格搜索的交叉验证方式优化最小二乘支持向量机的参数和作为其输入的主成分个数。每种类型聚丙烯酰胺各采集60个样本，共采集180个样本，每种类型样本随机选取45个样本，共135样本作为训练样本集，剩余45个样本作为测试集。为了验证该方法能否鉴别掺假样本，制备了掺入不同比例非离子聚丙烯酰胺的5个阴离子和5个阳离子聚丙烯酰胺样本。采用基于训练样本集交叉验证预测误差的F统计显著性检验方法来确定样本的鉴别结果误差阈值。结果表明，预测测试集时，准确率为100％。预测10个混和样本时，所有混合样本都被准确识别出。说明该方法能快速无损鉴别不同类型的聚丙烯酰胺并且具有掺假鉴别能力，为聚丙烯酰胺类型的快速鉴别提供了一种新方法。
提齣瞭一種基于近紅外光譜分析技術和最小二乘支持嚮量機的鑒彆方法，能夠快速、無損鑒彆聚丙烯酰胺的三種類型。穫取非離子，陰離子和暘離子等三種類型的聚丙烯酰胺樣本的近紅外漫反射光譜，用主成分分析方法對樣本光譜數據進行降維，併提取主成分。基于前三箇主成分對三種類型的聚丙烯酰胺樣本進行聚類分析，併將主成分作為最小二乘支持嚮量機的輸入。通過基于網格搜索的交扠驗證方式優化最小二乘支持嚮量機的參數和作為其輸入的主成分箇數。每種類型聚丙烯酰胺各採集60箇樣本，共採集180箇樣本，每種類型樣本隨機選取45箇樣本，共135樣本作為訓練樣本集，剩餘45箇樣本作為測試集。為瞭驗證該方法能否鑒彆摻假樣本，製備瞭摻入不同比例非離子聚丙烯酰胺的5箇陰離子和5箇暘離子聚丙烯酰胺樣本。採用基于訓練樣本集交扠驗證預測誤差的F統計顯著性檢驗方法來確定樣本的鑒彆結果誤差閾值。結果錶明，預測測試集時，準確率為100％。預測10箇混和樣本時，所有混閤樣本都被準確識彆齣。說明該方法能快速無損鑒彆不同類型的聚丙烯酰胺併且具有摻假鑒彆能力，為聚丙烯酰胺類型的快速鑒彆提供瞭一種新方法。
제출료일충기우근홍외광보분석기술화최소이승지지향량궤적감별방법，능구쾌속、무손감별취병희선알적삼충류형。획취비리자，음리자화양리자등삼충류형적취병희선알양본적근홍외만반사광보，용주성분분석방법대양본광보수거진행강유，병제취주성분。기우전삼개주성분대삼충류형적취병희선알양본진행취류분석，병장주성분작위최소이승지지향량궤적수입。통과기우망격수색적교차험증방식우화최소이승지지향량궤적삼수화작위기수입적주성분개수。매충류형취병희선알각채집60개양본，공채집180개양본，매충류형양본수궤선취45개양본，공135양본작위훈련양본집，잉여45개양본작위측시집。위료험증해방법능부감별참가양본，제비료참입불동비례비리자취병희선알적5개음리자화5개양리자취병희선알양본。채용기우훈련양본집교차험증예측오차적F통계현저성검험방법래학정양본적감별결과오차역치。결과표명，예측측시집시，준학솔위100％。예측10개혼화양본시，소유혼합양본도피준학식별출。설명해방법능쾌속무손감별불동류형적취병희선알병차구유참가감별능력，위취병희선알류형적쾌속감별제공료일충신방법。
In this paper ,a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide . The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide ,Anionic Polyacrylamide and Cationic Polyacrylamide were measured .Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents .The first three principal components were used for cluster analysis of the three different types of Polyacrylamide .Then those principal components were also used as inputs of least square support vector machine model .The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search .60 samples of each type of Polyacrylamide were collected . Thus a total of 180 samples were obtained .135 samples ,45 samples for each type of Polyacrylamide ,were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model .In addition ,5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different propor-tion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples .The prediction error threshold for each type of Polyacrylamide was determined by F statistical signifi-cance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation . The discrimination accuracy of the built model was 100% for prediction of the test set .The prediction of the model for the 10 mixing samples was also presented ,and all mixing samples were accurately discriminated as adulterated samples .The overall re-sults demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples ,and offered a new approach to discriminate the types of Polyacrylamide .
