工程塑料应用
工程塑料應用
공정소료응용
ENGINEERING PLASTICS APPLICATION
2014年
5期
75-79
,共5页
张毅民%白家瑞%刘红莎%汤桂兰%胡彪
張毅民%白傢瑞%劉紅莎%湯桂蘭%鬍彪
장의민%백가서%류홍사%탕계란%호표
近红外光谱%废旧塑料%鉴别%Fisher判别
近紅外光譜%廢舊塑料%鑒彆%Fisher判彆
근홍외광보%폐구소료%감별%Fisher판별
near infrared spectrum%plastics wastes%identification%Fisher discriminant
通过对化学计量学方法的分析研究,提出了一种基于近红外光谱的丙烯腈-丁二烯-苯乙烯塑料、聚对苯二甲酸乙二酯、聚氯乙烯、聚丙烯、聚苯乙烯和聚乙烯6种塑料的一次性鉴别模型,为近红外塑料识别软件的二次开发提供有效程序。结果表明,光谱经3次多项式、13窗口S-G最小二乘拟合平滑+sym17函数、分解2层小波分析+主成分分析+特征波长选择+Fisher判别处理,可得5个判别函数式。由这些函数式建立的判别模型可以实现6种塑料的一次性识别,其校正集样本自身验证和交叉验证的识别率分别为100%和84.9%,表明该模型稳定;预测集样本进行模型外部检验的准确率为100%,表明该模型可行。
通過對化學計量學方法的分析研究,提齣瞭一種基于近紅外光譜的丙烯腈-丁二烯-苯乙烯塑料、聚對苯二甲痠乙二酯、聚氯乙烯、聚丙烯、聚苯乙烯和聚乙烯6種塑料的一次性鑒彆模型,為近紅外塑料識彆軟件的二次開髮提供有效程序。結果錶明,光譜經3次多項式、13窗口S-G最小二乘擬閤平滑+sym17函數、分解2層小波分析+主成分分析+特徵波長選擇+Fisher判彆處理,可得5箇判彆函數式。由這些函數式建立的判彆模型可以實現6種塑料的一次性識彆,其校正集樣本自身驗證和交扠驗證的識彆率分彆為100%和84.9%,錶明該模型穩定;預測集樣本進行模型外部檢驗的準確率為100%,錶明該模型可行。
통과대화학계량학방법적분석연구,제출료일충기우근홍외광보적병희정-정이희-분을희소료、취대분이갑산을이지、취록을희、취병희、취분을희화취을희6충소료적일차성감별모형,위근홍외소료식별연건적이차개발제공유효정서。결과표명,광보경3차다항식、13창구S-G최소이승의합평활+sym17함수、분해2층소파분석+주성분분석+특정파장선택+Fisher판별처리,가득5개판별함수식。유저사함수식건립적판별모형가이실현6충소료적일차성식별,기교정집양본자신험증화교차험증적식별솔분별위100%화84.9%,표명해모형은정;예측집양본진행모형외부검험적준학솔위100%,표명해모형가행。
Through researching the chemometrics methods,a new method for recognition of six kinds of plastics such as ABS,PET,PVC,PP,PS and PE with Fisher discriminant classification using near infrared spectra was proposed,which provided an effective application for the secondary development of near infrared recognition software. The experimental results show that five discriminants functional expressions can be got after the spectrum processing with Savitzky-Golay smoothing in 3 polynomial and 13 windows+the wavelet analysis in layer 2 and sym17 function+principal component analysis+choose characteristic wavelength+Fisher discriminant analysis. The recognition model which is built with these functional expressions,can be effectively used to identify the six plastics. The accuracies for its validation and cross validation are 100% and 84.9% respectively in calibration set, which shows the stability of the model. The identification accuracies for the unknown samples are 100% in test set which proves the feasibility of the model.