一種新型光譜多元分析模式識(shí)別方法
發(fā)布時(shí)間：2018-09-11 09:24
【摘要】：SIMCA采用PCA模型參數(shù)和F檢驗(yàn)構(gòu)造計(jì)算T2i/T2ucl和Si/Q統(tǒng)計(jì)量作為樣本分類的新屬性,并計(jì)算待測(cè)樣本到各類主成分空間的歐式距離作為判別類別的依據(jù),是一種最常用和優(yōu)秀的光譜分類方法。但是,在Q對(duì)T2作圖平面上,以歐式距離確定的樣本分布范圍是一個(gè)圓,多數(shù)情況下并不一定能符合實(shí)際樣本分布規(guī)律。本文在分析了SIMCA理論缺陷的基礎(chǔ)上,提出了一種新方法,即用馬氏距離代替歐氏距離作為判別依據(jù)來判斷樣本的類別。并設(shè)計(jì)了采用紅外光譜判別組分比例很接近的摻假食用油樣本的實(shí)驗(yàn),以及用近紅外光譜判別相近皮毛樣本的實(shí)驗(yàn)。用調(diào)和比5%~8%的食用油紅外光譜PCA模型,分別以馬氏距離和歐式距離計(jì)算出其樣本的分布范圍,結(jié)果表明馬氏距離的分類與識(shí)別能力更強(qiáng)。新方法和SIMCA對(duì)動(dòng)物皮毛樣本的正確識(shí)別率分別為87.5%和75%,對(duì)比例相近的食用油調(diào)和油的正確識(shí)別率分別為65%和55%。結(jié)果表明新方法對(duì)化學(xué)組成差異微小的樣品分類精度明顯優(yōu)于SIMCA。
[Abstract]:SIMCA uses PCA model parameters and F test to construct T2i/T2ucl and Si/Q statistics as new attributes of sample classification, and calculates the Euclidean distance from samples to various principal component spaces as the basis for classification. It is one of the most common and excellent spectral classification methods. However, the range of sample distribution determined by Euclidean distance is a circle on the map plane of Q pair T2, and in most cases it does not conform to the law of actual sample distribution. On the basis of analyzing the defects of SIMCA's theory, a new method is proposed in this paper, which is to use Markov distance instead of Euclidean distance as the basis to judge the classification of samples. The experiment of using infrared spectrum to distinguish the samples of adulterated edible oil with very close proportion of components and the experiment of using near infrared spectrum to distinguish the samples of similar fur were designed. The PCA model of infrared spectrum of edible oil with a harmonic ratio of 5% and 8% is used to calculate the distribution range of the samples from Markov distance and Euclidean distance respectively. The results show that the classification and recognition ability of Markov distance is stronger. The correct recognition rates of the new method and SIMCA for animal fur samples were 87.5% and 75%, respectively. The correct recognition rates for edible oil blending oil with similar proportion were 65% and 55%, respectively. The results show that the classification accuracy of the new method is better than that of SIMCA. for samples with slight difference in chemical composition.
【作者單位】： 北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院;北京化工大學(xué)材料科學(xué)與工程學(xué)院;碳纖維及功能高分子教育部重點(diǎn)實(shí)驗(yàn)室;北京市毛麻絲織品質(zhì)量監(jiān)督檢驗(yàn)站;內(nèi)蒙古自治區(qū)纖維檢驗(yàn)局;
【基金】：國(guó)家重大科學(xué)儀器設(shè)備開發(fā)專項(xiàng)(2013YQ220643) 北京市自然科學(xué)基金項(xiàng)目(4172044)資助
【分類號(hào)】：O657.3;TS227
本文編號(hào)：2236288
[Abstract]:SIMCA uses PCA model parameters and F test to construct T2i/T2ucl and Si/Q statistics as new attributes of sample classification, and calculates the Euclidean distance from samples to various principal component spaces as the basis for classification. It is one of the most common and excellent spectral classification methods. However, the range of sample distribution determined by Euclidean distance is a circle on the map plane of Q pair T2, and in most cases it does not conform to the law of actual sample distribution. On the basis of analyzing the defects of SIMCA's theory, a new method is proposed in this paper, which is to use Markov distance instead of Euclidean distance as the basis to judge the classification of samples. The experiment of using infrared spectrum to distinguish the samples of adulterated edible oil with very close proportion of components and the experiment of using near infrared spectrum to distinguish the samples of similar fur were designed. The PCA model of infrared spectrum of edible oil with a harmonic ratio of 5% and 8% is used to calculate the distribution range of the samples from Markov distance and Euclidean distance respectively. The results show that the classification and recognition ability of Markov distance is stronger. The correct recognition rates of the new method and SIMCA for animal fur samples were 87.5% and 75%, respectively. The correct recognition rates for edible oil blending oil with similar proportion were 65% and 55%, respectively. The results show that the classification accuracy of the new method is better than that of SIMCA. for samples with slight difference in chemical composition.
【作者單位】： 北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院;北京化工大學(xué)材料科學(xué)與工程學(xué)院;碳纖維及功能高分子教育部重點(diǎn)實(shí)驗(yàn)室;北京市毛麻絲織品質(zhì)量監(jiān)督檢驗(yàn)站;內(nèi)蒙古自治區(qū)纖維檢驗(yàn)局;
【基金】：國(guó)家重大科學(xué)儀器設(shè)備開發(fā)專項(xiàng)(2013YQ220643) 北京市自然科學(xué)基金項(xiàng)目(4172044)資助
【分類號(hào)】：O657.3;TS227
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