本文選題：仿生嗅覺 + 電子鼻��； 參考：《廣東工業(yè)大學(xué)》2012年碩士論文

【摘要】：我國中藥材資源豐富,對(duì)人類醫(yī)學(xué)發(fā)展和促進(jìn)人體健康發(fā)揮著巨大的作用。但由于藥材種類繁多,市場(chǎng)上出現(xiàn)大量的假冒偽劣產(chǎn)品,就連普通的中藥材都出現(xiàn)了大量的混淆品,嚴(yán)重影響了中醫(yī)藥的發(fā)展。因此,中藥材的品質(zhì)判定一直是人們研究的熱點(diǎn),其中產(chǎn)地因素又是評(píng)判中藥材品質(zhì)的重要標(biāo)準(zhǔn)之一。但是長期以來,國內(nèi)外對(duì)于中藥材品質(zhì)的評(píng)定,普遍采用的是感官評(píng)審法,然而,感官評(píng)審法往往要受諸多因素的影響。這就對(duì)中藥材品質(zhì)的檢測(cè)和評(píng)判提出了更高的要求,要求其更加科學(xué)和規(guī)范。 氣味在中藥材品質(zhì)分析中占有重要地位,仿生嗅覺技術(shù)模擬了人類嗅覺的原理,通過檢測(cè)中藥材揮發(fā)性氣味的整體信息來自動(dòng)完成對(duì)氣味的辨識(shí)。目前,國內(nèi)外關(guān)于將仿生嗅覺技術(shù)運(yùn)用于中藥材領(lǐng)域的研究報(bào)道還相對(duì)比較少,因此我們擬通過仿生嗅覺技術(shù)來檢測(cè)中藥材揮發(fā)出的綜合信息,建立一種評(píng)價(jià)中藥材的新技術(shù)。 研究以姜科、傘形科和菊科三種典型科屬的中藥材作為研究對(duì)象,通過PEN3電子鼻檢測(cè)并提取其各特征值,生成高維的特征向量。然后采用主成分分析法提取其相應(yīng)的主成分分量,構(gòu)成模式識(shí)別的輸入。結(jié)合聚類分析和BP神經(jīng)網(wǎng)絡(luò)兩種模式識(shí)別方法來實(shí)現(xiàn)對(duì)不同產(chǎn)地以及易混淆中藥材的判別與鑒定,最后建立適量的中藥材氣味指紋圖譜庫。 聚類分析結(jié)果顯示能夠正確的對(duì)各待測(cè)樣品進(jìn)行歸類。采用BP神經(jīng)網(wǎng)絡(luò)的方法對(duì)不同產(chǎn)地白術(shù)訓(xùn)練集的回判正確率均為100%,誤判的待測(cè)樣本只發(fā)生在安徽白術(shù),其判斷正確率為86.67%；對(duì)易混淆的三組藥材訓(xùn)練集的正確率均為100%,只有砂仁發(fā)生誤判,其判斷正確率為93.33%。 對(duì)兩種模式識(shí)別方法的優(yōu)劣進(jìn)行分析和對(duì)比,得出結(jié)論：聚類分析由于其算法簡(jiǎn)單,能夠快速的對(duì)樣品進(jìn)行分類,但如果使用復(fù)雜的距離相似度度量時(shí),計(jì)算復(fù)雜度會(huì)提高,使其不再具備快速簡(jiǎn)便的優(yōu)點(diǎn)；BP神經(jīng)網(wǎng)絡(luò)具有高度的非線性,在理論上可以逼近任意曲面,但是計(jì)算量較大,計(jì)算復(fù)雜度也較高。本文實(shí)驗(yàn)最合適的方法是聚類分析。 最后利用基于統(tǒng)計(jì)特征(均值、方差、峰值)的3種方法來構(gòu)建樣品的指紋圖譜庫,結(jié)果發(fā)現(xiàn),指紋圖譜曲線具有較高的區(qū)分度,并且各待測(cè)樣本的指紋圖譜曲線都能夠與庫中相應(yīng)的指紋圖譜曲線基本相吻合。 結(jié)果顯示,采用PEN3電子鼻能夠正確實(shí)現(xiàn)中藥材的分類鑒別和指紋圖譜庫的構(gòu)建。
[Abstract]:Chinese medicinal materials are rich in resources and play a great role in the development of human medicine and the promotion of human health. However, because of the variety of medicinal materials, a large number of fake and shoddy products appear in the market, even ordinary Chinese medicinal materials appear a large number of confounding products, which seriously affect the development of traditional Chinese medicine. Therefore, the quality evaluation of Chinese medicinal materials has been the focus of research, among which the origin factor is one of the important criteria to judge the quality of Chinese medicinal materials. However, for a long time, the sensory evaluation method is widely used in the quality evaluation of Chinese medicinal materials at home and abroad. However, the sensory evaluation method is often affected by many factors. Therefore, the quality of traditional Chinese medicine is required to be more scientific and standardized. Smell plays an important role in the quality analysis of Chinese medicinal materials. Bionic olfactory technology simulates the principle of human olfaction and automatically realizes the identification of smell by detecting the whole information of volatile odors of Chinese medicinal materials. At present, there are relatively few reports on the application of bionic olfactory technology in the field of Chinese medicinal materials at home and abroad. Therefore, we intend to use bionic olfactory technology to detect the volatile information of Chinese medicinal materials and establish a new technology to evaluate Chinese medicinal materials. In this study, the traditional Chinese medicines of three typical families and genera of ginger, umbrella and Compositae were used as the research objects. The PEN3 electronic nose was used to detect and extract each characteristic value and to generate the high dimensional characteristic vector. Then principal component analysis (PCA) is used to extract the corresponding principal components to form the input of pattern recognition. Cluster analysis and BP neural network were used to identify and distinguish Chinese medicinal materials from different habitats and easily confused. Finally, a proper amount of odor fingerprint library was established. Cluster analysis results show that each sample can be correctly classified. Using BP neural network method, the correct rate of correct judgment for the training set of Atractylodes macrocephala from different areas is 100, and the samples to be misjudged only occur in Anhui Atractylodes macrocephala. The correct rate of judgment was 86.67, and the correct rate of the three sets of medicine training was 100, only Amomum villosum was misjudged, and the correct rate of judgment was 93.33. The advantages and disadvantages of the two pattern recognition methods are analyzed and compared. It is concluded that the clustering analysis can quickly classify samples because of its simple algorithm, but the computational complexity will be increased if the complex distance similarity measure is used. The BP neural network has a high degree of nonlinearity and can approach any surface in theory, but it has a large amount of computation and a high computational complexity. The most suitable method for this experiment is clustering analysis. Finally, three methods based on statistical features (mean, variance, peak) are used to construct the fingerprint database of samples. The results show that the fingerprint curve has a high degree of differentiation. And the fingerprint curve of each sample can be basically consistent with the corresponding fingerprint curve in the database. The results showed that the classification and identification of Chinese medicinal materials and the construction of fingerprint library could be correctly realized by using PEN3 electronic nose.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH788.2

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本文編號(hào)：1826672