本文選題：電子鼻　切入點(diǎn)：漂移現(xiàn)象　出處：《河南科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：電子鼻被認(rèn)為是檢測(cè)揮發(fā)性樣品最具發(fā)展?jié)摿Φ囊环N分析儀器。近年來(lái),電子鼻廣泛應(yīng)用于食品檢測(cè)領(lǐng)域。然而,傳感器漂移現(xiàn)象使電子鼻在實(shí)際應(yīng)用中不能長(zhǎng)期穩(wěn)健地檢測(cè)食品等樣品。因此,消除或降低漂移影響對(duì)電子鼻實(shí)現(xiàn)長(zhǎng)期穩(wěn)健檢測(cè)十分重要。電子鼻在對(duì)6種中國(guó)白酒樣品的鑒別過(guò)程中,漂移現(xiàn)象十分明顯,由先驗(yàn)數(shù)據(jù)構(gòu)建的識(shí)別模型不能有效和準(zhǔn)確地預(yù)測(cè)后期樣本數(shù)據(jù)。因此,對(duì)6種白酒的長(zhǎng)期監(jiān)控,如何減少漂移現(xiàn)象的影響,實(shí)現(xiàn)電鼻子長(zhǎng)期穩(wěn)健檢測(cè)是必須研究的工作。本文從信號(hào)變換的角度出發(fā),提出了一種基于小波包變換的遞歸漂移校正方法,用于長(zhǎng)期穩(wěn)健檢測(cè)6種白酒樣品。該方法主要包括構(gòu)建小波包分解系數(shù)的校正函數(shù)和確定構(gòu)建檢測(cè)模型的樣本測(cè)試時(shí)間窗口(SMTW)。借助于所構(gòu)建的小波包分解系數(shù)的校正函數(shù),6種白酒樣品的電子鼻漂移信號(hào)被很好地校正。通過(guò)比較不同SMTW下所構(gòu)建的檢測(cè)模型之間的差異,確定了SMTW為6個(gè)月。通過(guò)遞歸校正方法處理SMTW中的樣本數(shù)據(jù)之后,建立了遞歸Fisher判別分析(FDA)鑒別模型,并且通過(guò)遞歸FDA模型可以準(zhǔn)確地鑒別SMTW之后的2個(gè)月的樣本數(shù)據(jù)。因此,將確定的6個(gè)月的SMTW向前移動(dòng),FDA模型也被不斷地遞歸修正,并且能實(shí)時(shí)對(duì)隨后2個(gè)月內(nèi)的樣本數(shù)據(jù)進(jìn)行正確地鑒別,最低鑒別正確率達(dá)96.5%。因此,采用上述方法實(shí)現(xiàn)了電子鼻對(duì)6種白酒樣品的長(zhǎng)期穩(wěn)健鑒別。最后,考慮到漂移是氣敏傳感器的一種固有行為,論文從無(wú)樣本(空載)條件下初步探討了電子鼻漂移現(xiàn)象,給出了空載條件下去漂移的可行性方法,以期為空載條件去漂移奠定部分基礎(chǔ)。
[Abstract]:The electronic nose is considered as a kind of analysis instrument to detect volatile samples with the most potential for development. In recent years, the electronic nose is widely used in the field of food detection. However, sensor drift phenomenon in the practical application of the electronic nose can not be long-term and steady detection of food samples. Therefore, eliminate or long-term robust detection is very important for the realization of electronic nose to reduce the drift effect. The electronic nose in the identification process of 6 kinds of Chinese liquor samples, the drift phenomenon is very obvious, can not identify the model constructed by the prior data forecast late sample data effectively and accurately. Therefore, the long-term monitoring of the 6 kinds of liquor, how to reduce the effect of drift phenomenon, to achieve long-term stable detection is the electric nose we must study work. From the point of view of signal transformation, proposes a recursive drift correction method based on wavelet packet transform, for the long-term stable detection of 6 kinds of liquor Sample. This method mainly includes the construction of wavelet packet decomposition coefficient correction function and determine the sample test time window construction detection model (SMTW). With the help of the correction function decomposition coefficients in wavelet packets constructed by the electronic nose signal drift 6 kinds of liquor samples is well corrected. The difference between the detection model by comparing different SMTW the construction of the SMTW is determined for 6 months. After treatment by recursive correction method of sample data in SMTW, establishes the recursive Fisher discriminant analysis (FDA) identification model, and through the recursive FDA model can sample data of 2 months after the accurate identification of SMTW. Therefore, the 6 months the SMTW moves forward, FDA model has been modified and can real-time recursive, then the sample data of 2 months were correctly identified, the minimum identification accuracy reaches 96.5%. therefore, using the above method to achieve The long-term stable identification of 6 kinds of liquor samples by electronic nose. Finally, taking into account the shift is an inherent behavior of gas sensors, the paper from the sample (no load) under the conditions discussed electronic nose drift phenomenon, given the feasibility method of no-load condition. The drift, in order to lay the foundation for the part of no-load condition to drift.

【學(xué)位授予單位】：河南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TS262.3;O657

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