本文選題：電子鼻 + 霉變玉米��； 參考：《河南科技大學(xué)》2017年碩士論文

【摘要】：為了提高電子鼻檢測霉變玉米的正確率,探究了電子鼻信號(hào)不同多特征組合表征模式方法對(duì)鑒別結(jié)果的影響,并給出了一種基于Wilks統(tǒng)計(jì)量的特征參量鑒別能力評(píng)價(jià)方法。同時(shí),考慮到不同氣敏傳感器選擇特性的差異,在多特征表征模式下給出了一種基于Wilks統(tǒng)計(jì)量主元消去變換的傳感器信號(hào)表征特征的篩選方法。為了提高電子鼻預(yù)測霉變玉米真菌毒素含量,在五特征表征模式下發(fā)展了一種基于核Fisher判別分析融合BP神經(jīng)網(wǎng)絡(luò)的預(yù)測模型構(gòu)建方法,以期提高霉變玉米真菌毒素含量的電子鼻檢測能力。具體研究工作說明如下:首先對(duì)電子鼻中每個(gè)氣敏傳感器的霉變玉米氣敏信號(hào)分別提取積分值、小波能量值、方差、相對(duì)穩(wěn)態(tài)平均值、平均微分值作為特征值。然后,利用WilksΛ統(tǒng)計(jì)量計(jì)算單一特征表征模式和多特征組合表征模式下所構(gòu)造的特征向量的鑒別能力。計(jì)算結(jié)果表明,多特征表征模式的鑒別效果優(yōu)于單特征表征模式的鑒別效果,并且隨著表征特征數(shù)的增多,所對(duì)應(yīng)的特征向量的鑒別能力也進(jìn)一步提高。研究結(jié)果也指出了多特征表征模式下如何進(jìn)行特征組合是不具有規(guī)律性的,但可通過對(duì)比不同特征組合的WilksΛ值,來獲得較好的表征特征組合。同時(shí),在多特征組合表征模式下,借助于所給出的特征篩選方法,優(yōu)化篩選了不同傳感器氣敏信號(hào)的特征組合。結(jié)果顯示:在多特征表征模式下不同傳感器特征表征是不同的,說明了特征篩選的必要性。為了揭示上述特征鑒別能力評(píng)定方法的有效性,運(yùn)用Fisher判別分析(FDA)直觀考察了不同特征表征模式下的鑒別結(jié)果。FDA結(jié)果顯示,無論是單一特征表征模式還是多特征表征模式,它們的鑒別效果與基于WilksΛ統(tǒng)計(jì)量的鑒別能力評(píng)價(jià)結(jié)果相吻合,而且隨著表征特征數(shù)的增多,鑒別正確率也逐漸提高,5特征組合下的FDA鑒別正確率升至98%。FDA分析結(jié)果表明,所給出的特征鑒別能力評(píng)價(jià)方法是有效的。最后,分別借助于BP神經(jīng)網(wǎng)絡(luò)、核變換FDA(KFDA)融合BP神經(jīng)網(wǎng)絡(luò)方法,研究了黃曲霉毒素B1含量、嘔吐毒素含量、玉米赤霉烯酮含量的預(yù)測模型構(gòu)建方法。預(yù)測結(jié)果顯示:單純的BP神經(jīng)網(wǎng)絡(luò)預(yù)測玉米赤霉烯酮、嘔吐毒素含量、黃曲霉毒素B1,預(yù)測誤差在5%以內(nèi)的正確樣本數(shù)所占比例最高為85%;而基于KFDA的BP神經(jīng)網(wǎng)絡(luò)的預(yù)測誤差在0.6%以內(nèi)的正確樣本數(shù)所占比例為100%。且兩種模型的預(yù)測值和實(shí)測值的擬合決定系數(shù)由0.95提高到1.00。研究結(jié)果表明,運(yùn)用基于KFDA的BP神經(jīng)網(wǎng)絡(luò)方法預(yù)測霉變玉米真菌毒素含量是有效的,提高了電子鼻的檢測精度。論文研究結(jié)果可獲得4個(gè)方面的結(jié)論:1)用多特征融合表征模式可以更有效地反映霉變玉米樣品的電子鼻響應(yīng)信息。2)多特征表征向量的鑒別能力可用WilksΛ統(tǒng)計(jì)量進(jìn)行有效評(píng)價(jià)。3)多特征表征模式下,每個(gè)氣敏傳感器的表征特征可用Wilks統(tǒng)計(jì)量主元消去變換的方法進(jìn)行篩選。4)基于KFDA融合BP神經(jīng)網(wǎng)絡(luò)方法預(yù)測霉變玉米真菌毒素含量是有效的。
[Abstract]:In order to improve the accuracy of detection of mouldy corn by electronic nose, the influence of different multi feature combination characterization methods on the identification results of electronic nose signals is explored, and an evaluation method for distinguishing feature parameters based on Wilks statistics is given. At the same time, considering the difference of the selection characteristics of different gas sensitive sensors, the multi feature characterization model is taken into account. In order to improve the prediction of mycotoxin content in mouldy corn by Wilks, a method of building a prediction model based on nuclear Fisher discriminant analysis fusion BP neural network is developed to improve the mildew in order to improve the mildew. The electronic nose detection ability of the content of mycotoxin in corn. The specific research work shows as follows: first, the integral value, the wavelet energy value, the variance, the relative steady state value and the average differential value are taken as the eigenvalues respectively, and the single characteristic table is calculated by the Wilks statistics. The results show that the discriminant effect of the multi feature representation model is better than that of the single feature representation model, and the identification ability of the corresponding eigenvector is further improved with the increase of the characteristic feature number. The feature combination under multi feature representation is not regular, but the better characterization combination can be obtained by comparing the Wilks values of different features. At the same time, the feature combination of different sensor gas sensing signals is optimized by using the feature selection method given in the multi feature combination representation mode. The results show that the feature representation of different sensors is different under the multi feature representation model, indicating the necessity of feature selection. In order to reveal the effectiveness of the evaluation method for the characteristics of the above features, the Fisher discriminant analysis (FDA) is used to visualized the.FDA results of the identification results under the different characterization patterns, regardless of the single feature. Characterization mode or multi feature representation model, their identification results are consistent with the evaluation results based on Wilks based statistics, and with the increase of characterization number, the correct rate of identification is gradually improved. The FDA identification accuracy under the 5 feature combination is raised to the 98%.FDA analysis results. The method is effective. Finally, with the help of BP neural network and nuclear transformation FDA (KFDA) fusion BP neural network, the method of predicting the content of aflatoxin B1, the content of vomit toxin and the prediction model for the content of Zea zearalenone is studied. The prediction results show that the simple BP neural network predicts the content of zearalenone, the content of vomit toxin and Aspergillus flavus Toxin B1, the maximum number of correct samples within 5% of the predicted error is 85%, while the proportion of the correct samples within 0.6% of the KFDA based BP neural network is 100%., and the fitting decision coefficients of the predicted and measured values of the two models are increased from 0.95 to the 1.00. research results, and the KFDA based BP nerve is used. The network method is effective to predict the content of mycotoxin in mouldy corn, which improves the detection precision of the electronic nose. The results of this paper can be obtained from 4 aspects: 1) the multi feature fusion characterization model can more effectively reflect the electronic nose response information.2 of mouldy corn samples). The identification ability of the multi characteristic vector can be calculated by the Wilks statistics. Under the multi characteristic.3) model, the characterization features of each gas sensor can be screened by the method of Wilks statistic principal element elimination transformation. It is effective to predict the content of mycotoxin in mouldy corn based on the KFDA fusion BP neural network method.
【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)】：TS207.3;TP212

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