【作者】 黄勇；

【导师】 胡旻；

【作者基本信息】 电子科技大学 ， 电子信息（专业学位）， 2023， 硕士

【摘要】 陈皮是一种名贵的药食品,在日常生活中被广泛使用,具有数百亿的市场价值,目前市面上的陈皮质量参差不齐,不法商家以次充好,但现有的鉴别技术存在着周期长、成本高等不足,因而探寻一种操作简便、检测周期短的实时鉴别方法具有十分重要的意义。太赫兹(Terahertz)波在电磁波谱中位于微波和红外线之间,具有指纹波谱性、安全性高、能量低等优良特性,正是这些特性使太赫兹在物质鉴别、国防、生物医学等领域有着广泛的应用。本文先利用太赫兹波谱与机器学习相结合的方法对不同陈皮进行分类,然后对陈皮波谱进行特征重要性分析寻找波谱中对分类结果起关键作用的特征点。主要工作如下:1.太赫兹波谱与机器学习的方法对七类陈皮进行分类。对不同陈皮的原始数据进行预处理并分别建立了支持向量机、随机森林、反向传播神经网络和核极限学习机四种分类模型。从实验结果来看,对于太赫兹时域光谱仪的数据来说,四类模型的最佳准确率分别为94.2857%、90%、82.4571%和91.4286%。对于太赫兹矢量网络分析仪的数据,四类模型的最佳准确率分别为98.5714%、98.5714%、88.5714%、95.7143%,其中支持向量机模型具有最佳性能且适中的训练时间。上述结果表明基于太赫兹波谱与机器学习的鉴别方法能够实现陈皮的鉴别。2.太赫兹波谱的特征重要性分析。通过等间隔划分波谱数据为不同频段、LASSO算法、偏最小二乘算法和互信息算法等方法对波谱进行特征重要性分析并建立基于支持向量机的分类模型。实验结果表明,不同频段的太赫兹时域光谱仪数据建立的分类模型在准确率上存在明显差异,其中0-1THz频段的准确率为91.4286%,而其余频段的准确率均不超过53%。而太赫兹矢量网络分析仪的数据在每个频段的准确率基本一致,均大于95%。利用重要性靠前的10个特征建立的分类模型的准确率不低于87%,最高为95.7143%。这表明这10个特征在分类过程中起主要作用,这些特征基本位于0.5T-0.85THz的频率范围之内,表明不同陈皮波谱在该频段内存在特征点。更多还原

【Abstract】 Chen Pi is a kind of valuable medicinal food,which is widely used in daily life and has a market value of tens of billions of dollars,but the quality of Chenpi in the market varies,and the illegal businessmen use substandard as good,which not only damages the interests of consumers,but also affects the brand image of Chenpi.Therefore,it is very important to explore a real-time identification method with easy operation and short detection period.Terahertz wave is located between microwave and infrared in the electromagnetic spectrum,and it is in the middle of infrared and microwave in the spectrum,so it has excellent characteristics such as fingerprint spectrum,high security,low energy,etc.It is these characteristics that make terahertz have a wide range of applications in material identification,national defense,biomedical and other fields.In this thesis,we first classify different chenopods using a combination of terahertz spectroscopy and machine learning,and then analyze the chenopod spectra for feature importance to find the feature points in the spectra that play a key role in the classification results.The main work is as follows:1.Terahertz spectroscopy with machine learning approach to classify seven types of Chen Pi.The raw data of different chenopods are preprocessed and four classification models,namely,support vector machine,random forest,back propagation neural network and kernel limit learning machine,are built respectively.From the experimental results,the best accuracy of the four types of models for the data of terahertz timedomain spectrometer is 94.2857%,90%,82.4571% and 91.4286 %.For the data from the terahertz vector network analyzer,the best accuracies of the four classes of models are98.5714%,98.5714%,88.5714%,and95.7143%,respectively,with the support vector machine model having the best performance and moderate training time.The above results show that the identification method based on terahertz spectroscopy and machine learning can achieve the identification of Chenopodium.2.Feature importance analysis of terahertz spectrum.The spectrum was divided into different frequency bands by equal intervals,LASSO algorithm,partial least squares algorithm and mutual information algorithm to analyze the feature importance of the spectrum and build a support vector machine based classification model.The experimental results show that the classification models established by terahertz time-domain spectrometer data in different frequency bands have significant differences in accuracy,among which the accuracy of 0-1THz band is 91.4286%,while the accuracy of the rest of the bands does not exceed 53%.And the accuracy of the data from the terahertz vector network analyzer is basically the same in each frequency band,which is greater than 95%.The accuracy of the classification models built using the top 10 features in importance is no less than87%,with the highest being 95.7143%.This indicates that these 10 features play a major role in the classification process,and these features basically lie within the frequency range of 0.5T-0.85 THz,indicating the existence of feature points in this frequency band for different chenopodium spectra.更多还原