深度神经网络在红外光谱定量分析VOCs中的应用

鉴于浅层人工神经网络(ANN)需要依靠先验知识进行人工提取特征,同时较浅的网络结构限制了神经网络学习复杂非线性关系的能力,将深度神经网络(DNN)应用于利用傅里叶变换红外光谱(FTIR)对多组分易挥发性有机物(VOCs)进行的浓度反演研究,并利用仿真实验验证了算法的有效性。从美国环境保护署(EPA)的数据库中选取了包括苯、甲苯、 1,3-丁二烯、乙苯、苯乙烯、邻二甲苯、间二甲苯、对二甲苯在内的八种VOCs气体在8~12μm波长范围内的吸光度谱,每种气体有四种不同浓度下的谱线,依据Beer-Lambert定律从每种VOCs气体中选择一种浓度下的吸光度谱进行混合,得到65 536种不同的VOCs混合气体吸光度谱样本。随机选择5 000组混合气体的吸光度谱,其中4 000组作为训练样本, 1 000组作为预测样本。通过积分提取和主成分提取对光谱矩阵进行降维预处理,将光谱维度从3 457维降到30维。将光谱矩阵经过预处理后得到的新矩阵作为网络输入,对应八种VOCs的浓度矩阵作为输出,建立了30-25-15-10-8的深度神经网络回归预测模型来实现多组分VOCs浓度反演,反演得到样本的均方根误差为0.002 7×10-6,相比于前人利用非线性偏最小二乘拟合、人工神经网络等方法拟合的精度有了明显的提高。每种VOCs气体的均方根误差均不超过0.005×10-6,每个样本的均方根误差均不超过0.006×10-6,证明了深度神经网络预测模型具有良好的非线性拟合能力和良好的稳定性。当训练样本不足(典型值:小于500)时,深度神经网络无法充分地学习,网络误差较大,精度低于单隐藏层的人工神经网络,但随着训练样本数量的增加,深度神经网络的精度不断提高,当训练样本数充足时,相比浅层的人工神经网络,深度神经网络具有更强的非线性关系学习能力,预测精度更高,模型更为稳定。同时,由于训练前对光谱矩阵进行了降维处理,大大降低了算法的复杂度,有效提高了反演效率。分析表明,深度神经网络预测模型具有良好的非线性拟合能力和良好的稳定性,无需人工提取特征就能够充分学习数据特征,同时对多组分VOCs进行浓度反演并达到较高精度。

Application of Deep Neural Network in Quantitative Analysis of VOCs by Infrared Spectroscopy

In view of the fact that shallow artificial neural networks(ANNs) rely on prior knowledge for artificial extraction of features, while shallower network structures limit the ability of neural networks to learn complex nonlinear relationships, this paper applies deep neural networks(DNN) to the study of inversion of multi-component volatile organic compounds(VOCs) by leaf-transformed infrared spectroscopy(FTIR), and the effectiveness of the algorithm was verified by simulation experiments. Eight VOCs including benzene, toluene, 1,3-butadiene, ethylbenzene, styrene, o-xylene, m-xylene, and p-xylene were selected from the US Environmental Protection Agency(EPA) database. In the wavelength range of 8~12 μm, each gas has four different concentration lines, and the absorbance spectrum at one concentration is selected from each VOCs gas according to Beer-Lambert’s law to obtain 65 536 different kinds. Samples of VOCs mixed gas absorbance spectra. The absorbance spectra of 5 000 groups of mixed gases were randomly selected, of which 4 000 were used as training samples and 1000 were used as prediction samples. The dimensional reduction of the spectral matrix was performed by integral extraction and principal component extraction, and the spectral dimension was reduced from 3457 to 30 dimensions. The new matrix obtained by preprocessing the spectral matrix was used as the network input, and the concentration matrix of the eight VOCs was used as the output. A deep neural network regression prediction model of 30-25-15-10-8 was established, and multiple groups were realized by using spectral data. Inversion of VOCs concentration, the root mean square error of the sample obtained by inversion was 0.002 7×10-6, which was obvious compared with the accuracy of previous methods using nonlinear partial least squares fitting and artificial neural network. improve. The root mean square error of each VOCs gas does not exceed 0.005×10-6, and the root mean square error of each sample does not exceed 0.006×10-6, which proves that the deep neural network prediction model has good nonlinear fitting ability. And good stability. When the training sample is insufficient(typical value: less than 500), the deep neural network cannot fully learn, the network error is larger, and the accuracy is lower than that of the single hidden layer artificial neural network, but as the number of training samples increases, the deep neural network accuracy is continuously improved. When the number of training samples is sufficient, the deep neural network has stronger nonlinear relation learning ability than the shallow artificial neural network, and the prediction accuracy is higher and the model is more stable. At the same time, due to the dimensionality reduction of the spectral matrix before training, the complexity of the algorithm is greatly reduced, and the inversion efficiency is effectively improved. The analysis shows that the deep neural network prediction model has good nonlinear fitting ability and good stability. It can fully learn the data features without manual extraction of features, and at the same time, the concentration inversion of multi-component VOCs can achieve higher precision.