随着餐饮业的发展, 餐饮烟气已经成为某些城市三大空气污染源之一。 由于餐饮烟气对人体健康威胁很大, 近年来对餐饮烟气的研究愈来愈热。 餐饮烟气中包含有大量食用油加热过程中裂解而产生的不饱和烃类, 危害着人类健康。 不同食用油裂解出来的成分以及含量有所不同, 通过构建一定的分类识别数学模型, 从而实现对食用油分类识别。 采用自主研发的傅里叶变换红外光谱仪, 采集了不同食用油油烟烟气红外光谱数据。 同时构建了主成分分析(PCA)分别结合概率神经网络(PNN)以及误差反向传播人工神经网络(BPANN)的分类识别算法。 将两种分类识别算法对不同食用油油烟烟气的傅里叶变换红外光谱数据进行分析。 通过样本数据对数学模型进行训练, 将训练好的数学模型对未知光谱数据进行分析, 来确定产生油烟烟气的食用油种类。 实验结果表明, 两种算法都能对不同的油烟种类进行较好地分类识别。 在全波段识别时, 识别率分别达到90.25%和97.0%。 通过对烟气光谱数据的吸收波段进行分析, 提取大气窗口并且具有较强可挥发性有机物(VOCs)吸收特征的波段（1 300～700 cm-1以及3 000～2 600 cm-1）; 将吸光度数据分成两个分离的吸收波段, 两种算法在3 000～2 600 cm-1波段都有较好的识别效果, PCA-PNN算法识别率为90.25%, PCA-BPANN算法识别率为92.25%。 可见, 两种人工神经网络算法都能有效对食用油烟种类进行识别。

With the developing of catering trade, cooking oil fume has became one of the three major air pollution sources in some cities. In recent years, a lot of research on the cooking oil fume have been done for its high threaten to human health. The cooking oil fume contains a large amount of unsaturated hydrocarbons produced by pyrolysis of edible oil, which are harmful to human health. The characteristics of the composition and content of edible oil fumes produced by pyrolysis of different edible oil are different. For classification and identification of edible oil, two kinds of classification and identification mathematical model are constructed. The spectrum data of different edible oil fume are collected by Fourier transform infrared spectrometer which is independent research and development. At the same time, different classification algorithms of the principal component analysis (PCA) combining probabilistic neural network (PNN) and the error back propagation artificial neural network (BPANN) are constructed respectively. Two kinds of classification algorithms are used to analyze the Fourier transform infrared spectrum data of different cooking fume gas. The mathematical models are trained by the sample data, and the trained mathematical model are used to analyze the unknown spectral data to determine the type of edible oil. The experimental results show that the two algorithms can classify and identify different types of oil fume. In the whole band recognition, the recognition rate is 90.25% and 97% respectively. By analyzed spectral data of flue gas absorption band, spectrums of atmospheric window and the strong absorption feature bands of volatile organic compounds (VOCs) (from 1 300 to 700 cm-1 and from 3 000 to 2 600 cm-1) were extracted. The absorbance data are divided into two parts with separated absorption band, and the two algorithms in 3 000～2 600 cm-1 band have better recognition rate. PCA-PNN algorithm recognition rate is 90.25% and PCA-BPANN algorithm recognition rate is 92.25%. Obviously, two kinds of artificial neural network algorithm combining principle component analysis respectively can effectively identify the types of edible oil fume.