Study on Recognition of Cooking Oil Fume by Fourier Transform Infrared Spectroscopy Based on Artificial Neural Network.

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.

[Based on Trigger Sampling Method and Phase Correction of Infrared Spectrum Measurement Applications].

Fourier transform infrared spectrometer can be realized in high temperature flue gas multicomponent measurement at the same time, has wide application prospects in the field. And one of the important factors to determine the success of application, lies in the measuring system of infrared interference figure sampling phase error control. This paper discusses the main-reasons of the appearance of phase error in the system, through the analysis of Helium-neon laser interference signal zero uniformity, illustrates the produce phase error is the main reason of the laser signal and reference signal phase difference. At the meantime, the quantitative analysis of the phase error influence on instrument signal to noise ratio (SNR), also the Mertz phase correction method for the instrument improves the thousands of times of the original signal to noise ratio. And the related experiment, the experimental results show that the system based on the interference figure sampling method satisfy the needs of high temperature flue gas measurements.