Application and Comparison of Spectral Data Filtering for Aromatic Hydrocarbon Concentration Identification by Using an Algorithm
Keywords:
Sensors, Aromatics hydrocarbons(BTX), Polynomial FIT (Polyfit), GasesProcessorsAbstract
Every year the production of benzene increases, thus as Benzene, Toluene and Xylenes cause significant harmful to human veracity, and the situation is exacerbated by that is the fact, which of exceeding the Maximum Permissible Concentration (MPC) of 0.1 mg/m3. Prolonged exposure to the concentration of benzene, can lead to such consequences as some types of cancer, reproductive shortcomings, harm to the nervous system, as well as pallor. The techniques used to decide focus depend on the utilization of information from substance sensors and mathematical strategies for deciding the convergence of gases. all this method has certain limitations, which leads to the complexity of the calibration. The main from of this study removing all unnecessary noise, and it is necessary to minimize the loss obtained from the spectroscopic data, for improving for working on the exactness of ascertaining the convergence of aromatic hydrocarbons. Has been comparing the data of aromatic compounds obtained, It started from receiving the data, and through the process of filtering the signal using the Median filter, as well as autofluorescence Background Removal and Polynomial Fit(Polyfit), and finally, a method has been used, (GasesProcessors), all that separately for(benzene, toluene, xylene), the classification results are presented in the form of a box with a "whisker These changes were 2.5%, 2%, and 2.8%, for the ratio (before processing to using the Medium filter ), respectively, and the changes were 3% for the ratio of the PolyFit method to the Medium filter, and 8%, 9%, and 4%, for the ratio method use(PolyFit) to Medium filter, and 3%,8% and 0% for the ratio of method use(PolyFit) to method use(PolyFit), respectively. It should be noted here that the proposed method (GasesProcessors) is superior in terms of filter performance and autofluorescence background removal compared to previous methods.
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