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Yan Hou

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Proceedings Article | 12 March 2020 Paper

Application of spectral droplet analysis method in flammable liquids identification

Haixiu Chen, Zhenlin Hu, Peng Wang, Weihua Xu, Yan Hou

Proceedings Volume 11436, 1143609 (2020) https://doi.org/10.1117/12.2542995

KEYWORDS: Liquids, Absorption, Capacitance, Optical fibers, Signal processing

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Personal safety in public places has become the primary demand of modern people's social life, and the detection of dangerous liquids is a key technology in the field of security inspection. The spectral drop analysis method is used to study the identification of flammable liquids in this paper. The spectral droplet analysis system is constructed with the fiber-capacitance drop sensor and spectrometer, and through the combination of signal processing circuit and the acquisition software, the experimental platform is designed. Experiments with typical liquid samples are completed and the three-dimensional fingerprints of the samples are obtained. And the characteristic values of liquid samples are extracted after data processing, the methods of rapid identification of flammable liquids based on spectral and drop fingerprint information are researched. With the spectral information, the characteristic wavelength points are selected to extract the characteristic parameters of the sample using the principal component analysis method. And the discriminant prediction models are established by distance discrimination, Bayes discriminant and Fisher discriminant. With the drop fingerprint data, the characteristic parameters are extracted with waveform analysis method, and then the extreme learning machine algorithm is used to build classification and identification model. The experimental results show that it is feasible to identify flammable liquids by spectral droplet analysis method.

Proceedings Article | 13 November 2019 Paper

Research on multi-parameter detection method for environmental water quality

Haixiu Chen, Jiangzhou Zhang, Zhishuai Li, Jie Liu, Yan Hou

Proceedings Volume 11343, 113430X (2019) https://doi.org/10.1117/12.2548545

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The droplet analysis technology and the detection principles of water quality parameters are combined to achieve quantitative detection of multi-parameter of water. The detection platform is designed based on fiber and capacitance droplet analysis technology, which is mainly composed of the droplet sensor, dissolved oxygen probe, liquid supply pump, photoelectric conversion elements, and the signal processing circuit. The detection of three quality parameters (refractive index, turbidity and dissolved oxygen) is carried out on this platform through experiments. For the turbidity of the water, the sample’s rainbow-peak value of the fingerprint obtained with the droplet sensor is proved to be highly correlated with turbidity. And the prediction model of turbidity is established by regression analysis method with Formazine standard solution, with he maximum relative error 3.9%. The measurement model of dissolved oxygen is researched by collecting the fluorescence signal excited by the dissolved oxygen probe and the sample’s temperature, and the performance of the BP neural network model and the regression model is compared. And it shows that BP neural network model performs better in the detection of dissolved oxygen. The measurement model of refractive index is determined through regression analysis, and the value of the rainbow-peak is selected as the key factor through the experiments with NaCl solution. The establishment of the three parameters’ detection model shows us a method to realize multi-parameter detection for environmental water quality.

Proceedings Article | 13 November 2019 Paper

Research on COD detection method based on UV-Vis spectroscopy

Haixiu Chen, Haifeng Xue, Jie Liu, Zhishuai Li, Yan Hou

Proceedings Volume 11343, 113430U (2019) https://doi.org/10.1117/12.2548522

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Ultraviolet-visible (UV-Vis) spectroscopy technology is used to measure chemical oxygen demand (COD) of water. The standard samples are prepared using potassium hydrogen phthalate. With different pretreatment methods and various modeling methods, the COD prediction models’ performance based on raw spectra are compared, and the sensitive wavelengths are selected on basis of the prediction results. In order to build prediction models with optimal performance, the water quality parameters’ effects on the detection of COD are also researched, and the experiments are carried out to find the relationship between COD and the sample’s temperature, turbidity. Then a combined method based on UV-Vis spectrum and water quality parameters is developed. The samples’ temperature and turbidity data are normalized with Min-Max Normalization method, and then different coefficients are assigned to the two parameters to form a new data, basing on the correlation coefficients of the models established by fusing the spectral information with temperature and turbidity respectively. A prediction COD model with the fusion data of water quality parameters and spectral information is established, using Partial least Squares(PLS) method. The experimental results show optimal performance (Mean ARE=2.46; RMSEP=1.92) for the prediction set. And this COD detection method set the foundation for further implementation of online analysis of water quality.

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