Fourier Transform Imaging Spectrometer(FTIS) is an instrument cable of acquiring two-dimensional spatial information and one-dimensional spectral information. The FTIS has attracted much attention and is widely applied in the fields like military reconnaissance, remote sensing, biomedicine, environmental monitoring, etc. The FTIS acquires the spectral intensity in different wavelengths by performing Fourier transform on the white light interference signal of the target generated by the FTIS. The spectral curve obtained directly by Fourier transform reflects the relationship between the wavenumber order and the spectral intensity. So wavelength calibration is required to convert the above relationship into the relationship between the wavelength and the spectral intensity, which makes it more intuitive. Therefore, wavelength calibration is a necessary step for FTIS to recovery spectrum. The traditional wavelength calibration method can only get the wavenumber order in the range of integer because of the picket fence effect in Fourier transform. It will definitely ignore the fractional part which results in the inaccurate wavenumber order, which will directly affect the precision of the wavelength calibration result. In order to solve this problem, a high-precision wavelength calibration method based on Fourier transform imaging spectrometer is proposed according to the principle of FTIS and Fourier transform. This method can calculate the wavenumber order with the precision of percentile, which will reduce the error of wavelength calibration effectively. As a result, the precision of spectral calibration can be increased eventually. This method realizes high-precision wavelength calibration by the way of adding zero to the interference fringe in the spatial domain. The core of the method is getting a more precise wavenumber order. The brief process of obtaining wavenumber order is as follows: First, the FTIS acquires the interference fringe of a monochromatic laser. Second, the original interference fringe is extrapolated with zero. Third, the subtle spectrum can be obtained by performing Fourier transform on the extrapolated interference fringe. Finally, the precise wavenumber order is calculated by dividing the abscissa of the peak value by the extrapolation multiple. The principle of this method is investigated and related simulations are then carried out. The simulation results indicate that the wavenumber order calculated by the method have the same precision with the preset parameters, which illustrates that the method can calculate the wavenumber order more accurately. Therefore, the method can improve the precision of the spectral calibration. Besides, related experiments are also performed. The laser interference fringes of different wavelengths generated by the actual FTIS all apply the method to get the wavenumber orders in the frequency domain. Then a curve which is the wavelength calibration function is fitted using the discrete relation between the wavelengths and the wavenumber orders. A laser whose wavelength is known is measured by the FTIS with the wavelength calibration function got by the proposed method. The error of the wavelength measurement result is one-fifth of the traditional method. The simulations and the experiment results indicate that the proposed method can improve the precision of the wavelength calibration, which provides the theory and technology support for spectral measurement using FTIS. It also provides a possibility for the development of FTIS towards the super resolution direction.
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.
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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