In recent years, water quality testing has become an increasingly important topic. Compared with some common water quality identification methods, this study proposes a new method for identifying water samples in UV-visible spectroscopy. In this study, the UV-visible spectra of water samples from two different regions of tianchi and shuimogou in Urumqi were measured, and the pattern recognition algorithm was used to identify the two types of water samples. The acquired UV-visible spectra of water samples were extracted from 80 original high-dimensional spectral data by Partial Least Squares Regression (PLS), and the extracted features were modeled and classified by Support Vector Machine (SVM) classifier. The parameters C and g are optimized by Grid Searching (GS). The classification accuracy of the tianchi water sample and the water mill ditch water sample was 100%. The results of this study illustrate the great potential for rapid detection of water samples using UV-visible spectroscopy in the future.
Silver ions cannot exist in excess in the human body. Conventional instrumental analysis methods such as atomic emission and atomic absorption are commonly used to detect Ag +, but the sensitivity is not satisfactory. Therefore, we developed a novel surface-enhanced Raman scattering (SERS) substrate with a single-layer porous silicon structure, and we completed the detection of Ag + in domestic water and food based on this substrate. The SERS substrate with porous silicon structure has high detection sensitivity. It is found that Ag + can be oxidized and deposited on porous silicon to change the Raman spectral properties. The results show that the Raman spectral intensity is linearly related to different content of silver ions, and the maximum linear correlation coefficient is 0.95123. The exploratory research results prove that the newly prepared SERS substrate with single-layer porous silicon is has great significance for the detection of water source and food safety.
In the extraction of Raman spectra, the signal will be affected by a variety of background noises, and then the effective information of Raman spectra is weakened or even submerged in noises, so the spectral analysis and denoising processing is very important. The traditional ensemble empirical mode decomposition (EEMD) method is to remove the noises by removing the IMF components that mainly contain the noises. However, it will lose some details of the Raman signal. For the problem of EEMD algorithm, the denoising method of smoothing filter combined with EEMD is proposed in this paper. First, EEMD is used to decompose the Raman noise signal into several IMF components. Then, the components mainly containing noises are selected using the self-correlation function, and the smoothing filter is used to remove the noises of the components. Finally, the sum of the denoised components is added with the remaining components to obtain the final denoised signal. The experimental results show that compared with the traditional denoising algorithm, the signal-to-noise ratio (SNR), the root mean square error (RMSE) and the correlation coefficient are significantly improved by using the proposed smoothing filter combined with EEMD.