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.
Arsenic (As) is a trace element exist in the environment, and it is one of the common poisonous elements in water, excessive intake of arsenic can cause great damage to human body. At present, mainly used laboratory detection methods of arsenic such as electrochemical method, ion chromatography, atomic absorption spectroscopy and so on, can detective arsenic, but these techniques have some problems such as low sensitivity, intractable operation and expensive. Based on the specific molecules of arsenic, we tested a new rapid detection method of arsenic solution, we prepared surface-enhanced Raman enhanced scattering substrate (SERS substrate) to complete the detection of arsenic solution. Through linear discriminant analysis, the result show that Raman spectrum has high specificity and sensitivity. The study indicated the feasibility of using SERS substrate to conduct Raman spectrum detection on arsenic, which was of great significance for the detection of arsenic in aqueous solution.