Dissolved oxygen is an important parameter for monitoring the water quality in the ocean. This study focuses on the research of compensation calibration methods for optical dissolved oxygen sensors, which possess advantages such as good robustness and high stability. Considering the issue of data drift caused by various environmental factors affecting optical sensors, the study specifically targets the drawbacks of multi-point nonlinear coupled compensation calibration methods. An optimized quantitative algorithm model is designed to simplify the operation process and enhance the accuracy of dissolved oxygen monitoring. The deviation between the reference value and the calibrated value of the sensor is within ±4μmol•L-1, and the goodness of fit reaches 0.99956.
In recent years, the increasingly serious problem of marine pollution has raised high concern about environmental issues among people, which in turn has promoted rapid development of marine pollution detection technology. UV fullspectrum analysis is a nitrate detection method based on characteristics such as no reagents required, convenient and fast, and high detection accuracy. This article takes nitrate standard solution prepared with potassium nitrate as the experimental object, collects UV full-spectrum from 0 to 7mg/L standard solutions, uses the Savitzky-Golay (SG) smoothing algorithm to remove noise from the original nitrate full-spectrum data, processes the UV full-spectrum data using principal component analysis (PCA) algorithm, extracts characteristic bands, and uses BP neural network modeling for the screened bands. The results show that within the spectral range of 201-207nm, the prediction model achieves the highest coefficient of determination, with an R2 of 0.99997, and the smallest root mean square error, with an RMSE of 0.11149, and all performance indicators are superior to those of the UV full-spectrum modeling method.
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