Nowadays, it is still a challenge for commercial nitrate sensors to meet the requirement of high accuracy in a complex water. Based on deep-ultraviolet spectral analysis and a regression algorithm, a different measuring method for obtaining the concentration of nitrate in seawater is proposed in this paper. The system consists of a deuterium lamp, an optical fiber splitter module, a reflection probe, temperature and salinity sensors, and a deep-ultraviolet spectrometer. The regression model based on weighted average kernel partial least squares (WA-KPLS) algorithm together with corrections for temperature and salinity (TSC) is established. After that, the seawater samples from Western Pacific and Aoshan Bay in Qingdao, China with the addition of various nitrate concentrations are studied to verify the reliability and accuracy of the method. The results show that the TSC-WA-KPLS algorithm shows the best results when compared against the multiple linear regression (MLR) and ISUS (in situ ultraviolet spectrophotometer) algorithms in the temperatures range of 4–25 °C, with RMSEP of 0.67 µmol/L for Aoshan Bay seawater and 1.08 µmol/L for Western Pacific seawater. The method proposed in this paper is suitable for measuring the nitrate concentration in seawater with higher accuracy, which could find application in the development of in-situ and real-time nitrate sensors.

The accuracy of the in-situ nitrate measurement in seawater is affected by many environmental factors, such as salinity, temperature, and turbidity. Along these lines, in this work, an environmental correction (including salinity, temperature, and turbidity) algorithm was proposed and experimentally verified, demonstrating a good prediction accuracy of the nitrate concentration. The orthogonal projections to latent structures (OPLS) algorithm was used for performing one-time turbidity and temperature correction of the acquired spectral data. After the environmental correction, the support vector machine (SVM) was used to establish the nitrate calculation model. The OPLS-SVM algorithm was systematically examined in spiked samples with four different groups of seawater matrices (the seawater samples from Western Pacific, Aoshan Bay of Qingdao, China, South China Sea, and Yellow Sea with addition of various nitrate concentrations and turbidity). From the extracted results, it was proven that the SVM calculation model with the OPLS correction algorithm could significantly improve the accuracy of the nitrate measurement. More specifically, a prediction performance with root mean square error (RMSE) of 0.22 μmol/L and R ² of 0.999 was demonstrated when the temperature and turbidity ranges were 5-25 °C and 0-50 NTU (nephelometric turbidity unit), respectively. The introduced method is suitable for measuring the nitrate concentration in turbidity seawater with a high degree of accuracy, which is undoubtedly of great importance for performing in-situ nitrate measurement in complex seawater environments.

A deep-ultraviolet (DUV) narrowband tunable filter with a central wavelength of 225.0 nm is designed, fabricated, and demonstrated experimentally. The simulation results showed a transmittance of about 42.1% at 225.0 nm and a full width at half-maximum (FWHM) of ∼ 1.2 n m . The experimental results for normal incidence light revealed a corresponding central wavelength of 225.0 nm, a transmittance of 27.7%, and a FWHM of 1.9 nm. By changing the incident angle, the central wavelength can be tuned from 225.0 to 224.5 nm. This DUV narrowband tunable filter has been shown to demonstrate good performance and has broad applicability.

Accurate extraction of real velocity signals from Doppler velocity measurements is challenging, particularly in environments with low signal-to-noise ratios (SNR). To address this issue, we propose a novel algorithm based on spectral peak density searching, designed for reliable Doppler signal processing under noisy conditions. First, we apply a Gaussian filter to reduce the interference of Gaussian white noise while preserving key characteristics of the Doppler signal. Next, a peak-searching operation identifies all spectral peaks in the denoised signals, and potential Doppler shift signals are selected based on a specific bandwidth threshold. The densest spectral peak band is then determined, and nonlinear least squares smoothing is applied to precisely identify the velocity shift signal. Compared to conventional Doppler signal processing algorithms, this method significantly improves processing accuracy, reducing execution time by approximately 56%, decreasing the average relative error from 3.41% to 1.92%, and increasing the detection probability from 58% to 88%. We validated the performance of the algorithm through experiments simulating weak signals with substantial noise under conditions of low emission intensity and high-speed water flow. The results demonstrate the effectiveness and reliability of the proposed algorithm. The novelty lies in its ability to quickly transmit the processed flow velocity value back to the user interface instead of a massive amount of raw flow velocity data, emphasizing its potential for practical applications in challenging environments.

The nitrate concentration in seawater is an important parameter for marine environment monitoring, which is of great significance for the analysis of seawater ecosystems. In this paper, a novel nitrate measurement system based on a narrowband tunable ultraviolet light source and support vector regression (SVR)-based algorithm is proposed and demonstrated. The system is composed of a tunable light source module, an optical fiber splitter, a temperature control module, a spectrometer and a data processing module. By controlling the motorized rotation stage and motorized filter wheel, a continuous and automatic narrowband tunable deep ultraviolet (DUV) light source module is developed. Different seawater samples from Aoshan Bay (Qingdao, China), the Western Pacific, Sanggou Bay (Weihai, China) and the South China Sea with nitrate concentrations of 0- $102~\mu $ mol/L are measured by this system based on a narrowband tunable DUV light source and the results are compared with those of a broadband DUV light source system. The calculation model is established based on the SVR algorithm with/without temperature and salinity correction (TSC), and the prediction results are compared with those of the in situ ultraviolet spectrophotometry (ISUS) algorithm. The results show that the method based on a narrowband tunable light source system and the TSC-SVR algorithm has the best performance in predicting the nitrate concentration, where the root mean squared error (RMSE) decreased by 29.16% (from 1.20 to $0.85~\mu $ mol/L) compared with the results of the broadband light source system, and the error range is from −3.01 to $2.99~\mu $ mol/L. The proposed method in this paper has the potential to provide more accurate measurements in marine environment monitoring.