Water resources are closely linked to human productivity and life. Owing to the deteriorating water resources environment, accurate and rapid determination of the main water quality parameters has become a current research hotspot. Ultraviolet-visible (UV-Vis) spectroscopy offers an effective tool for qualitative analysis and quantitative detection of contaminants in a water environment. In this review, the principle and application of UV-Vis technology in water quality detection were studied. The principle of UV-Vis spectroscopy for detecting water quality parameters and the method of modeling and analysis of spectral data were presented. Various UV-Vis technologies for water quality detection were reviewed according to the types of pollutants, such as chemical oxygen demand, heavy metal ions, nitrate nitrogen, and dissolved organic carbon. Finally, the future development of UV-Vis spectroscopy for the determination of water quality was discussed.

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.

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Shrimp tends to deteriorate during the refrigeration process. To monitor the freshness of shrimp during refrigeration, near-infrared (NIR) hyperspectral imaging was utilized to non-destructively identify the freshness of shrimp. In the process, three preprocessing methods (multivariate scatter correction [MSC], standard normal variate [SNV], and direct orthogonal signal correction [DOSC]) were employed to preprocess the full-wavelength spectral data, and three characteristic wavelength extraction algorithms (competitive adaptive reweighted sampling [CARS], and random forest [RF] simulated annealing [SA]) were used to extract the best-pre-processed data. Because extreme learning machine (ELM) and kernel extreme learning machine (KELM) are easily affected by parameters, ELM (based on teaching-learning-based optimization [TLBO]) and KELM (based on teaching-learning-based optimization [TLBO]) were proposed. In this study, four discriminant models (ELM, TLBO– ELM, KELM, and TLBO–KELM) were used for the full wavelength modeling analysis and the characteristic wavelength modeling analysis. In this work, the results of the final selected models are presented.

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