Turbidity compensation method based on Mie scattering theory for water chemical oxygen demand determination by UV-Vis spectrometry

Spectrochip-based Calibration Curve Modeling (CCM) for Rapid and Accurate Multiple Analytes Quantification in Urinalysis

Most urine test strips are intended to enable the general population to rapidly and easily diagnose potential renal disorders. It is semi-quantitative in nature, and although the procedure is straightforward, certain factors will affect the judgmental outcomes. This study describes rapid and accurate quantification of twelve urine test strip parameters: leukocytes, nitrite, urobilinogen, protein, pH, occult blood, specific gravity, ketone, bilirubin, glucose, microalbumin, and creatinine using a micro-electromechanical system (MEMS)-based spectrophotometer, known as a spectrochip. For each parameter, absorption spectra were measured three times independently at eight different concentration levels of diluted standard solutions, and the average spectral intensities were calculated to establish the calibration curve under the characteristic wavelength (λ c ). Then, regression analysis on the calibration curve was performed with GraphPad Prism software, which revealed that the coefficient of determination (R 2 ) of the modeled calibration curves was greater than 0.95. This result illustrates that the measurements exceed standard levels, confirming the importance of a spectrochip for routine multi-parameter urine analysis. Thus, it is possible to obtain the spectral signal strength for each parameter at its characteristic wavelength in order to compare directly with the calibration curves in the future, even in situations when sample concentration is unknown. Additionally, the use of large testing machines can be reduced in terms of cost, time, and space by adopting a micro urine testing platform based on spectrochip, which also improves operational convenience and effectively enables point-of-care (POC) testing in urinalysis.

A COD measurement method with turbidity compensation based on a variable radial basis function neural network

In recent years, ultraviolet-visible spectrometry has been widely used to measure sewage's chemical oxygen demand (COD). However, most methods that use UV-vis spectroscopy for COD measurement have not eliminated the influence of turbidity. Therefore, this article proposes a new COD measurement method using UV-vis spectroscopy. This method includes a new turbidity compensation algorithm and an algorithm for COD measurement using a variable radial basis function (VRBF) neural network. Our turbidity compensation algorithm first utilizes principal component analysis (PCA) to extract the characteristic wavelengths of the spectrum. Then, turbidity is used to fit the absorbance difference caused by turbidity at the characteristic wavelength, and a turbidity compensation model is obtained. The turbidity compensation model is used to remove the influence of turbidity from the absorbance spectrum, thereby compensating for its effect on the COD measurement. Secondly, the VRBF neural network model is used to measure the COD concentration. The results show that, compared with the traditional partial least squares regression model, the R2 coefficient of determination increases from 0.27 to 0.88, and the root-mean-square deviation decreases from 5.56 to 1.69. Compared with the improved bagging algorithm and MLP algorithm, this method can measure COD concentration from absorption spectra faster, more directly, and more accurately.

A straightforward approach utilizing an exponential model to compensate for turbidity in chemical oxygen demand measurements using UV-vis spectrometry

Recently, ultraviolet-visible (UV-vis) absorption spectrometry has garnered considerable attention because it enables real-time and unpolluted detection of chemical oxygen demand (COD) and plays a crucial role in the early warning of emerging organic contaminants. However, the accuracy of detection is inevitably constrained by the co-absorption of organic pollutants and turbidity at UV wavelengths. To ensure accurate detection of COD, it is necessary to directly subtract the absorbance caused by turbidity from the overlaid spectrum using the principle of superposition. The absorbance of COD is confined to the UV range, whereas that of turbidity extends across the entire UV-vis spectrum. Therefore, based on its visible absorbance, the UV absorbance of turbidity can be predicted. In this way, the compensation for turbidity is achieved by subtracting the predicted absorbance from the overlaid spectrum. Herein, a straightforward yet robust exponential model was employed based on this principle to predict the corresponding absorbance of turbidity at UV wavelengths. The model was used to analyze the overlaid absorption spectra of synthetic water samples containing COD and turbidity. The partial least squares (PLS) method was employed to predict the COD concentrations in synthetic water samples based on the compensated spectra, and the corresponding root mean square error (RMSE) values were recorded. The results indicated that the processed spectra yielded a considerably lower RMSE value (9.51) than the unprocessed spectra (29.9). Furthermore, the exponential model outperformed existing turbidity compensation models, including the Lambert-Beer law-based model (RMSE = 12.53) and multiple-scattering cluster method (RMSE = 79.34). Several wastewater samples were also analyzed to evaluate the applicability of the exponential model to natural water. UV analysis yielded undesirable results owing to filtration procedures. However, the consistency between the compensated spectra and filtered wastewater samples in the visible range demonstrated that the model is applicable to natural water. Therefore, this proposed method is advantageous for improving the accuracy of COD measurement in turbid water.

A Turbidity-Compensation Method for Nitrate Measurement Based on Ultraviolet Difference Spectroscopy

To solve the problem that turbidity in water has a significant effect on the spectra of nitrate and reduces the accuracy of nitrate detection, a turbidity-compensation method for nitrate measurement based on ultraviolet difference spectra is proposed. The effect of turbidity on the absorption spectra of nitrate was studied by using the difference spectra of the mixed solution and a nitrate solution. The results showed that the same turbidity had different effects on the absorbance of different concentrations of nitrate. The change in absorbance due to turbidity decreased with an increase in the nitrate concentration at wavelengths from 200 nm to 230 nm, although this change was constant when the wavelength was greater than 230 nm. On the basis of this characteristic, we combined the residual sum of squares (RSS) and interval partial least squares (iPLS) to select wavelengths of 230-240 nm as the optimal modeling interval. Furthermore, the turbidity-compensation model was established by the linear fitting of the difference spectra of various levels of turbidity. The absorption spectra of the nitrate were extracted by subtracting the turbidity-compensation curve from the original spectra of the water samples, and the nitrate concentration was calculated by using a partial least squares (PLS)-based nitrate-prediction model. The experimental results showed that the average relative error of the nitrate predictions was reduced by 50.33% to 1.33% by the proposed turbidity-compensation method. This indicated that this method can better correct the deviation in nitrate's absorbance caused by turbidity and improve the accuracy of nitrate predictions.

Online water quality monitoring based on UV-Vis spectrometry and artificial neural networks in a river confluence near Sherfield-on-Loddon

Water quality monitoring is very important in agricultural catchments. UV-Vis spectrometry is widely used in place of traditional analytical methods because it is cost effective and fast and there is no chemical waste. In recent years, artificial neural networks have been extensively studied and used in various areas. In this study, we plan to simplify water quality monitoring with UV-Vis spectrometry and artificial neural networks. Samples were collected and immediately taken back to a laboratory for analysis. The absorption spectra of the water sample were acquired within a wavelength range from 200 to 800 nm. Convolutional neural network (CNN) and partial least squares (PLS) methods are used to calculate water parameters and obtain accurate results. The experimental results of this study show that both PLS and CNN methods may obtain an accurate result: linear correlation coefficient (R²) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R² between predicted value and true values of TSS concentrations is 0.827 with PLS model and 0.915 with CNN model. CNN method may obtain a better linear correlation coefficient (R²) even with small number of samples and can be used for online water quality monitoring combined with UV-Vis spectrometry in agricultural catchment.

High sensitivity and wide range chlorophyll-a determination by simultaneous measurement of absorbance and fluorescence using a linear CCD

In the determination of chlorophyll with the fluorescence method in the natural water, the suspended particles and colloids will seriously interfere with the incident light and the fluorescence. Based on the analysis of the interaction between light and the measured substances, a high sensitivity, wide range of chlorophyll-a concentration measurement strategy, which combines optical information of fluorescence and absorbance with the CCD integration time transformation method, is proposed. Correspondingly, a novel algorithm, which can significantly correct the attenuation of incident light due to the absorption of suspended particles and the deviation of detected fluorescence caused by the scattered light and reflected light, is proposed to realize turbidity compensation. For verification, a self-designed compact optical experimental device consisting of a single LED and a linear CCD was set up to obtain the fluorescence spectrum and absorbance spectrum simultaneously. The experimental results demonstrate that the compensation strategy can commendably compensate for the impact of the suspended particles. The relative error of chlorophyll-a measurement is less than 5%, even in a high turbidity environment. Furthermore, the minimum detection limit is significantly reduced from conventional 0.01 μg/L to 0.0025 μg/L in the range of 0.0025-130 μg/L with the CCD integration time transformation method, which improves the measurement sensitivity. This device and method have the potential to be applied to the in situ online measurement of chlorophyll-a concentration in natural water.