In Situ Determination of Nitrate in Water Using Fourier Transform Mid-Infrared Attenuated Total Reflectance Spectroscopy Coupled with Deconvolution Algorithm

Abstract

Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy has been used to determine the nitrate content in aqueous solutions. However, the conventional water deduction algorithm indicated considerable limits in the analysis of samples with low nitrate concentration. In this study, FTIR-ATR spectra of nitrate solution samples with high and low concentrations were obtained, and the spectra were then pre-processed with deconvolution curve-fitting (without water deduction) combined with partial least squares regression (PLSR) to predict the nitrate content. The results show that the typical absorption of nitrate (1200-1500 cm^-1) did not clearly align with the conventional algorithm of water deduction, while this absorption was obviously observed through the deconvolution algorithm. The first principal component of the spectra, which explained more than 95% variance, was linearly related to the nitrate content; the correlation coefficient (R²) of the PLSR model for the high-concentration group was 0.9578, and the ratio of the standard deviation of the prediction set to that of the calibration set (RPD) was 4.22, indicating excellent prediction performance. For the low-concentration group model, R² and RPD were 0.9865 and 3.15, respectively, which also demonstrated significantly improved prediction capability. Therefore, FTIR-ATR spectroscopy combined with deconvolution curve-fitting can be conducted to determine the nitrate content in aqueous solutions, thus facilitating rapid determination of nitrate in water bodies with varied concentrations.

Keywords: Fourier transform attenuated total reflection; curve-fitting; deconvolution; nitrate; partial least squares; water bodies.

Figure 1

Fourier transforms mid-infrared attenuated total reflectance (FTIR-ATR) spectra of nitrate solutions. (a) High-concentration group; (b) low-concentration group.

Figure 2

Characteristic absorption bands of nitrate solutions through water deduction ((a) high-concentration group; (b) low-concentration group; n = 44) and deconvolution from raw spectra ((c) high-concentration group; (d) low-concentration group; n = 48).

Figure 3

Principal component distribution of FTIR-ATR spectra of the nitrate solution after deconvolution ((a) high-concentration group; (b) low-concentration group).

Figure 4

Distribution and model evaluation of the partial least squares (PLS) factor (a,d), training set ((b,e); n = 33), and testing set ((c,f); n = 11) of the partial least squares regression (PLSR) prediction model (without water deduction) for high nitrate solutions (a–c) and low nitrate solutions (d–f). Note: RMSECV, root mean square error in cross validation; RMSEC, root mean square error of calibration; RMSEP, root mean square error of prediction.

Figure 5

Distribution and model evaluation of the PLS factor (a,d), training set ((b,e); n = 36), and testing set ((c,f); n = 12) of the PLSR prediction model with deconvolution (without water deduction) for high nitrate solutions (a–c) and low nitrate solutions (d–f). Note: RMSECV, root mean square error in cross validation; RMSEC, root mean square error of calibration; RMSEP, root mean square error of prediction.