Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH₄-N) and nitrate nitrogen (NO₃-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH₄-N and NO₃-N in different soils were found at 978 cm⁻¹ and 1044 cm^−1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation (${\mathrm{R}}_{\mathrm{p}}^2$) of multiple linear regression prediction models for NH₄-N and NO₃-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400–2000 cm⁻¹ were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the ${\mathrm{R}}_{\mathrm{p}}^2$ was above 0.99, the root mean square errors of prediction (RMSE_P) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH₄-N), sodium nitrate (nitrate nitrogen, NO₃-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH₄-N, R²_p = 0.92, RMSE_P = 0.77% and RPD = 3.63; for NO₃-N, R²_p = 0.92, RMSE_P = 0.74% and RPD = 4.17; for urea-N, R²_p = 0.96, RMSE_P = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.

Nitrite is harmful to people and animals when it is excessive in an environment. Traditional detection methods are time-consuming and are generally restricted by sensitivity. In this study, a simple and efficient electrochemical sensor made of a glassy carbon electrode (GCE), modified with MoS₂ nanosheets/carboxylic multiwall carbon nanotubes (MoS₂/MWCNT-COOH), was used to detect nitrite. Cyclic voltammetry (CV) was used for drawing the standard curve of nitrite. The properties of the modified materials were analyzed by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and electrochemical impedance spectroscopy (EIS). The modified electrode presents a great response to nitrite, shows a wide sensing range (10–10,000 μM) and shows a low detection limit (3.6 μM). The characterization of nanomaterials indicates that MoS₂/MWCNT-COOH has a big surface area (150.3 m² g⁻¹) and abundant pores (pore volume is 0.7085 cm³ g⁻¹). In addition, the sensor shows high sensitivity (0.35 μA μM⁻¹ cm⁻²), good reproducibility (RSD is 2.2%), and good stability (the responding current only decreased about 4% after 2 weeks). Therefore, the MoS₂/MWCNT-COOH-modified electrode is a potential analytical method in nitrite determination.