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.

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.

Chrysanthemum (Chrysanthemum morifolium Ramat.) is one of the eight well-known traditional medicinal herbs ‘Zhe Ba Wei’ in China. Its flowers are used in the production of medicine for antitumor, a...

Winterberry holly (Ilex verticillata) is an economically valuable landscaping ornamental plant. Serious outbreaks have been reported, in its leaf tips curl upward, irregular black brown spots appear on leaves, and extensive defoliation is commonly observed. The incidence in Hangzhou was estimated at 50% and resulted in large economic losses for growers in 2018. Samples were collected from the main cultivation area in Zhejiang Province. In total, 11 fungal isolates were obtained from diseased leaves through a single-spore purification method, and isolate LVY 9 exhibited strong pathogenicity. Based on morphology and molecular phylogenetic analyses based on multilocus sequence typing of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH), internal transcribed spacer (ITS) regions, actin (ACT), calmodulin (CAL), and chitin synthase (CHS-1) genes, we identified the pathogen as Colletotrichum siamense, causative agent of anthracnose of winterberry holly.

Nitrogen nitrates play a significant role in the soil’s nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate–nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with $R_c^2$ = 0.998, $R_p^2$ = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.