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 (R2 p) 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 R2 p was above 0.99, the root mean square errors of prediction (RMSEP) 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.

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.