Rapid and Quantitative Determination of Soil Water-Soluble Nitrogen Based on Surface-Enhanced Raman Spectroscopy Analysis
Source abstract
Abstract
An accurate and rapid determination of soil water-soluble nitrogen is conducive to scientific fertilization in precision agriculture. Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive fingerprint with the advantages of simple operation and high detection efficiency. In this paper, partial least squares (PLS), principal components analysis (PCA), and least squares supports vector machine (LS-SVM) were applied to analyze the relationship between soil water-soluble nitrogen concentration and SERS. The results showed that the SERS-enhancing effect based on Opto Trace Raman 202 (OTR 202) was better than that of silver nanosubstrate and gold nanosubstrate. In addition, the prediction accuracy of soil water-soluble nitrogen in PLS was the highest ( R p 2 = 0.91 , RMSE p = 8.76 mg / L , R P D = 3.00 ) when the original spectra were preprocessed with first-derivative. Moreover, 1028, 1370, 1436, and 1636 cm−1 could be determined as characteristic peaks of soil water-soluble nitrogen, the association between soil water-soluble nitrogen concentration and a SERS intensity of 1370 cm−1 was the highest ( R p 2 = 0.94 ) , and the regression equation was y = 93.491x + 1771.5. Beyond that, the prediction accuracy of distinguishing between a low soil water-soluble nitrogen concentration (22.7–63.7 mg/L) and a high soil water-soluble nitrogen concentration (70.5–118.3 mg/L) based on PCA and LS-LVM was 86.67%. In conclusion, soil water-soluble nitrogen could be detected rapidly and quantitatively using SERS, which was beneficial to provide a rapid, accurate, and reliable scheme for scientific and precise fertilization.
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Research context
OpenAlex topics: Spectroscopy Techniques in Biomedical and Chemical Research · Gold and Silver Nanoparticles Synthesis and Applications · Melamine detection and toxicity
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, P.R. China
College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China
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