拉曼光谱在精细农业土壤成分快速检测中的研究进展

拉曼光谱利用分子运动对入射光产生非弹性散射的原理对分子成分进行检测，具有受水分干扰小、样本预处理小、与红外光谱信息互补等特点，在土壤成分快速分析方面展现了很大的优势。但是拉曼光谱信号弱，受荧光干扰强，为土壤拉曼信号的有效获取带来困难。为了分析拉曼光谱在土壤成分检测中的应用潜力，该研究综述了移频激发差分拉曼光谱技术、共焦显微拉曼技术以及表面增强技等基于拉曼光谱的土壤成分检测技术，分析了土壤成分拉曼光谱检测的研究进展，并提出进一步研究建议。结果表明：1）脂肪族化合物以及芳香族化合物都具有拉曼活性，为基于拉曼光谱的土壤有机质含量的定性、定量分析提供了理论依据。为了弥补拉曼光谱对有机质整体定量预测精度的不足，采用红外-拉曼光谱融合方式补偿单独拉曼光谱数据中缺失的土壤有机质信息，可显著改善预测精度。2）利用表面增强技术可以增强土壤溶液中可溶性氮与土壤有效氮拉曼特征波峰的强度，获得了良好的定量预测效果，回归模型决定系数R2达到0.91～0.99。3）土壤中很多含磷的化合物都具有拉曼活性，拉曼光谱是识别土壤中不同磷酸盐形态的极其有效的工具，在土壤磷素含量的分析中，应用小波包分解的拉曼光谱对滤除有机质的磷酸盐参杂土壤中磷素浓度进行预测，回归模型精度R2达到0.94。拉曼光谱检测的样本范围取决于激发光照射在样本上的光点尺寸，而土壤样本的空间变异性为聚焦目标物质带来困难。因此，实现现场高分辨率检测的关键是获取有效拉曼信号、同时降低背景信号的干扰。移频激发技术与显微拉曼技术为农田土壤养分的原位测量提供了技术保障。建议：1）采用光谱融合方法提升回归模型的预测精度。2）降低冗余变量，提升模型的可解读性与重现性。3）充分考虑土壤对拉曼光谱的影响，为开发农田现场土壤成分快速监测技术提供参考。

Research progress on the rapid detection of soil components using Raman spectroscopy: A review

The current measurement of soil properties can be limited in the variable-rate fertilizer application using soil analysis, due to the labor-intensive and time-consuming in the laboratory. Rapid, non-destructive soil detection is highly required to be developed using visible-infrared spectroscopy. Among them, Raman spectroscopy can be used to detect the vibrational properties of molecules, due to the fast, non-invasive, small sample treatment, and free interference from water in various fields. Nevertheless, incident light is also required to induce the Raman scattering, in order to compensate for the information with infrared spectroscopy. Therefore, the Raman scattering measurement can be widely used in the composition and dynamic process of soil minerals, bacteria colonies, and humic fraction in soil sensing. However, it is still lacking on the fluorescence effect of soil in the sensing of soil nutrients, which can weaken the Raman signals and the information extraction for the quantitative analysis. Raman spectroscopy has been introduced into some novel measurement techniques to improve the signal-to-noise ratio in recent years, including the shifted excitation Raman difference spectroscopy (SERDS), confocal Raman microscope, and surface-enhanced Raman spectroscopy (SERS). Meanwhile, both aliphatic and aromatic compounds are Raman active in the quantitative and qualitative detection of soil organic matter (SOM). Moreover, many phosphorous compounds in soil are also Raman active, leading to low prediction accuracy using visible-near infrared spectroscopy. In this review, the research progress was proposed on the Raman spectroscopy in the rapid detection of soil nutrients, together with the technical means in suppressing soil fluorescence interference to obtain high-resolution Raman signals. In the detection of SOM, the fusion of infrared and Raman spectral data significantly improved the prediction accuracy of 43% in the root mean square error (RMSE). In addition, the SERDS technique was used to detect the SOM of 33 soil samples. The reconstructed Raman peaks of soil minerals and organic materials obtained an excellent prediction accuracy of the SOM, with the determination of coefficient R2=0.82, and the residual prediction deviation RPD=1.81. Raman spectroscopy was used to detect the water-soluble nitrogen in the soil solution, whereas, the SERS was to enhance the Raman peaks. An excellent correlation was obtained between the concentration of water-soluble nitrogen and the SERS data (R2=0.91). Research showed that Raman spectroscopy can be an effective tool to identify the different phosphate species in soil. Wavelet packet decomposition of Raman spectra was used to predict the phosphorus concentration in the phosphate-mixed soil with the SOM leached, where the accuracy of the regression model reached R2=0.94. Since the measurement area of Raman spectroscopy depended mainly on the spot size of the laser that irradiated on the sample surface, the spatial variability of the soil sample can be difficult to focus on the target substance. The effective Raman signal of soil nutrients can be obtained with high spatial resolution while suppressing the interferences from the background light. The combination of SERDS and micro-Raman technologies can be expected to serve as an in-situ measurement of soil nutrients. The spectral fusion can be reduced the redundant variable since the interpretability and reproducibility of the prediction model are paramount in the sensor development using Raman spectroscopy.