偏最小二乘回归
拉曼光谱
化学计量学
土壤有机质
生物系统
衰减全反射
融合
光谱学
主成分分析
人工智能
模式识别(心理学)
分析化学(期刊)
计算机科学
土壤水分
环境科学
土壤科学
化学
红外光谱学
机器学习
色谱法
光学
物理
量子力学
语言学
哲学
有机化学
生物
作者
Zhe Xing,Changwen Du,Yazhen Shen,Fei Ma,Jianmin Zhou
标识
DOI:10.1016/j.compag.2021.106549
摘要
Determination of soil organic matter (SOM) is extremely important for diagnosing the fertility status of agricultural soils. Thus, fast and efficient approaches are needed to aid soil fertilization assessment. In this work, the method proposed is based on the combination of mid-infrared attenuated total reflection (FTIR-ATR) and dispersive Raman spectroscopy, as a rapid and nondestructive alternative to traditional chemical analysis. The ability of both two individual and the fused spectroscopy in SOM prediction was tested. Partial least squares regression (PLSR) was used to construct predictive models to correlate soil spectra with SOM content. Simple data fusion was accomplished by concatenating the principal components of the two spectra. The predictive performance was not essentially improved, and even decreased for the fused ATR-Raman spectra based on the simple fusion strategy. Better results were obtained with the advanced method of data fusion, which is, concatenating the selected variables of the two spectroscopic techniques after the step of variable selection by competitive adaptive reweighted sampling (CARS). The results showed that the RMSEP of the prediction model was decreased using both the individual and fused spectra data, combining with the CARS algorithm. Models based on fused spectra data with selected variables had the best performance in accuracy of SOM prediction. Therefore, the fused technology of ATR and Raman spectroscopy is a promising approach to predict soil properties, such as SOM, with the advantage of simple preparation of soil samples.
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