在室内条件下提取了苯乙烯在不同土壤中的光谱诊断波段及其范围,并以其作为土壤中苯乙烯识别及其含量预测的依据。采用微分处理法与光谱数据转换法对土壤光谱反射率进行处理,以增大样品之间的光谱变化差异,并采用多元线性回归(SMLR)、偏最小二乘回归(PLSR)和支持向量机(SVMR)方法建模以预测不同土壤中的苯乙烯含量。结果表明,受苯乙烯污染的不同土壤的光谱特征分别位于1800,2200,2400 nm附近;受自身理化性质及苯乙烯含量影响,土壤光谱反射率的降速先增大后减小,直至苯乙烯在土壤中饱和,反射率变化趋于稳定。PLSR模型对土壤中苯乙烯含量的预测效果最优,SMLR模型次之,SVMR模型较差。PLSR模型的决定系数为0.982~0.998,模型稳定性强,其校正标准差与预测标准差的差值为0.004~0.016,模型预测精度高。

The spectral diagnostic bands and their ranges of styrene in different soils are extracted under indoor conditions and used as the basis for the identification and content prediction of styrene in soil. The soil spectral reflectance is processed by the differential processing method and the spectral data conversion method to increase the difference in spectral change among samples. The stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), and support vector machine regression (SVMR) methods are used to model and predict the styrene content in different soils. The results show that the spectral characteristics of different soils contaminated by styrene are located near 1800, 2200, and 2400 nm, respectively. Under the influence of its physical and chemical properties and styrene content, the decrease rate of the soil spectral reflectance increases first and then decreases until the styrene is saturated in soils, and the change in reflectivity tends to be stable. The PLSR model has the best prediction effect on the styrene content in soils, followed by the SMLR model, and the SVMR model has the worst effect. The determination coefficient of the PLSR model is 0.982--0.998, indicating that the model is stable, and the difference between the corrected and predicted standard deviations is 0.004--0.016, indicating that the model has high prediction accuracy.