基于ISE的土壤硝态氮原位检测模型比较

Comprison of Detection Models for Soil Nitrate Concentration Based on ISE

针对离子选择电极预测土壤硝态氮浓度易受土壤悬液组分影响的问题,以提高离子选择电极预测土壤硝态氮浓度精准度为目标,探讨了Nernst、SAM、BP-ANN 3种模型的浓度预测性能。开展标准溶液检测实验,预判3个模型的预测性能,结合田间玉米监测实验和盆栽粉冠番茄监测实验验证样本实验结果。实验结果表明,3个模型的预测结果与样本真值均具有较好的一致性。其中,SAM模型的浓度预测结果最为精确,其决定系数均不小于0.9,且MAE、MRE、RMSE分别为2.03~5.08 mg/L、0.64%~8.79%、2.21~5.49 mg/L。SAM浓度预测模型具有精度较高、抗干扰性好的特点,对基于ISE的土壤硝态氮原位检测具有一定的参考价值。

There are a variety of ions in soil suspension,so when using ion selective electrode to detect soil nitrate-nitrogen,it is particularly vulnerable to the interference of other ions,which will greatly affect the accuracy of detection.In order to improve the accuracy of monitoring soil nitrate concentration by ion selective electrode,it is necessary to establish a model to predict the concentration.The prediction performances of Nernst,SAM and BP-ANN models for soil nitrate concentration detection were discussed.The standard solution test was carried out to predict the prediction performance of the three models.Combined with the field corn monitoring experiment and potted pink crown tomato monitoring experiment to verify the sample experimental results,the soil nitrate concentration was taken as the output of the three models,and the effect of optical method on the determination of soil nitrate concentration was compared.The experimental results showed that the prediction results of the three models were in good agreement with the true values of the samples.Among them,the SAM model was the most accurate,and its correlation coefficients were not less than 0.9.The variation ranges of MAE,MRE and RMSE were 2.03~5.08 mg/L,0.64%~8.79%and 2.21~5.49 mg/L,respectively.SAM concentration prediction model had the characteristics of high precision and good anti-interference.It can run on a relatively simple platform and had a wider range of application.In addition,when combined with the fluid control system,it can ensure the detection accuracy and improve the detection efficiency.It had a certain reference value for in-situ detection of soil nitrate nitrogen based on ISE.