样本前处理是影响离子选择电极测土时效性的关键环节。以缩短静置时间为目的,该文探讨了高分子絮凝剂Superfloc127用于土壤硝态氮浸提前处理操作的可行性。首先,基于相界电位模型对Superfloc127加入土样硝态氮浸提液后电极反应动力学过程进行建模分析,理论可行性验证后,开展测土试验,分析其对自制全固态电极(PPy-NSISE)及商用电极(PVC-NISE)电化学性能的影响。结果表明:使用Superfloc127后,样本制备时间由1.5 h缩短至10 s;PPy-NSISE及PVC-NISE响应斜率分别为-51.4和-52.1 m V/Decade,检测灵敏度无明显改变,测土结果与标准结果之间的决定系数分别为0.69和0.31;连续测定12 h后,PPy-NSISE和PVC-NISE的响应斜率分别降低至-35.1和-25.4 m V/Decade,性能不可恢复。通过理论解析及大量分析试验证明,絮凝剂Superfloc127无法应用在基于离子选择电极法的土壤硝态氮快速分析,提高土壤前处理效率的研究工作仍需深入开展。
Fast detection of soil nitrate has an important significance for variable rate fertilization.As NO-3ion selective electrode is easily affected by chloride ion and temperature interference in its practical application for soil nutrient test,a three-layer artificial neural network model optimized by response surface methodology(RSM)was designed to reduce the interference of Cl-on NO-3ion selectivity electrode(ISE)in soil nitrate detection.The output of the model was NO-3-N concentration and three input parameters were temperature,response potential of ISE-NO-3and of ISE-Cl-.A multi-layer feed-forward(MLFF)network with one hidden layer and using gradient descent with momentum(GDM)as learning algorithm was used to develop the error correct model.By response surface methodology,a multivariate quadratic equation was developed to quantitatively describe the relationship between mean absolute error(MAE)and topological parameters of the artificial neuron network(ANN)model,then the optimum number of hidden neurons,momentum coefficient,training epoch,step size,and training runs were found.In range of 10 to 40℃,the best ANN model can correct interference of Cl-within 250 mg/kg while the primary ion concentration ranging from 5 to 250 mg/kg.For practical soil nutrient detection,the MAE could reach 8.6 mg/kg and relative standard deviation was lower than 6.5%compared with16%of no model correction.
