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以148份肉鸡粪和菊花渣工厂化高温堆肥过程样品为研究对象,分别探讨了基于基本理化指标和近红外光谱预测鸡粪堆肥过程中有机质含量的可行性。根据样品中干物质含量、pH值和电导率3种理化指标与有机质含量的相关关系建立了基于理化指标预测有机质含量的一元和二元线性回归模型。结果表明,利用干物质含量预测有机质含量简便、易行且最具实际应用价值(R2=0.81,P<0.001)。采用多元线性回归、主成分回归和偏最小二乘回归3种定量分析方法分别建立了鸡粪堆肥过程有机质含量的近红外预测模型。其中,主成分回归和偏最小二乘定量分析方法所得预测模型的验证决定系数(r2)均为0.95,验证相对分析误差(RPD)均大于4.0,所建模型的预测精度较高,可用于实际检测工作。
The 148 high-temperature composting samples of broiler manure and chrysanthemum residue were taken as research objects. The feasibility of predicting the organic matter content of chicken manure composting based on basic physical and chemical indicators and near-infrared spectroscopy was discussed. According to the correlation between the three physical and chemical indexes and organic matter content of dry matter content, pH value and conductivity in the sample, the linear and linear regression models based on physical and chemical indexes to predict the organic matter content were established. The results showed that the prediction of organic matter content by dry matter content was simple, easy and practical (R2 = 0.81, P <0.001). Three kinds of quantitative analysis methods of multiple linear regression, principal component regression and partial least squares regression were used to establish the near-infrared prediction model of organic matter content in composting of chicken manure. Among them, the principal component regression and partial least squares quantitative analysis of the predictive model validation coefficients (r2) were 0.95, verify the relative analysis error (RPD) are greater than 4.0, the model has a high prediction accuracy, can be used in practice Testing work.