孙明.基于比色法和光谱法的土壤中磷的快速检测 方法研究[J].食品安全质量检测学报,2016,7(11):4478-4483
基于比色法和光谱法的土壤中磷的快速检测 方法研究
Rapid determination of phosphorus content in soil based on colorimetry and spectroscopy
投稿时间:2016-09-08  修订日期:2016-11-03
DOI:
中文关键词:  比色法  光谱法  快速检测  磷钼蓝反应  土壤养份测试仪
英文关键词:colorimetry  spectroscopy  rapid detection  phosphorus molybdenum blue reaction  soil nutrient tester
基金项目:国家星火计划项目(2015GA620012)
作者单位
孙明 中国农业大学信息与电气工程学院, 现代精细农业系统集成研究教育部重点实验室, 农业部农业信息获取技术重点实验室 
AuthorInstitution
SUN Ming College of Information and Electrical Engineering, China Agricultural University, Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture 
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中文摘要:
      目的 研发土壤中磷素的快速检测方法。方法 针对土壤中磷含量的快速检测, 利用比色法和光谱法构建了磷钼蓝反应的吸收曲线模型, 并对一种智能土肥养份综合测试仪进行了测试。结果 在检测波长为870 nm时, 将2.0 mL的26 g/L的钼酸铵溶液、1.0 mL的100 g/L抗坏血酸溶液和0.4 g/L的EDTA添加到酸性反应体系中, 通过使用紫外-可见分光光度处理所测得的数据, 可知磷溶液浓度与吸光度之间呈线性关系, 得到线性回归模型为Y=0.01558X?0.1106, 决定系数(r2)=0.995。对这种智能土肥养份综合测试仪进行准确性测试, 线性偏差在3%内, 通过与所构建的线性回归模型的计算值进行对比, 预测值误差在4%以内。结论 所构建的线性回归模型是可用的, 这种智能土肥养份综合测试仪可满足土壤中磷的快速检测要求。
英文摘要:
      Objective To research a rapid detection method of phosphorus in soil. Methods For rapidly detecting the phosphorus content in soil, the absorption curve model of phosphorus molybdenum blue reaction by using the colorimetric method and the spectral method was established, and a kind of intelligent soil nutrient tester was tested. Results At the detection wavelength 870 nm, by adding 2.0 mL 26 g/L ammonium molybdate solution, 1.0 mL 100 g/L ascorbic acid solution with 0.4 g/L EDTA in the reaction system, based on the data measured by a UV-visible spectrophotometer, the absorbance was linear with the concentration of the phosphorus solution, and the linear regression model was Y=0.01558X-0.1106, correlation coefficient (r2)=0.995. For the intelligent soil nutrient tester, the accuracy test was taken and the linear deviation was within 3%. And by comparing the calculated values with the established linear regression model, the predicted error was within 4%. Conclusion The established linear regression model is available and the intelligent soil nutrient tester can meet the requirements of the rapid detection of phosphorus in soil.
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