基于灰色模型的蔬菜农药残留量不确定度评定

针对检测值的分布函数未知时,蔬菜农药残留量的不确定度存在难以评定的问题,提出了一种基于灰色模型的评定方法。采用气相色谱法测定蔬菜中9种有机氯类和拟除虫菊酯类农药残留量;将农药残留量检测看作灰色过程,构建蔬菜农残量不确定度的灰色评定数学模型,并用该模型对9种有机氯类和拟除虫菊酯类农药残留量的不确定度进行评定;与蔬菜农残量不确定度的统计评定结果进行对比。结果表明:基于灰色模型的评定方法能够较快计算蔬菜农残量的不确定度,与统计学标准差计算值之间无显著性差异,两者结果的相关性极强(相关系数为0.997)。该方法用于评定小样本、分布类型未知的蔬菜农残量的不确定度是科学可行的。

Uncertainty evaluation of pesticide residues in vegetable based on gray model

The uncertainty of pesticide residues in vegetables is an important indicator to judge whether the detection of pesticide residues is good or not in quality, which is too difficult to evaluate if the distribution function of the detected value is unknown. This study proposes an evaluation method based on the gray model to solve this problem. Steps are adopted as follows: first of all, detect the nine sorts of organochlorine and pyrethroid pesticide residues in vegetables by use of gas chromatography; secondly, establish, taking the detection of pesticide residues as a kind of gray process, the mathematical model of gray evaluation on the uncertainty of pesticide residues in vegetables; thirdly, use the model to evaluate the uncertainty of the above nine pesticide residues; and finally, make a comparison of the statistical result in the residues with that of the evaluation on the uncertainty. It is indicated that the evaluation by gray model is faster to calculate the uncertainty of the residues, and the correlation between those two results is 0.997 with high relativity if compared with the calculation value of the statistical standard deviation. And it is concluded that the method of evaluation is suitable for the detection of small samples and unknown distribution types with the advantages of fast, accurate and reliable in calculation, which has good effect in the application.