李 彬.高效液相色谱法测定Fapas能力验证样品中咖啡因含量的不确定度评定[J].食品安全质量检测学报,2020,11(8):2511-2517 |
高效液相色谱法测定Fapas能力验证样品中咖啡因含量的不确定度评定 |
Uncertainty evaluation of determination of caffeine in Fapas proficiency testing samples by high performance liquid chromatography |
投稿时间:2020-02-17 修订日期:2020-04-21 |
DOI: |
中文关键词: 不确定度 咖啡因 Fapas 高效液相色谱法 |
英文关键词:uncertainty caffein Fapas high performance liquid chromatography |
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中文摘要: |
目的 评定高效液相色谱法测定Fapas能力验证项目中咖啡因含量的不确定度。方法 依据国家计量技术规范JJF 1059.1-2012《测量不确定度评定与表示》对研磨咖啡粉(Sample A)和去咖啡因研磨咖啡粉(Sample B)2个样品中咖啡因含量测定的不确定度来源进行分析。通过建立数学模型量化不确定度分量, 计算合成不确定度和扩展不确定度, 并与Fapas能力验证结果进行对比。结果 Sample A结果报告为(13.36±0.21) g/kg, k=2; Sample B结果报告为(265.0±5.1) mg/kg, k=2, 且Fapas能力验证结果为满意; 2个样品咖啡因含量测量不确定度均主要来源于标准曲线配制过程、样品重复性和标准曲线拟合3部分。结论 该研究可为评价咖啡因含量测定结果的质量和控制实验室内部质量提供参考; 同时不确定度评定和能力验证结果表明实验室在不同的浓度水平上均有能力开展相关检测并能保证数据可靠。 |
英文摘要: |
Objective To evaluate the uncertainty of determination of caffeine in Fapas proficiency testing (PT)samples using high performance liquid chromatography. Methods The sources of uncertainty in caffeine determination in both ground coffee (Sample A) and decaffeinate ground coffee (Sample B) samples were analyzed according to the national measurement technical specification JJF 1059.21-2012 Evaluation and expression of uncertainty in measurement. The combined uncertainty and extended uncertainty were calculated by establishing mathematical model, and the results were compared with Fapas PT report. Results The result report of Sample A was (13.36±0.21) g/kg, k=2, while Sample B was (265.0±5.1) mg/kg, k=2. Both results were satisfactory according to PT report. And both of the main uncertainty sources were from the standard solution preparation, the repeatability of sample measurement, and the fitting of the standard curve. Conclusion The results provide references to evaluate the quality of caffeine determination and control lab internal quality. Uncertainty evaluation and PT results show that the laboratory is capable of carrying out relevant tests at different concentration levels and can guarantee the reliability of data. |
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