Conventional versus Extended Standard Addition Method: Determination of Capsaicinoids in Topical Creams by High-Performance Liquid Chromatography – Diode Array Detection (HPLC-DAD)

分析物 色谱法 化学 校准 基质(化学分析) 标准加入 检出限 样品制备 高效液相色谱法 分析化学(期刊) 标准溶液 数学 统计
作者
Anca E. Anghel,Ibolya Balazs,Augustin C. Moț
出处
期刊:Analytical Letters [Taylor & Francis]
卷期号:56 (2): 200-215
标识
DOI:10.1080/00032719.2022.2042010
摘要

External standard calibration is the most common approach used to calculate the unknown concentration of analytes in analytical procedures. However, this method is accurate only if the matrix can be reproduced in the standard samples or if there are no significant interferences in the sample. A well-known calibration method that was proposed especially for the analysis of samples with matrix effects is conventional standard addition method (SAM) which implies adding known and increasing quantities of the analyte to portions of the sample. Despite its benefit of constant matrix effect in the calibration standards, SAM has been frequently shown to be a suboptimal approach with flaws that include extrapolation prediction and thus wider confidence intervals and non-linearity at lower concentrations. Recently, new approaches have been proposed to overcome these shortcomings, including extension of the SAM by blank addition, a geometric alternative for interpolation, and even a new mathematical alternative. Herein, we applied an alternative to extend the SAM method, for semisolid samples with unavailable blank, by adding more samples with lower mass than the unknown in the SAM, and thus extending measurable points below the intercept. This approach was applied for capsaicinoid determination in topical creams after liquid-liquid microextraction, followed by high performance liquid chromatography with diode array detection (HPLC-DAD). Microextraction of the capsaicinoids was optimized in terms of pH, solvent type, vortexing and extraction times. The method appeared to significantly improve the precision, limit of detection, and lead to reduced bias compared to conventional SAM.

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