An analytical method based on GC–MS was developed for the determination of a wide panel of urinary estrogens, together with their principal metabolites. Because of the low concentration of estrogens in urine, an efficient sample pre‐treatment was optimized by a design of experiment (DoE) procedure to achieve satisfactory sensitivity. A second DoE was built for the optimization of the chromatographic run, with the purpose of reaching the most efficient separation of analytes with potentially interfering ions and similar chromatographic properties. The method was fully validated using a rigorous calibration strategy: from several replicate analyses of blank urine samples spiked with the analytes, calibration models were built with particular attention to the study of heteroscedasticity and quadraticity. Other validation parameters, including the limit of detection, intra‐assay precision and accuracy, repeatability, selectivity, specificity, and carry‐over, were obtained using the same set of data. Further experiments were performed to evaluate matrix effect and extraction recovery. Then the urinary estrogen profiles of 138 post‐menopausal healthy women were determined. These profiles provide a representation of physiological concentration ranges, which, in forthcoming studies, will be matched on the base of multivariate statistics with the urinary estrogenic profile of women with breast or ovarian cancer.

中文翻译：

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优化和验证GC-MS定量方法以确定人尿中扩展的雌激素谱：健康女性人群的变异区间

开发了一种基于GC-MS的分析方法，用于测定各种尿雌激素及其主要代谢物。由于尿液中的雌激素浓度较低，因此通过实验设计（DoE）程序对有效的样品预处理进行了优化，以实现令人满意的灵敏度。建立第二个DoE来优化色谱运行，以最有效地分离具有潜在干扰离子和相似色谱特性的分析物。该方法已使用严格的校准策略进行了充分验证：通过对加标有分析物的空白尿样进行多次重复分析，建立了校准模型，并特别注意异方差和二次方的研究。其他验证参数 包括检测限，测定内精密度和准确性，可重复性，选择性，特异性和残留物，是使用同一组数据获得的。进行了进一步的实验，以评估基质效应和提取回收率。然后确定了138名绝经后健康女性的尿雌激素谱。这些特征提供了生理浓度范围的代表，在即将进行的研究中，将在多变量统计的基础上将其与乳腺癌或卵巢癌女性的尿雌激素特征相匹配。然后确定了138名绝经后健康女性的尿雌激素谱。这些特征提供了生理浓度范围的代表，在即将进行的研究中，将在多变量统计的基础上将其与乳腺癌或卵巢癌女性的尿雌激素特征相匹配。然后确定了138名绝经后健康女性的尿雌激素谱。这些特征提供了生理浓度范围的代表，在即将进行的研究中，将在多变量统计的基础上将其与乳腺癌或卵巢癌女性的尿雌激素特征相匹配。