Validating phase quantification procedures of powder X-ray diffraction (XRD) data for an implementation in an ISO/IEC 17025 accredited environment has been challenging due to a general lack of suitable certified reference materials. The preparation of highly pure and crystalline reference materials and mixtures thereof may exceed the costs for a profitable and justifiable implementation. This study presents a method for the validation of XRD phase quantifications based on semi-synthetic datasets that reduces the effort for a full method validation drastically. Datasets of nearly pure reference substances are stripped of impurity signals and rescaled to 100% crystallinity, thus eliminating the need for the preparation of ultra-pure and -crystalline materials. The processed datasets are then combined numerically while preserving all sample- and instrument-characteristic features of the peak profile, thereby creating multi-phase diffraction patterns of precisely known composition. The number of compositions and repetitions is only limited by computational power and storage capacity. These datasets can be used as input files for the phase quantification procedure, in which statistical validation parameters such as precision, accuracy, linearity, and limits of detection and quantification can be determined from a statistically sound number of datasets and compositions.

中文翻译：

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使用半合成数据验证 XRD 相量化

由于普遍缺乏合适的认证参考材料，在 ISO/IEC 17025 认证环境中验证粉末 X 射线衍射 (XRD) 数据的相位量化程序一直具有挑战性。高纯度和结晶参考物质及其混合物的制备可能会超过盈利和合理实施的成本。本研究提出了一种基于半合成数据集验证 XRD 相量化的方法，该方法大大减少了完整方法验证的工作量。几乎纯参考物质的数据集被去除杂质信号并重新调整为 100% 结晶度，从而消除了制备超纯和结晶材料的需要。然后将处理后的数据集进行数字组合，同时保留峰轮廓的所有样品和仪器特征，从而创建精确已知成分的多相衍射图案。组合和重复的数量仅受计算能力和存储容量的限制。这些数据集可用作相位量化程序的输入文件，其中统计验证参数，如精度、准确度、线性以及检测和量化的限制可以从统计上合理的数据集和组成数量中确定。