Jahrman Evan P, Yu Lee L, Krekelberg William P, Sheen David A, Allison Thomas C, Molloy John L
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
J Anal At Spectrom. 2022;37(6). doi: 10.1039/d1ja00445j.
The toxicity and bioavailability of arsenic is heavily dependent on its speciation. Therefore, robust and accurate methods are needed to determine arsenic speciation profiles for materials related to public health initiatives, such as food safety. Here, X-ray spectroscopies are attractive candidates as they provide , nondestructive analyses of solid samples without perturbation to the arsenic species therein. This work provides a speciation analysis for three certified reference materials for the food chemistry community, whose assigned values may be used to assess the merit of the X-ray spectroscopy results. Furthermore, extracts of SRM 3232 Kelp Powder, which is value-assigned for arsenic species, are measured to provide further evidence of its efficacy. These analyses are performed on the results of As K-edge X-ray Absorption Near Edge Structure (XANES) measurements collected on each sample. Notably, such analyses have traditionally relied on linear combination fitting of a minimal subset of empirical standards selected by stepwise regression. This is known to be problematic for compounds with meaningfully collinear spectra and can yield overestimates of the accuracy of the analysis. Therefore, the least absolute shrinkage and selection operator (lasso) regression method is used to reduce the risk of overfitting and increase the interpretability of statistical inferences. As this is a biased statistical method, results and uncertainties are estimated using a bootstrap method accounting for the dominant sources of variability. Finally, this method does not separate model and data selection from regression analysis. Indeed, a survey of many spectral influences is presented including changes in the: state of methylation, state of protonation, oxidation state, coordination geometry, and sample phase. These compounds were all included in the model's training set, preventing model over-simplification and enabling high-throughput and robust analyses.
砷的毒性和生物利用度在很大程度上取决于其形态。因此，需要可靠且准确的方法来确定与公共卫生倡议相关材料（如食品安全）中的砷形态分布。在这里，X射线光谱学是有吸引力的选择，因为它们能对固体样品进行无损分析，而不会干扰其中的砷形态。这项工作为食品化学领域的三种有证标准物质提供了形态分析，其赋值可用于评估X射线光谱学结果的优劣。此外，对已对砷形态进行赋值的SRM 3232海带粉提取物进行了测量，以进一步证明其有效性。这些分析是基于对每个样品进行的As K边X射线吸收近边结构（XANES）测量结果进行的。值得注意的是，此类分析传统上依赖于通过逐步回归选择的最少经验标准子集的线性组合拟合。众所周知，对于具有明显共线光谱的化合物，这会产生问题，并且可能高估分析的准确性。因此，使用最小绝对收缩和选择算子（lasso）回归方法来降低过度拟合的风险，并提高统计推断的可解释性。由于这是一种有偏统计方法，因此使用考虑主要变异性来源的自助法来估计结果和不确定性。最后，该方法没有将模型和数据选择与回归分析分开。实际上，本文展示了对许多光谱影响的研究，包括甲基化状态、质子化状态、氧化态、配位几何结构和样品相的变化。这些化合物都包含在模型的训练集中，防止了模型过度简化，并实现了高通量和稳健的分析。
