DETERMINATION OF HIGH-PRECISION ISOTOPE RATIOS FROM EXPERIMENTAL DISTRIBUTIONS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 由于自然过程的同位素变异性提供了重要的信息，研究各种复杂的现象，如确定一个给定的样品的起源，物种的饮食研究，硝化率在树木等，这些测量需要非常高的精度确定同位素比值的特定元素。同位素比质谱仪（IRMS）是广泛使用的高精度分析工具。IRMS仪器仅接受有限数量的气体形式的样品分析物，这些气体必须代表原始样品的同位素特征，这导致缺乏灵活性。这项工作的目的是克服固有的IRMS的局限性，估计元素同位素丰度的实验同位素分布。 实验同位素分布是对分子中存在的各种元素的同位素丰度的间接测量。 它可以用每个原子的同位素丰度的联合卷积来表示。已经开发了数学技术，以便从各种元素中找出已知的同位素丰度贡献，然后计算感兴趣的元素的相应未知值。通过求解卷积方程，从每个观察到的同位素峰生成所需同位素丰度的估计值，并将最终结果报告为获得的每个单个值的平均值。 进行计算机生成的模拟，以生成具有已知元素组成和同位素丰度的给定分子的实验同位素分布。将由此获得的结果应用于开发的理论框架，以估计来自每个同位素峰的碳的同位素丰度，并在计算中考虑其他元素的丰度值。这些估计结果表明，与它们的真实值是很好的协议。增加用于产生实验同位素分布的离子的数量大大改善了估计。 这是因为在无限离子数的限制下，实验同位素分布接近其理论对应物，并且是其组分组成的真实代表。高分子量的分子被证明是特别有利的，因为在同位素分布中存在大量的同位素峰，导致更大量的信息。初步结果表明，有足够多的离子和多次实验，它是可能的样品之间的区别变化非常轻微的碳同位素丰度。例如，估计值可以区分样品是来自海洋浮游生物（C13丰度= 1.09%，Delta C13=-19.5）还是来自以C4植物为食的动物的肉（C13丰度= 1.1%，Delta C13=-12.5）。实验数据的结果将与真实丰度值及其估计值进行比较。这种方法消除了将样品转化为气体形式的需要。该结果适用于任何类型的质谱仪和任何类型的样品。结果也可以扩展到估计任何未知元素的同位素丰度提供的其他元素的同位素丰度是已知的先验。 发表了一篇论文，详细介绍了分析化学中的方法和计算。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Isotope variability due to natural processes provides important information for studying a variety of complex phenomenon like determining the genesis of a given sample, dietary studies of species, nitrification rates in trees etc. These measurements require very high-precision determination of isotope ratios of a particular element involved. Isotope Ratio Mass Spectrometers (IRMS) are widely employed tools for such a high-precision analysis. IRMS instruments accept the sample analyte in the form of only a limited number of gases which must represent the isotopic characteristics of the original sample, which causes lack of flexibility. This work aims at overcoming the limitations inherent to IRMS by estimating the elemental isotopic abundance from the experimental isotopic distribution. Experimental isotopic distribution is an indirect measure of the isotopic abundances of various elements present in the molecule. It can be represented by the joint convolution of the isotopic abundances of each of the individual atoms. Mathematical techniques have been developed in order to factor out the known isotopic abundance contributions from various elements followed by calculation of the corresponding unknown values for the element of interest. An estimate for the required isotopic abundance is generated from each of the observed isotopic peaks by solving the convolution equations, and the final result is reported to be the mean of each of the individual values obtained. Computer generated simulations were carried out in order to generate the experimental isotopic distributions for a given molecule with known elemental composition and isotopic abundances. The results thus obtained were subjected to the developed theoretical framework in order to estimate the isotopic abundance for Carbon from each isotopic peak, with the abundance values for the other elements being taken into consideration in the calculation. These estimated results are shown to be in good agreement with their true values. Increasing the number of ions for generating the experimental isotopic distribution greatly improves the estimate. This is because in the limit of infinite number of ions, experimental isotopic distribution approaches its theoretical counterpart and is a true representative of the composition of its constituents. High molecular weight molecules are shown to be particularly advantageous because of the presence of larger number of isotopic peaks in the isotopic distribution leading to a greater amount of information. Initial results reveal that with sufficiently high number of ions and multiple experiments, it is possible to distinguish between the samples varying very slightly in the Carbon isotopic abundance. For example, the estimate can distinguish whether the sample originated from marine plankton (C13 abundance = 1.09%, Delta C13=-19.5) or meat from an animal feeding on a C4 plant (C13 abundance=1.1%, Delta C13=-12.5). Results from the experimental data will be presented comparing the true abundance value with its estimated counterpart. This approach eliminates the need to convert the sample into gaseous form. The results are applicable to any type of mass spectrometer, and for any type of sample. The results can also be extended to estimate the isotopic abundance of any unknown element provided the isotopic abundance of the other elements are known a priori. A paper was published detailing the method and calculations in Analytical Chemistry.