IsotoPy: A Novel Software for Stable Isotope Research in Organic Geochemistry with Orbitrap-MS

Introduction

            Stable isotopes have been used for decades to elucidate the geochemical aspects of petroleum accumulation and support basin modeling. Specifically, the isotopic composition of H, C, and S in specific compounds has been employed in studies of paleo-depositional environments, thermal evolution, biodegradation, oil-oil, and oil-rock correction¹. Traditionally, the gold standard for isotopic ratio analysis via mass spectrometry has been the Isotope Ratio Mass Spectrometer (IRMS). However, in 2017, the first study utilizing Orbitrap mass spectrometry (Orbitrap MS) for isotopic ratio analysis was published². Since then, the number of studies employing Orbitrap-MS for this purpose has been increasing annually^3,4,5.

            In this context, in response to the growing demand for advanced data treatment tools, our multidisciplinary team has developed IsotoPy, an innovative and versatile software platform specifically designed to streamline and optimize the processing of isotopic ratio data obtained via high-resolution Orbitrap-MS instruments. The software effectively addresses a series of persistent challenges in the field, particularly those related to data standardization, error propagation, and critical decision-making during isotopic analysis, offering researchers a comprehensive, automated, and user-friendly solution tailored for both routine and complex analytical workflows.

Experimental

            The software comprises three main components: (1) a high-level Python framework that ensures compatibility with other tools and supports batch processing; (2) a web-based application that provides an intuitive user interface for data input and visualization; and (3) a locally running Python application designed for batch processing, enabling users to efficiently handle large datasets on their own machines without requiring an internet connection. The protocol integrates various statistical and mathematical methods into a unified workflow, enabling error analysis and the generation of tabular and graphical results to guide the analyst’s decisions.

            The framework, implemented in Python 3, ensures compatibility with other tools and supports batch processing, making it a flexible solution for large datasets. The graphical interface is intuitive and designed for users without prior knowledge of data science or programming. It allows users to input data files and parameters easily, with automatic data validation to ensure the accuracy of the analysis. Users can quickly perform all necessary data treatments, processing, and calculations, generating comprehensive tables and graphs essential for isotopic ratio analysis. The software supports various types of isotopic ratio analyses, including those involving hydrogen, carbon, nitrogen, oxygen, and sulfur. It handles different analysis methods and file types, making it versatile for multiple applications.

Results and Discussion

            To demonstrate the software’s capability in petroleum biomarker studies, a stable carbon isotope analysis was performed using a benzoic acid sample from Sigma Aldrich, contrasted with a benzoic acid reference standard from the International Isotope Exchange (IEA). The analysis followed a bracketed block design, alternating between reference and sample across seven 5-minute acquisition blocks over a total period of 35 minutes. Several preprocessing steps were applied to the bracketed acquisitions data, including dead volume time trimming, outlier removal, elimination of zero scans, normality assessment, acquisition error analysis, and shot-noise evaluation. After these corrections, isotope ratios were determined for each acquisition block, and δ¹³C-values were calculated. The results, presented in Figure 1 and Table 1, were entirely generated using IsotoPy software.

            For each block, the chromatogram was trimmed to the 1-9 minute interval to remove the initial dead volume and the final wash phase. The peaks corresponding to the target molecules were identified based on a mass tolerance window (121.0296 ± 0.001 for M0 and 122.0328 ± 0.001 for M+1). The acquisition error was calculated for each block and ranged from 0.38‰ to 0.42‰, with the ratio of acquisition error to shot noise ranging from 1.14 to 1.22. The average δ¹³C value obtained was -28.38‰, with a standard deviation of 1.36‰, representing only a 0.61‰ deviation from the value obtained by IRMS for the same standard. This level of accuracy and precision underscores the software’s reliability, meeting the requirements for distinguishing petroleum origins and detecting potential adulterations in crude oil samples.

                These findings demonstrate the software's effectiveness in processing isotopic data, facilitating the identification of isotopic markers relevant to crude oil characterization. The integration of automated workflows and advanced visualization tools enhances the accuracy and efficiency of isotopic analysis in organic geochemistry.

Figure 1. Chromatogram (A) and average spectrum (B) of single acquisition (block 1) of benzoic acid. TIC before (C) and after (D) cutting by time and removing outliers and zero scans. Cumulative Isotopic Ratio (E) and Acquisition error and Shot-Noise (F) of block 1. Average Isotopic Ratio of each block (G) and δ¹³C of sample blocks (H).

Table 1. Parameters of benzoic acid bracketed acquisitions.

Conclusions

            In summary, IsotoPy represents a significant advancement in isotopic ratio analysis, providing a robust, efficient, and user-friendly solution for researchers and analysts. By automating complex data treatments and decision-making processes, it facilitates rapid and accurate isotopic analysis, contributing to enhanced scientific research and industrial applications. The combination of offline processing, compatibility with diverse data formats, and interactive visualization makes IsotoPy a valuable tool for both academic and industrial environments. It not only reduces the analytical burden on users but also democratizes access to isotopic analysis by extending this capability to laboratories equipped with Orbitrap instruments.

Acknowledgements

The authors thank CNPq, CAPES and Petrobras for sponsor and fellowships.

References

¹Eiler K, et al. Earth and Planetary Science Letters, 262, 2007, 309-327.

²Eiler J, et al. International Journal of Mass Spectrometry, 422, 2017, 126.

³Hilkert A, et al. Analytical Chemistry, 93, 2021, 9139.

⁴Mueller EP, et al. Analytical Chemistry, 94, 2022, 1092.

⁵Csernica T, et al.International Journal of Mass Scpetrometry 490, 2023, 117084.