Open Source Software Toolchain for Automated Non-Targeted Screening for Toxins in Alternative Foods
- S. W. Breuer, L. Toppen, S. K. Schum, J. M. Pearce
Summary
Breuer et al. introduce a novel, open-source methodology for non-targeted toxin screening in resilient (alternative) foods, addressing the time and cost limitations of previous methods. The method, validated against proprietary software, offers a more detailed and accessible solution for preliminary food safety analysis, particularly benefiting scientists in under-resourced settings.
Abstract
Previous published methods for non-targeted screening of toxins in alternative foods such as leaf concentrate, agricultural residues or plastic fed to biological consortia are time consuming and expensive and thus present accessibility, as well as, time-constraint issues for scientists from under resourced settings to identify safe alternative foods. The novel methodology presented here, utilizes a completely free and open source software toolchain for automatically screening unknown alternative foods for toxicity using experimental data from ultra-high-pressure liquid chromatography and mass spectrometry. The process uses three distinct tools (mass spectrometry analysis with MZmine 2, formula assignment with MFAssignR, and data filtering with ToxAssign) enabling it to be modular and easily upgradable in the future. MZmine 2 and MFAssignR have been previously described, while ToxAssign was developed here to match the formulas output by formula assignment to potentially toxic compounds in a local table, then look up toxic data on the Open Food Tox Database for the matched compounds. This process is designed to fill the gap between food safety analysis techniques and developing alternative food production techniques to allow for new methods of food production to be preliminarily tested before animal testing. The methodology was validated against a previous method using proprietary commercial software. The new process identifies all of the toxic elements the previous process identified with more detailed information than the previous process was able to provide automatically.