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The Inductively Coupled Plasma Mass Spectrometry (ICP-MS) provides elemental analysis through ionization of samples. Numerous geochemistry laboratories utilize the ICP-MS and has become a community standard. The ICP-MS data reduction process is time intensive and involves correction for blank contribution and sensitivity drift during measurement, as well as construction of standard calibration lines based on measurements of standard solutions. We look to decrease turnaround time and increase efficiency through automation of the data reduction process using Python. The ICP-MS data reduction process was 3 hours or more, in our observations. Python packages such as Openpyxl and Pandas, allow us to carry out data reduction functions. Inputting the file location of the original ICP-MS data, the code will extract data from the file, follow data reduction functions, create graphs of calibration curves for various elements, and save reduced data as a new excel file without making changes to the original file. Automated files will be checked for correct values using non-automated data reduction as reference. The code operated universally, using various unreduced ICP-MS excel data files from previous analyses, and followed data reduction functions correctly. When compared to non-automated data reduction, automated data reduction was able to output the same values and calibration curve graphs. The processing time using automated data reduction was less than 1 second. Using python to automate the data reduction process significantly decreased the duration from 3 hours to less than 1 second. Additionally, efficiency increased by factoring out potential human error in the process.
Martin, Grace Abigail, "Increasing Analysis Efficiency through Automation of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) Data Reduction Process using Python" (2021). Undergraduate Research Symposium Posters. 18.