Improvement of Compound Identification Workflow in Chemical Analysis Data Using Knowledge Processing

Abstract

The gas chromatography-mass spectrometer (GC-MS) is an instrument capable of measuring the concentrations of various volatile substances. It is used as input data for multivariate analysis and machine learning applications across a wide range of fields from quality control of materials and food to disease diagnosis. To utilize the results of this multivariate analysis and machine learning as scientific insights, it is essential to identify the substance names from the contributing dimensions. Typically, the identification process involves searching a library of standard spectra using the similarity in mass distributions (spectra) of molecular fragments obtained by applying high energy to the molecules. However, due to the presence of many similar spectra, numerous candidate substances exist, and the correct compound may be overlooked. This paper proposes a system to improve the workflow by using knowledge processing technology. Practical examples using open data related to the quality of materials and foods demonstrate a reduction in missed compounds.