Data Processing Strategies For Non-Targeted Analysis Of Foods Using Liquid Chromatography/High-Resolution Mass Spectrometry

Non-targeted analysis (NTA) is a powerful methodology used to classify samples and/or identify unknown compounds/classes of interest. These methods are especially useful for food applications including safety, quality, authentication, and nutrition, among others. In particular, data generated from liquid chromatography/high-resolution mass spectrometry (LC/HR-MS) often contains thousands of detected compounds in a single sample. While incredibly useful, NTA can be especially challenging for food analysis due to sample diversity and complexity; thus, automated processing tools are vital for minimizing manual data interrogation/interpretation. While many approaches can be used to process this data, we focus on those with the most promise for food analysis: chemometrics, compound databases, MS/MS libraries (including MS/MS similarity and in silico MS/MS), molecular networking, mass defects, retention time prediction, and ion mobility spectrometry tools. Recent advancements, advantages, and applications of these strategies are provided, demonstrating their utility and providing guidance for researchers developing NTA workflows for food analysis.Published by Elsevier B.V.