Strategies for data reduction in non-targeted screening analysis: The impact of sample variability for food safety applications
| Autor: | Christine M. Fisher, Ann M. Knolhoff |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Food Safety
Non targeted Avena Databases Factual Computer science Sample (statistics) computer.software_genre 01 natural sciences Differential analysis Analytical Chemistry Screening analysis Ingredient 0404 agricultural biotechnology Tandem Mass Spectrometry Sample complexity business.industry 010401 analytical chemistry 04 agricultural and veterinary sciences General Medicine Food safety 040401 food science 0104 chemical sciences Data mining Edible Grain business computer Chromatography Liquid Food Science Data reduction |
| Zdroj: | Food Chemistry. 350:128540 |
| ISSN: | 0308-8146 |
| Popis: | While analytical methods targeting specific compounds are critical for food safety, analytes excluded from the targeted list will not be identified. Non-targeted analyses (NTA) using LC/HR-MS complement these approaches by producing information-rich data sets where molecular formula can be generated for each detected compound; however, data mining can be labor intensive. Thus, we examined different NTA approaches to reduce the number of compounds needing further investigation, without relying on a suspect list or MS/MS database, both in single ingredient foods (i.e., oats) and more complex, oat-containing samples. We investigated inherent sample variability and utilized this information to build in-house databases for removing food compounds from sample data. While food databases were useful for data reduction, differential analysis was the most promising approach for single ingredient foods because it substantially reduced the number of features while retaining spiked QC compounds; however, a combination of approaches was necessary with greater sample complexity. |
| Databáze: | OpenAIRE |
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