Nontargeted Screening of Food Matrices: Development of a Chemometric Software Strategy To Identify Unknowns in Liquid Chromatography–Mass Spectrometry Data

The ability to identify contaminants or adulterants in diverse, complex sample matrixes is necessary in food safety. Thus, nontargeted screening approaches must be implemented to detect and identify unexpected, unknown hazardous compounds that may be present. Molecular formulas can be generated for...

Full description

Saved in:
Bibliographic Details
Published in:Analytical chemistry (Washington) 2016-04, Vol.88 (7), p.3617-3623
Main Authors: Knolhoff, Ann M, Zweigenbaum, Jerry A, Croley, Timothy R
Format: Article
Language:English
Subjects:
Adulterants
Chromatography
Chromatography, Liquid
Data mining
Data processing
Drug Contamination
Food
Food Contamination - analysis
Food Safety
Foods
Fruit and Vegetable Juices - analysis
Humans
Infant
Infant Formula - analysis
Infant Formula - chemistry
Infants
Mass Spectrometry
Matrix
Screening
Software
Strategy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The ability to identify contaminants or adulterants in diverse, complex sample matrixes is necessary in food safety. Thus, nontargeted screening approaches must be implemented to detect and identify unexpected, unknown hazardous compounds that may be present. Molecular formulas can be generated for detected compounds from high-resolution mass spectrometry data, but analysis can be lengthy when thousands of compounds are detected in a single sample. Efficient data mining methods to analyze these complex data sets are necessary given the inherent chemical diversity and variability of food matrixes. The aim of this work is to determine necessary requirements to successfully apply data analysis strategies to distinguish suspect and control samples. Infant formula and orange juice samples were analyzed with one lot of each matrix containing varying concentrations of a four compound mixture to represent a suspect sample set. Small molecular differences were parsed from the data, where analytes as low as 10 ppb were revealed. This was accomplished, in part, by analyzing a quality control standard, matrix spiked with an analytical standard mixture, technical replicates, a representative number of sample lots, and blanks within the sample sequence; this enabled the development of a data analysis workflow and ensured that the employed method is sufficient for mining relevant molecular features from the data.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.5b04208