Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry

Knolhoff, Ann M.; Croley, Timothy R.

doi:10.1016/j.chroma.2015.08.059

Cited by 100 publications

(43 citation statements)

References 112 publications

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“…The described tools are commonly based on multivariate statistics. Fewer attempts have been made to use HRMS and foodomics in order to determine the presence of contaminants, as reviewed by Knolhoff and Croley [27]. One such a recent study with promising results [28] has great similarities to the present study.…”

Section: Resultsmentioning

confidence: 64%

Non-targeted analysis of unexpected food contaminants using LC-HRMS

et al. 2018

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A non-target analysis method for unexpected contaminants in food is described. Many current methods referred to as “non-target” are capable of detecting hundreds or even thousands of contaminants. However, they will typically still miss all other possible contaminants. Instead, a metabolomics approach might be used to obtain “true non-target” analysis. In the present work, such a method was optimized for improved detection capability at low concentrations. The method was evaluated using 19 chemically diverse model compounds spiked into milk samples to mimic unknown contamination. Other milk samples were used as reference samples. All samples were analyzed with UHPLC-TOF-MS (ultra-high-performance liquid chromatography time-of-flight mass spectrometry), using reversed-phase chromatography and electrospray ionization in positive mode. Data evaluation was performed by the software TracMass 2. No target lists of specific compounds were used to search for the contaminants. Instead, the software was used to sort out all features only occurring in the spiked sample data, i.e., the workflow resembled a metabolomics approach. Procedures for chemical identification of peaks were outside the scope of the study. Method, study design, and settings in the software were optimized to minimize manual evaluation and faulty or irrelevant hits and to maximize hit rate of the spiked compounds. A practical detection limit was established at 25 μg/kg. At this concentration, most compounds (17 out of 19) were detected as intact precursor ions, as fragments or as adducts. Only 2 irrelevant hits, probably natural compounds, were obtained. Limitations and possible practical use of the approach are discussed.Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1028-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 64%

Non-targeted analysis of unexpected food contaminants using LC-HRMS

et al. 2018

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“…Food fingerprint approaches, also known as non‐target analysis, have become a powerful tool for quality control of many products, for example, edible oil , fruit juice , alcoholic beverage , milk powder among others . These methodologies have been developed for different purposes, like to vouch authenticity brand or geographical origin to detect presence of adulterants compounds .…”

Section: Introductionmentioning

confidence: 99%

Second‐order capillary electrophoresis diode array detector data modeled with the Tucker3 algorithm: A novel strategy for Argentinean white wine discrimination respect to grape variety

et al. 2016

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Data obtained by capillary electrophoresis with diode array detection (CE-DAD) were modeled with the purpose to discriminate Argentinean white wines samples produced from three grape varieties (Torrontés, Chardonnay, and Sauvignon blanc). Thirty-eight samples of commercial white wine from four wine-producing provinces of Argentina (Mendoza, San Juan, Salta, and Rio Negro) were analyzed. CE-DAD matrices with dimensions of 421 elution times (from 1.17 to 7.39 minutes) × 71 wavelengths (from 227 to 367 nm) were joined in a three way data array and decomposed by Tucker3 method under non-negativity constraint, employing 18, 18 and six factors in the modes 1, 2 and 3, respectively. Using the scores of Tucker model, it was possible to discriminate samples of Argentinean white wine by linear discriminant analysis and Kernel linear discriminant analysis. Core element analysis of the Tucker3 model allows identifying the loading profiles in spectral mode related to Argentinean white wine samples.

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“…Some impurity peaks may go unnoticed in LC/MS separations, especially when targeted methods are used. However, impurities can cause a variety of problems, such as signal suppression of analyte peaks, when the impurity peak elutes near, or concurrently with, an analyte of interest, and this can lead to mass errors and inaccurate quantitation …”

Section: Introductionmentioning

confidence: 99%

“…However, impurities can cause a variety of problems, such as signal suppression of analyte peaks, when the impurity peak elutes near, or concurrently with, an analyte of interest, and this can lead to mass errors and inaccurate quantitation. 1,8 During routine work in our laboratory, several extraneous peaks were observed in both solvent blanks and samples. The peaks persisted even after extensive re-equilibration of the column (6-8 h) and after running several blanks.…”

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confidence: 99%

Identification of two novel trace impurities in mobile phases prepared with commercial formic acid

Bakota

Levine

2020

Rapid Comm Mass Spectrometry

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Rationale While liquid chromatography/high‐resolution mass spectrometry (LC/HRMS) is a versatile analytical technique, it is also sensitive to trace impurities. These impurities may come from a variety of sources, including reagents, solvents, and the sample matrix itself. Impurities in reagents may become concentrated and elute as peaks when a gradient method is used, and these peaks may cause suppression of peaks of interest both in the electrospray source, as well as in the C‐trap in systems that contain one. Methods We observed a notable increase in the size of several impurity peaks in a reversed‐phase gradient method upon switching suppliers of formic acid. We used LC/HRMS to separate and fragment these impurity compounds and assign probable formulae. Results The mass spectra were compared with those of compounds found in the literature with the same formulae, and the observed peaks were matched to two amine compounds not previously reported as impurities in LC/MS systems: trihexylamine and N‐methyldihexylamine. The identities were confirmed by high‐resolution accurate mass and retention time matching against commercially available standards of these compounds. Conclusions To the best of our knowledge, this is the first time that trihexylamine and N‐methyldihexylamine have been reported in such systems. We hypothesize that these are derived from the formic acid manufacturing process and recommend that users monitor purchased formic acid for the presence of impurities.

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Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry

Cited by 100 publications

References 112 publications

Non-targeted analysis of unexpected food contaminants using LC-HRMS

Non-targeted analysis of unexpected food contaminants using LC-HRMS

Second‐order capillary electrophoresis diode array detector data modeled with the Tucker3 algorithm: A novel strategy for Argentinean white wine discrimination respect to grape variety

Identification of two novel trace impurities in mobile phases prepared with commercial formic acid

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