Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea

Delaporte, Grégoire; Cladière, Mathieu; Bouveresse, Delphine Jouan-Rimbaud; Camel, Valérie

doi:10.1016/j.foodchem.2018.10.089

Cited by 36 publications

(33 citation statements)

References 39 publications

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“…Pairwise t -test, as a reliable statistical test method, was performed to calculate p values between the two concentration groups and the p < 0.05 observed in this study indeed showed the existence of significant differences among groups. Previous studies [ 29 , 54 ] also adopted fold change of concentration > 2 to discern variables with high contrast among groups as marker compounds. Herein, marker compounds on behalf of 50 PPCPs all presented fold change values above 2, supporting the validity of marker compounds obtained with our analytical strategy.…”

Section: Resultsmentioning

confidence: 99%

“…Previous studies have successfully applied non-targeted screening methods on the basis of metabolomics to pesticide residues in plant matrices, e.g., orange juice [ 28 ] and tea [ 29 ], providing the feasibility to screen PPCPs residues in plant-derived foods. In light of the otherness of analytes, the reported methods may not be completely applied to our study.…”

Section: Resultsmentioning

confidence: 99%

“…Samples of 20 ng mL −1 –1~20 ng mL −1 –9, 50 ng mL −1 –1~50 ng mL −1 –9 and 100 ng mL −1 –1~100 ng mL −1 –9 were employed to label samples from three concentration groups. Each sample provided a 30 µL solution as a quality control (QC) sample [ 29 , 33 , 34 ], which experienced 3 injections before and after each concentration group. As a result, 12 samples marked as QC-1, QC-2, and QC-12 were obtained to evaluate the stability of LC-MS/MS.…”

Section: Methodsmentioning

confidence: 99%

“…The latter sense is to employ omics-related approaches to complete U&U contaminant screening [ 24 , 25 ], which can be realized by high resolution mass spectrometry (HRMS) technology [ 26 , 27 ]. To date, LC-MS/MS-based metabolomics analytical methods have showed good practicability on non-targeted screening of some pesticides in food matrices, e.g., orange juice [ 28 ], milk [ 24 ] and tea [ 29 ], with the screening ratio of pesticides depending on their contents. These studies have obtained desirable outcomes, but the methods they proposed are so sophisticated that they are not favorable for wide application.…”

Section: Introductionmentioning

confidence: 99%

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Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

Xue¹,

Yang²,

Liu³

et al. 2022

Molecules

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The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL−1) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise t-test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.4~2.0 µg kg−1 and 0.3~2.1 µg kg−1 in lettuce and maize matrices, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

Xue¹,

Yang²,

Liu³

et al. 2022

Molecules

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show abstract

“…Nowadays, ICA is widely used in,  Medical imaging (Determining the sources of the signals emitted by the brain and separating the ultrasonography signals [10][11][12]),  Geology (Investigating seismic waves and geological mapping [13][14][15]),  Fault detection (Detecting faults encountered in quality control processes [16][17][18]),  Image processing (Revealing the attributes of the images and deblurring the images [19][20][21]),  Telecommunications (Separating radio waves or audio signals [22][23][24]),  Econometrics (Determining the components that play a role in the formation of financial series [25][26][27]),  Data and text mining (Reducing size [28][29][30]),  Bioinformatics and genetics (Ascertaining essential components in gene expression [31][32][33]),  Chemistry (Analyzing the components in the NIR spectroscopy used in the production and break down of foods [34][35][36]).…”

Section: Development Process and Application Areas Of Independent Commentioning

confidence: 99%

Evaluation of Independent Components Analysis from Statistical Perspective and Its Comparison with Principal Components Analysis

Bursa

Tatlidil

2020

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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One of the most important problems in statistics and related fields is that finding an appropriate representation of multivariate data. Here is meant by representation; to transform the data into a more visible (accessible) form. Independent Components Analysis (ICA) is a statistical method used to find the underlying components of multivariate data and makes its main structure more visible. In this respect, ICA can also be seen as an extension of the Principal Components Analysis (PCA). However, ICA, contrary to PCA, is based on statistical independence rather than unrelatedness and statistical independence is a much stronger feature than unrelatedness. In addition, while the normal distribution of the components obtained in PCA is desired, the independent components of ICA are requested not to distribute normally. In the study, although it is a multivariate statistical method, the subject of ICA, which is not well known in the field of statistics and which is mostly used in engineering, was discussed in detail and contributed to the limited statistical literature on the subject. In the application part, ICA was compared with a similar method, PCA. Both analyzes were applied to an artificial dataset and it was concluded that ICA was much more successful than PCA in detecting non-normal components.

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Contaminants as Chemical Hazards in Food

Cariou¹,

Bizec²

2022

Hazards in the Food Processing and Distribution Chain

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Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea

Cited by 36 publications

References 39 publications

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

Evaluation of Independent Components Analysis from Statistical Perspective and Its Comparison with Principal Components Analysis

Contaminants as Chemical Hazards in Food

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