Data handling and data analysis in metabolomic studies of essential oils using GC-MS

Lebanov, Leo; Ghiasvand, Alireza; Paull, Brett

doi:10.1016/j.chroma.2021.461896

Cited by 22 publications

(12 citation statements)

References 187 publications

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“…After data acquisition, the next steps relate to the multi-dimensional data analysis. Pre-processing is important to “clean” the data obtained in the detection to narrow down the most relevant information and “optimize” the data analysis [ 64 ]. This process may include noise filtering (removal of signals from the equipment and the experimental procedure), peak definition (identification of metabolite signals) and peak alignment (correction of possible peak deviations), normalization (removal of small variations caused by the method that are usually constant in samples), scaling (adjustment of the intensity of different variables), and transformation (adjustment of the distribution of the data and mitigating large outliers) [ 7 , 65 ].…”

Section: Platforms For Metabolomic Studies: Analytical Methods and Data Processingmentioning

confidence: 99%

“…Regarding multivariate analyses, they can be supervised when information about the groups of interest in the sample is already known, or unsupervised, which are exploratory techniques. The main examples of the former are partial least squares (PLS), partial least squares discriminant analysis (PLS-DA), orthogonal projections to latent structures discriminant analysis (OPLS-DA), linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), random forests (RF), and artificial neural network (ANN) [ 64 , 65 ]. The latter rely on PCA, cluster analysis (CA), hierarchical clustering (HCA), and t -distributed stochastic neighbor embedding ( t -SNE) [ 7 , 60 ].…”

Section: Platforms For Metabolomic Studies: Analytical Methods and Data Processingmentioning

confidence: 99%

“…PLS and OPLS analysis can assist in obtaining more accurate and robust data, validating the information from the exploratory analysis by amplifying the variability between samples [ 12 , 64 ]. LDA and PLS-DA are supervised statistical analyses that use latent variables to enhance the differentiation between classes.…”

Section: Platforms For Metabolomic Studies: Analytical Methods and Data Processingmentioning

confidence: 99%

See 2 more Smart Citations

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Pedrosa

Lima

Heleno

et al. 2021

Foods

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Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.

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Section: Platforms For Metabolomic Studies: Analytical Methods and Data Processingmentioning

confidence: 99%

Section: Platforms For Metabolomic Studies: Analytical Methods and Data Processingmentioning

confidence: 99%

Section: Platforms For Metabolomic Studies: Analytical Methods and Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Pedrosa

Lima

Heleno

et al. 2021

Foods

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“…PTR-MS has the potential to sample VOCs on-line and make quantitative analysis fast without any sample preparation [146,147]. The most widely used of these identification techniques is GC-MS [148]. Although PTR-MS can better achieve quantitative identification, most of the volatiles identified are preliminary [149].…”

Section: Identification Of Plants Vocsmentioning

confidence: 99%

Research Advances in Allelopathy of Volatile Organic Compounds (VOCs) of Plants

Xie

Tian

et al. 2021

Horticulturae

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Allelopathy is an ecological phenomenon in which organisms interfere with each other. As a management strategy in agricultural systems, allelopathy can be mainly used to control weeds, resist pests, and disease and improve the interaction of soil nutrition and microorganisms. Volatile organic compounds (VOCs) are allelochemicals volatilized from plants and have been widely demonstrated to have different ecological functions. This review provides the recent advance in the allelopathic effects of VOCs on plants, such as growth, competition, dormancy, resistance of diseases and insect pests, content of reactive oxygen species (ROS), enzyme activity, respiration, and photosynthesis. VOCs also participate in plant-to-plant communication as a signaling substance. The main methods of collection and identification of VOCs are briefly summarized in this article. It also points out the disadvantages of VOCs and suggests potential directions to enhance research and solve mysteries in this emerging area. It is necessary to study the allelopathic mechanisms of plant VOCs so as to provide a theoretical basis for VOC applications. In conclusion, allelopathy of VOCs released by plants is a more economical, environmentally friendly, and effective measure to develop substantial agricultural industry by using the allelopathic effects of plant natural products.

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“…At present, the combination of chromatography and mass spectrometry has become the key technology for the analysis of metabolites in biological systems [ 4 , 5 ]. Compaed with gas chromatography coupled to mass spectrometry (GC–MS) [ 6 , 7 , 8 ], high-performance liquid chromatography–mass spectrometry (LC–MS) can analyze compounds with semi-polar and lower volatility in a wider mass range without derivatization [ 9 , 10 , 11 ]. The samples are separated by the chromatographic column after injection and identified by analyzing the spectra acquired by the mass spectrometer.…”

Section: Introductionmentioning

confidence: 99%

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

Tian

Lin

Wang³

et al. 2021

Molecules

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Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.

show abstract

Data handling and data analysis in metabolomic studies of essential oils using GC-MS

Cited by 22 publications

References 187 publications

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Research Advances in Allelopathy of Volatile Organic Compounds (VOCs) of Plants

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

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