Baseline Correction Method Using an Orthogonal Basis for Gas Chromatography/Mass Spectrometry Data

Xu, Zhanfeng; Sun, Xiaobo; Harrington, Peter de B.

doi:10.1021/ac2016745

Cited by 34 publications

(18 citation statements)

References 34 publications

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“…41 One hundred spectra at the end of each chromatogram where there was no analytical signal were chosen to build the basis set that comprised 50 orthogonal components.…”

Section: Methodsmentioning

confidence: 99%

Classification of Cultivation Locations of Panax quinquefolius L Samples using High Performance Liquid Chromatography–Electrospray Ionization Mass Spectrometry and Chemometric Analysis

et al. 2012

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Panax quinquefolius L (P. quinquefolius L) samples grown in the United States and China were analyzed with high performance liquid chromatography–mass spectrometry (HPLC–MS). Prior to classification, the two-way data sets were subjected to pretreatment including baseline correction and retention time (RT) alignment. Principal component analysis (PCA) and projected difference resolution (PDR) metrics were used to evaluate the data quality and the pretreatment effects. A fuzzy rule-building expert system (FuRES) classifier was used to classify the P. quinquefolius L samples grown in the United States and China with the optimized partial least-squares (o-PLS) classifier as the positively biased control method. A classification rate as high as 98 ± 3% with FuRES was obtained after baseline correction and RT alignment, which is equivalent to the result obtained by using the positively biased o-PLS control method (98 ± 3%). RT alignment improved the classification rates for both FuRES and o-PLS classifiers (18% improvement for the FuRES classification rate and 10% improvement for the o-PLS classification rate with baseline correction). From the rule obtained to classify the P. quinquefolius L samples grown in the United States and China, peaks were identified that can be prospective biomarkers for differentiating samples from different growth regions. HPLC–MS with chemometric analysis has the potential to be used as an authentication method for P. quinquefolius L grown in China and the United States.

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“…41 One hundred spectra at the end of each chromatogram where there was no analytical signal were chosen to build the basis set that comprised 50 orthogonal components.…”

Section: Methodsmentioning

confidence: 99%

Classification of Cultivation Locations of Panax quinquefolius L Samples using High Performance Liquid Chromatography–Electrospray Ionization Mass Spectrometry and Chemometric Analysis

et al. 2012

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“…Chromatographic baselines are usually not constant during chromatographic runs, due to the use of temperature programs and the thermal degradation or vaporization of the stationary phase. 15 In addition, unavoidable run-to-run retention time variations can wreak havoc with multivariate models. Other undesirable variations are mainly due to the detector nonlinearity 16 , ionization suppression 17 , and changes in instrument parameters (e.g., temperature and gas flow fluctuations, and matrix effects).…”

Section: Introductionmentioning

confidence: 99%

Authentication of Organically and Conventionally Grown Basils by Gas Chromatography/Mass Spectrometry Chemical Profiles

Wang

Chen

et al. 2013

Anal. Chem.

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Basil plants cultivated by organic and conventional farming practices were accurately classified by pattern recognition of gas chromatography/mass spectrometry (GC/MS) data. A novel extraction procedure was devised to extract characteristic compounds from ground basil powders. Two in-house fuzzy classifiers, i.e., the fuzzy rule-building expert system (FuRES) and the fuzzy optimal associative memory (FOAM) for the first time, were used to build classification models. Two crisp classifiers, i.e., soft independent modeling by class analogy (SIMCA) and the partial least-squares discriminant analysis (PLS-DA), were used as control methods. Prior to data processing, baseline correction and retention time alignment were performed. Classifiers were built with the two-way data sets, the total ion chromatogram representation of data sets, and the total mass spectrum representation of data sets, separately. Bootstrapped Latin partition (BLP) was used as an unbiased evaluation of the classifiers. By using two-way data sets, average classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0%, respectively, for 100 independent evaluations. The established classifiers were used to classify a new validation set collected 2.5 months later with no parametric changes except that the training set and validation set were individually mean-centered. For the new two-way validation set, classification rates with FuRES, FOAM, SIMCA, and PLS-DA were 100%, 83%, 97%, and 100%, respectively. Thereby, the GC/MS analysis was demonstrated as a viable approach for organic basil authentication. It is the first time that a FOAM has been applied to classification. A novel baseline correction method was used also for the first time. The FuRES and the FOAM are demonstrated as powerful tools for modeling and classifying GC/MS data of complex samples and the data pretreatments are demonstrated to be useful to improve the performance of classifiers.

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“…NMM, PF, and IRLS are three BCMs that process each spectrum individually (refer to Introduction). They are publicly available in an R package called baseline 33 . Five different window sizes (10,25,50, 100, and 200) were used for NMM, and the best performer, window size 50, was used for comparing to the other methods.…”

Section: Background Removal On the Three Vitamin Dataset: Comparison mentioning

confidence: 99%

“…Some other BCMs do not include assumptions about the shape of the background at individual time points but instead assume that the shape of the background does not change over time 32,33 . They use spectra from all the time points to estimate this common shape of background.…”

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confidence: 99%

A Statistical Approach of Background Removal and Spectrum Identification for SERS Data

Wang

Xiao

Dai

et al. 2020

Sci Rep

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SERS (surface-enhanced Raman scattering) enhances the Raman signals, but the plasmonic effects are sensitive to the chemical environment and the coupling between nanoparticles, resulting in large and variable backgrounds, which make signal matching and analyte identification highly challenging. Removing background is essential, but existing methods either cannot fit the strong fluctuation of the SeRS spectrum or do not consider the spectra's shape change across time. Here we present a new statistical approach named SABARSI that overcomes these difficulties by combining information from multiple spectra. Further, after efficiently removing the background, we have developed the first automatic method, as a part of SABARSI, for detecting signals of molecules and matching signals corresponding to identical molecules. The superior efficiency and reproducibility of SABARSI are shown on two types of experimental datasets. Surface-enhanced Raman scattering (SERS) is increasingly used to identify and quantify biomolecules in complex samples 1 because the observed Raman spectrum provides a molecular fingerprint that can be used to identify specific molecules. Advances in SERS methodology incorporating internal standards enables quantitative analysis at low concentrations. Incorporating SERS with separation methods can provide high throughput molecularly specific detection 2-6. A significant challenge to using SERS for molecular analysis is separating the molecular signal from the large background arising from the enhancing nanostructure. The enhanced signal originates from the interaction of analytes with the enhanced electromagnetic field from the plasmonic nanostructures 7. These enhancements transform Raman scattering into an ultrasensitive technique that can detect single molecules 8,9. Despite this amazing sensitivity associated with SERS, a number of challenges exist that complicate analysis and interpretation of the signals observed. First, SERS signals contain both molecular contributions and a large continuum background that is associated with the plasmonic nanostructures 10-13. The origin of the continuum background observed in SERS spectra is not fully understood but is generally attributed to some form of plasmonic emission, which can vary with solvents, ionic strength, and changes in nanoparticle structure. At high laser intensities, molecules can photodegrade to produce broad features in the SERS spectrum, and the nanoparticles can change shape altering the emission background. Experiments that can minimize these photodegradation effects 14,15 are important and can also promote stable backgrounds. Additionally, in solution, the molecules can diffuse away from the nanostructures and can have competitive interactions with other solution species 16. These interactions can lead to short signal durations when the analyte can be detected 17. The substrate, solvent, and analytes of interest all make major contributions to a SERS spectrum 18. Typically, the contributions to the signal from the substrate and solvent a...

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Baseline Correction Method Using an Orthogonal Basis for Gas Chromatography/Mass Spectrometry Data

Cited by 34 publications

References 34 publications

Classification of Cultivation Locations of Panax quinquefolius L Samples using High Performance Liquid Chromatography–Electrospray Ionization Mass Spectrometry and Chemometric Analysis

Classification of Cultivation Locations of Panax quinquefolius L Samples using High Performance Liquid Chromatography–Electrospray Ionization Mass Spectrometry and Chemometric Analysis

Authentication of Organically and Conventionally Grown Basils by Gas Chromatography/Mass Spectrometry Chemical Profiles

A Statistical Approach of Background Removal and Spectrum Identification for SERS Data

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