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

Sun, Xiaobo; Chen, Pei; Cook, Shannon L.; Jackson, Glen P.; Harnly, James M.; Harrington, Peter de B.

doi:10.1021/ac2032832

Cited by 35 publications

(22 citation statements)

References 46 publications

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“…29 In a PLS model, the number of latent variables is selected, which yields the best prediction rates with respect to minimizing the root-mean-square prediction error. 22 PLS-DA is a particular case of PLS, in which Y is a set of binary variables describing the categories of categorical variables on X . 29 The number of response variables is equal to the number of categories.…”

Section: Theorymentioning

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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Section: Theorymentioning

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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“…RTs in the modeling set were aligned with an algorithm that uses polynomial interpolation to align the mass spectra with respect to RT so that the correlation with the mean spectrum is maximized. Each object in all the other data sets was aligned to the two‐way average of the modeling set …”

Section: Methodsmentioning

confidence: 99%

Application of chemometrics to resolve overlapping mass spectral peak clusters between trichloroethylene and its deuterated internal standard

Zhang

Harrington

2015

Rapid Comm Mass Spectrometry

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This paper describes the first application of CLS to deconvolute overlapping peaks between an analyte and its corresponding isotopic internal standard for quantification. The proposed method enables simple isotopic analogs of analytes (one H or C atom is isotopically labeled) to be used as internal standards for analytes with isotopic distributions. It has wide application because of the environmental impact and prevalence of halogenated VOCs, especially when analytes have isotopic distributions that overlap with an internal standard or when sophisticated isotopic analogs of the analytes with three or more (2)H- or/and (13)C-atoms are prohibitively expensive or even impossible.

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“…A useful approach is chemical profiling or spectral fingerprinting of plant extracts [1][2][3][4][5]. Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a more reproducible complementary technique for the identification and quantification of metabolites in plant extracts [6].…”

Section: Introductionmentioning

confidence: 99%

Spectral Representation of Proton NMR Spectroscopy for the Pattern Recognition of Complex Materials

Harrington

Wang

2017

J. Anal. Test.

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Proton nuclear magnetic resonance (NMR) spectroscopy provides a powerful tool for chemical profiling, also known as spectral fingerprinting, because of its inherent reproducibility. NMR is now increasing in use for authentication of complex materials. Typically, the absorbance spectrum is used that is obtained as the phase-corrected real component of the Fourier transform (FT) of the free induction decay (FID). However, the practice discards half the information that is available in the dispersion spectrum obtained as the imaginary component from the FT. For qualitative analysis or quantitative analysis of small sets of absorbance peaks, the symmetric and sharp peaks of the real spectra work well. However, for pattern recognition of entire spectra, trading peak resolution for peak reproducibility is beneficial. The absolute value of the complex spectrum gives the length or magnitude of magnetization vector in the complex plane; therefore, the magnitude relates directly to the signal (i.e., induced magnetization). The magnitude spectrum is obtained as the absolute value from the real and imaginary spectral components after the FT of the FID. By breaking with tradition and using the magnitude spectrum the reproducibility of the spectra and consequent recognition rates can be improved. This study used a 500-MHz 1 H NMR instrument to obtain spectra from 4 diverse datasets; 12 tea extracts, 8 liquor samples, 9 hops extracts, and 25 Cannabis extracts. Six classifiers were statistically evaluated using 100 bootstrapped Latin partitions. The classifiers were a fuzzy rule-building expert system (FuRES) tree, support vector machine trees (SVMTreeG and SVMTreeH), a regularized linear discriminant analysis (LDA), super partial least squares discriminant analysis (sPLS-DA), and a one against all support vector machine (SVM). All classifiers gave better or equivalent results for the magnitude spectral representation than for the real spectra, except for one case of the 24 evaluations. In addition, the enhanced reproducibility of the absolute value spectra is demonstrated by comparisons of the pooled within sample standard deviations. For pattern recognition of NMR spectra, the magnitude spectrum is advocated.

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

Cited by 35 publications

References 46 publications

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

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

Application of chemometrics to resolve overlapping mass spectral peak clusters between trichloroethylene and its deuterated internal standard

Spectral Representation of Proton NMR Spectroscopy for the Pattern Recognition of Complex Materials

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