A comprehensive strategy using chromatographic profiles combined with chemometric methods: Application to quality control of Polygonum cuspidatum Sieb. et Zucc

A method for chromatographic fingerprinting of flavor was established for the quality control of mutton. Twenty-five mutton samples that were chosen from twelve batches were investigated by gas chromatography-mass spectroscopy (GC-MS) and gas chromatography-olfactometry (GC-O). Spectral correlative chromatograms combined with GC-O assessment were employed, and 32 common odor-active compounds that characterize mutton flavor fingerprint were obtained. Based on the flavor chromatographic fingerprint data, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were designed and employed as chromatographic fingerprint methods. Defined categories were perfectly discriminated after PLS-DA was conducted on the fused matrix, demonstrating a 100% accurate classification. Fourteen constituents were further screened with PLS-DA to be the main chemical markers, and they were used to develop similar approaches for the determination of mutton quality and traceability. The flavor fingerprint of mutton established using SPME-GC-MS/O coupled with PLS-DA is appropriate for differentiating and identifying samples, and the procedure would be used in quality control.

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Application of fingerprint combined with quantitative analysis and multivariate chemometric methods in quality evaluation of dandelion ( Taraxacum mongolicum )

Royal Society Open Science ◽

10.1098/rsos.210614 ◽

2021 ◽

Vol 8 (10) ◽

Author(s):

Chunying Li ◽

Yao Tian ◽

Chunjian Zhao ◽

Shen Li ◽

Tingting Wang ◽

...

Keyword(s):

Quality Control ◽

Quantitative Analysis ◽

Herbal Medicines ◽

Hierarchical Cluster ◽

Assessment Method ◽

Chemometric Methods ◽

External Standard Method ◽

Significant Difference ◽

Quality Assessment Method ◽

Single Marker

A quality assessment method based on quantitative analysis of multi-components by single marker (QAMS) and fingerprint was constructed from 15 batches of dandelion ( Taraxacum mongolicum ), using multivariate chemometric methods (MCM). MCM were established by hierarchical cluster analysis (HCA) and factor analysis (FA). HCA was especially performed using the R language and SPSS 22.0 software. The relative correction factors of chlorogenic acid, caffeic acid, p-coumaric acid, luteolin and apigenin were calculated with cichoric acid as a reference, and their contents were determined. The differences between external standard method (ESM) and QAMS were compared. There was no significant difference ( t -test, p > 0.05) in quantitative determination, proving the consistency of the two methods (QAMS and ESM). Dandelion material from Yuncheng, Shandong was used as a reference chromatogram. The fingerprints in 15 batches of dandelion were established by HPLC analysis. The similarity of the fingerprints in different batches of dandelion material was greater than or equal to 0.82. A total of 10 common peaks were identified. This strategy is simple, rapid and efficient in multiple component detection of dandelion. It is beneficial in simplifying dandelion's quality control processes and providing references to enhance quality control for other herbal medicines.

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Development of the mass spectral fingerprint by headspace-solid-phase microextraction-mass spectrometry and chemometric methods for rapid quality control of flavoring essence

Microchemical Journal ◽

10.1016/j.microc.2016.04.011 ◽

2016 ◽

Vol 128 ◽

pp. 75-83 ◽

Cited By ~ 9

Author(s):

Wenqian Hou ◽

Yonghui Tian ◽

Tougeng Liao ◽

Yanping Huang ◽

Zhentao Tang ◽

...

Keyword(s):

Mass Spectrometry ◽

Quality Control ◽

Solid Phase Microextraction ◽

Solid Phase ◽

Chemometric Methods ◽

Headspace Solid Phase Microextraction ◽

Mass Spectral

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Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths

Analytical Methods ◽

10.1039/d0ay00295j ◽

2020 ◽

Vol 12 (25) ◽

pp. 3260-3267

Author(s):

Ileana M. Simion ◽

Augustin-C. Moţ ◽

Costel Sârbu

Keyword(s):

Medicinal Plant ◽

Hierarchical Clustering ◽

Plant Extracts ◽

Clustering Algorithm ◽

Chemometric Methods ◽

Fuzzy C Means ◽

Divisive Hierarchical Clustering ◽

Medicinal Plant Extracts ◽

Hierarchical Clustering Algorithm ◽

Chromatographic Profiles

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), have been successfully applied in this study.

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