Application of Chromatographic Fingerprint to Quality Control for Clematis chinensis

The quality control of Saussurea involucrata has been greatly improved by macroscopic and microscopic identification and chemical profiling described in Chinese Pharmacopoeia since 2005. However, these methods have their own limitations, e.g., their dependence on personal experience and expertise, and it is a huge challenge to identify closely related species that share similar or identical morphological characteristics and chemical profiles. A novel and generally accepted identification strategy is urgently needed as a complement to regulations for protecting the public health interests. In this work, a comprehensive chromatographic fingerprint method was developed and tested on 43 samples from four haplotypes of S. involucrata according to DNA barcoding. Three common patterns consisting of 20, 14, and 7 common peaks were generated by frequency filters of median, upper quartile, and 100%, respectively. Based on two formerly screened patterns, S. involucrata can be effectively identified from its five easily confused snow lotus species, including the most closely related plant (S. orgaadayi) in the orthogonal partial least-squares discriminant analysis (OPLS-DA) models. The model is supported by good R and Q coefficients. In addition, different haplotypes of S. involucrata can be discriminated in the OPLS-DA model using the 20 common peaks. Among them, peaks 9, 11, 16 (zaluzanin C), and 18 (dehydrocostus lactone) have been identified as fingerprint markers of S. involucrata via S-plots and VIP values (>1). Additionally, peaks 19 and 20 were identified as linolenic acid and linoleic acid with anti-inflammatory activity, and they were isolated from the herb for the first time. Collectively, the chromatographic fingerprint of S. involucrata can be an effective and integrated method for the identification of authentic herbs from adulterant species or related plants, and discrimination of its different haplotypes provides an objective and reliable tool for quality control.

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Quality Control of Mutton by Using Volatile Compound Fingerprinting Techniques and Chemometric Methods

Journal of Food Quality ◽

10.1155/2017/9273929 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Ping Zhan ◽

Honglei Tian ◽

Baoguo Sun ◽

Yuyu Zhang ◽

Haitao Chen

Keyword(s):

Gas Chromatography ◽

Quality Control ◽

Principal Component ◽

Chemical Markers ◽

Chemometric Methods ◽

Chromatographic Fingerprint ◽

Gas Chromatography Olfactometry ◽

Gas Chromatography Mass Spectroscopy ◽

Chromatographic Fingerprinting

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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