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1.
Ecotoxicol Environ Saf ; 208: 111691, 2021 Jan 15.
Article in English | MEDLINE | ID: mdl-33396023

ABSTRACT

DNA barcoding is an emerging molecular identification and classification technology that has been applied to medicinal plants since 2008. The application of this technique has greatly ensured the safety and effectiveness of medicinal materials. In this paper, we review the application of DNA barcoding and some related technologies over the past 10 years with respect to improving our knowledge of medicinal plant identification and authentication. From single locus-based DNA barcodes to combined markers to genome-scale levels, DNA barcodes contribute more and more genetic information. At the same time, other technologies, such as high-resolution melting (HRM), have been combined with DNA barcoding. With the development of next-generation sequencing (NGS), metabarcoding technology has also been shown to identify species in mixed samples successfully. As a widely used and effective tool, DNA barcoding will become more useful over time in the field of medicinal plants.


Subject(s)
DNA Barcoding, Taxonomic/methods , DNA, Plant/genetics , Plants, Medicinal/genetics , High-Throughput Nucleotide Sequencing
2.
J Agric Food Chem ; 68(49): 14643-14651, 2020 Dec 09.
Article in English | MEDLINE | ID: mdl-33252222

ABSTRACT

In response to the need from the food industry for new analytical solutions, a fit-for-purpose quantitative 1H NMR methodology was developed to authenticate pure coffee (100% arabica or robusta) as well as predict the percentage of robusta in blends through the study of 292 roasted coffee samples in triplicate. Methanol was chosen as the extraction solvent, which led to the quantitation of 12 coffee constituents: caffeine, trigonelline, 3- and 5-caffeoylquinic acid, lipids, cafestol, nicotinic acid, N-methylpyridinium, formic acid, acetic acid, kahweol, and 16-O-methylcafestol. To overcome the chemical complexity of the methanolic extract, quantitative analysis was performed using a combination of traditional integration and spectral deconvolution methods. As a result, the proposed methodology provides a systematic methodology and a linear regression model to support the classification of known and unknown roasted coffees and their blends.


Subject(s)
Coffea/chemistry , Magnetic Resonance Spectroscopy/methods , Alkaloids/analysis , Caffeine/analysis , Coffea/classification , Coffee/chemistry , Cooking , Discriminant Analysis , Diterpenes/analysis , Food Contamination/analysis , Seeds/chemistry , Seeds/classification
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