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Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran
Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran
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Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran
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Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran
Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran

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Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran
Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran
Journal Article

Unidentifiable by morphology: DNA barcoding of plant material in local markets in Iran

2017
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Overview
Local markets provide a rapid insight into the medicinal plants growing in a region as well as local traditional health concerns. Identification of market plant material can be challenging as plants are often sold in dried or processed forms. In this study, three approaches of DNA barcoding-based molecular identification of market samples are evaluated, two objective sequence matching approaches and an integrative approach that coalesces sequence matching with a priori and a posteriori data from other markers, morphology, ethnoclassification and species distribution. Plant samples from markets and herbal shops were identified using morphology, descriptions of local use, and vernacular names with relevant floras and pharmacopoeias. DNA barcoding was used for identification of samples that could not be identified to species level using morphology. Two methods based on BLAST similarity-based identification, were compared with an integrative identification approach. Integrative identification combining the optimized similarity-based approach with a priori and a posteriori information resulted in a 1.67, 1.95 and 2.00 fold increase for ITS, trnL-F spacer, and both combined, respectively. DNA barcoding of traded plant material requires objective strategies to include data from multiple markers, morphology, and traditional knowledge to optimize species level identification success.

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