Literature DB >> 30226333

[Microscopic and molecular identification of pine needles].

Hengpei Gong1, Zuwang Liu1, Yanyue Chen1, Jian Zhang2, Rubin Cheng1, Zhen Huang1.   

Abstract

OBJECTIVE: To identify pine needles from different plant origins by microscopic and molecular approaches.
METHODS: The characteristics of pine needles of Pinus massoniana Lamb., Pinus thunbergii Parl. and Pinus armandii Franch. were investigated via plant morphology and microscopic characteristics. ITS2 and rbcL were analyzed with PCR amplification and bi-directional sequencing. MEGA 6.0 was used to calculate the intra-and inter-specific Kimura-2-Parameter (K2P) distances, and the phylogenetic tree was constructed by using the neighbor-joining (NJ) method.
RESULTS: There were significant differences in the number and length of pine needles, number of vascular bundles, distribution of stomatal lines, number and distribution of resin channels among three kinds of pine needles. The lengths of ITS2 sequences of Pinus massoniana Lamb., Pinus thunbergii Parl. and Pinus armandii Franch. were 470, 469 and 470 bp, respectively. The lengths of rbcL sequences in three kinds of pine needles were 553 bp. The intraspecific variation rates of ITS2 sequences in Pinus massoniana Lamb., Pinus thunbergii Parl. and Pinus armandii Franch. were 0%, 0.2%, and 2.8%, respectively; and the intraspecific variation rates of rbcL sequences were 0%, 2.4%, and 1.1%, respectively. There was no significant barcoding gap in intraspecific and interspecific genetic distances of ITS2 sequences. The intraspecific and interspecific distances of rbcL sequences were clearly separated in the barcoding gap test. The NJ tree based on rbcL showed that the three pine needles clustered into three separate groups, indicating that rbcL DNA marker could distinguish the Pinus massoniana Lamb., Pinus thunbergii Parl., Pinus armandii Franch. and its close relative species.
CONCLUSIONS: s The three types of pine needles can be distinguished accurately and rapidly by microscopic and molecular identification. The study provides methodology and experimental basis for the quality evaluation and classification of pine needles.

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Year:  2018        PMID: 30226333     DOI: 10.3785/j.issn.1008-9292.2018.06.14

Source DB:  PubMed          Journal:  Zhejiang Da Xue Xue Bao Yi Xue Ban        ISSN: 1008-9292


  1 in total

1.  Applications of machine learning in pine nuts classification.

Authors:  Biaosheng Huang; Jiang Liu; Junying Jiao; Jing Lu; Danjv Lv; Jiawei Mao; Youjie Zhao; Yan Zhang
Journal:  Sci Rep       Date:  2022-05-25       Impact factor: 4.996

  1 in total

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