摘要

The study aimed to distinguish genomic DNAs from nine species of plants belonging to six families and analyze their genetic relationship by using surface-enhanced Raman scattering (SERS). The silver nanocolloid and excitation wavelength of 785 nm used in this study yielded excellent quality of the SERS spectra. Raman signals were remarkably enhanced. Although the spectra for the nine species of plants appeared very similar, there were significant differences according to the analysis of variance analysis. There were three strong characteristic peaks. The peak at 625 cm(-1) was due to the vibration overlap of C3'-endo/anti deoxyribose, cytosine, and guanine; the one at 715 cm(-1) was due to the scissoring vibrations of C2-N1-C6 of adenine; and that at 1011 cm(-1) was due to the stretching vibration of the C-O bond of deoxyribose and vibrations of cytosine. The SERS data were smoothed and standardized and evaluated using second derivative analysis, principal component analysis, and hierarchical cluster analysis. A model was established using the data from hierarchical cluster analysis and principal components of the second derivative. The clustering result of this model was highly consistent with the traditional classification of plants; all plant species investigated were correctly clustered into classes according to the cluster distance coefficient among them; the accuracy of clustering was 100%. Chinese cabbage (Brassica pekinensis Rupr.) and green cabbage (Brassica chinensis L.) belonging to Cruciferae, maize (Zea mays L) and bamboo (Sinocalamus affinis McClure) belonging to Gramineae, and magnolia (Magnolia delavayi Franch.) and champaca (Michelia alba DC.) belonging to Magnoliaceae were clustered into three separate classes, and fern (Nephrolepis auriculata L., Nephrolepidaceae), garlic (Allium sativum L, Amaryllidaceae), and ginkgo (Ginkgo biloba L, Ginkgoaceae) were each clustered into separate classes. These findings suggest that the SERS spectra of plant genomic DNAs can be used to classify species and analyze their genetic relationship. It is an effective and perfect supplement to traditional classification and can form the basis for genetic analysis.