Element-tracing of mineral matters in Dendrobium officinale using ICP-MS and multivariate analysis

Springerplus. 2016 Jul 4;5(1):979. doi: 10.1186/s40064-016-2618-2. eCollection 2016.

Abstract

Rare studies have been performed to trace the mineral elements in Dendrobium officinale. In this study, we aim to trace the mineral elements in D. officinale collected from ten geographical locations in China. ICP-MS system was used for simultaneous determination of mineral elements. Principal component analysis was performed using the obtained data in the quantification of mineral contents. Cluster analysis was performed using the Ward's method. Several of essential microelments were detected in D. officinale, including ferrum (Fe), manganese (Mn), zinc (Zn), chromium (Cr), nickel (Ni) and vanadium (V). Among these elements, three elements (i.e. Fe, Mn and Zn) were highly and simultaneously detected in the D. officinale collected from the ten locations. The level of Ni was positively associated with that of Zn (r = 0.986, P < 0.01). The level of titanium (Ti) was positively associated with that of V (r = 0.669, P < 0.05), and negatively associated with Cr (r = -0.710, P < 0.05). In addition, the level of Mn was positively associated with that of barium (r = 0.749, P < 0.05). Further, the level of Fe was positively associated with that of Ni (r = 0.664, P < 0.05), Zn (r = 0.742, P < 0.05), and rare earth elements (r = 0.847, P < 0.01), respectively. Three eigenvalues explained about 86.60 % of the total variance, which contributed significantly to the explanation of cumulative variance. Cluster analysis indicated the cultivars were categorized into 3 clusters. Ni, Zn, Fe, Cr, Ti and rare earth elements were designated as the characteristic elements. Cultivars collected from Yulin, Menghai, and Shaoguan ranked the top 3 in the comprehensive scores, indicating the content of the mineral elements was comparatively higher in these locations.

Keywords: Cluster analysis; Dendrobium officinale; ICP-MS; Mineral elements; Principal component analysis.