پديدآورندگان :
Abtahi Mahtab Sharif University of Technology, Tehran , Parastar Hadi h.parastar@sharif.edu Sharif University of Technology, Tehran
كليدواژه :
Chromatographic fingerprinting , Salvia , Multivariate classification , k , nearest neighbors.
چكيده فارسي :
Chromatographic fingerprinting is a common method for classification, authentication
and quality control of natural complex samples such as plant extracts [1]. In this regard,
gas chromatography (GC) is the best option for fingerprinting and identification of
chemical composition of such samples, as GC can provide reliable qualitative and
quantitative information about the sample [2]. On the other hand, due to the complexity
of natural sample matrices and lack of selectivity in analytical instruments, multivariate
chemometric methods have been largely used to extract maximum useful information
from chromatographic fingerprints [1,3]. In the present contribution, a
chemometrics-based strategy is proposed for GC fingerprints analysis of Salvia for
source identification and quality control. On this matter, ultrasonic-assisted
extraction-dispersive liquid-liquid microextraction (UAE-DLLME) was used for
extraction of chemical components of twenty-eight Salvia samples from eight
populations. The optimum extraction conditions were obtained using factorial based
response surface methodology (RSM). The optimum parameters were 60 mg of
powdered aerial parts of dried Salvia sample, 2.5 mL of methanol as first extraction
solvent, 45 min first sonication time, 40°C extraction temperature, 30 μL of
tetrachloroethylene as preconcentration solvent, 2 min second sonication time and NaCl
7% (w/v). The GC profiles were arranged in a data matrix and this data matrix was
autoscaled before cluster analysis. The data was then analyzed using principal
component analysis (PCA), hierarchical cluster analysis (HCA) and k-nearest neighbors
(kNN) clustering methods to explore similarities and dissimilarities among different
Salvia samples according to their secondary metabolites. As an instance, PCA with three
PCs could explain 85.8 % variance of data. In general, three clear-cut clusters were
determined using PCA score plot and HCA and kNN dendrograms. In addition,
according to the PCA loading plot and kNN dendrogram of selected variables by
different variable selection methods, the chemical markers (chemotypes) responsible to
this differentiation were characterized. Finally, a reference chromatographic fingerprint
was developed for each cluster which can be used for quality control. It is concluded
that the proposed strategy in this work can be successfully applied for comprehensive
analysis of chromatographic fingerprints of complex natural samples
