characterization of the volatile profile of iranian tea using chromatographic fingerprint combined with multivariate curve resolution: toward improving quality control and authentication

Gc-ms fingerprint analysis plays an outstanding role for characterizing and authenticating of unknown metabolites in complex natural products [1]. however, efficient acquisition of the required information about the components from a gc-ms-based dataset remains a challenging task because of fundamental problems including baseline drift, spectral background, various types of noises, peak shape deformation (non-gaussian peaks), low s/n ratio and co-elution peaks that may mislead similarity searches in the standard mass spectral library, even under the best experimental conditions [2]. the main strategy for solving these problems is chemometrics approaches such as, multivariate curve resolution (mcr). the ultimate goal of curve resolution would be to be able to determine the number of components in an overlapping chromatographic peak as well as the spectrum and concentration profile of each compound [3]. in recent years, the multivariate curve resolution-alternating least squares (mcr-als) method, based on factor analysis, has been shown to be a powerful tool for the peak resolution of the screened tic peaks to obtain the pure chromatographic profiles and pure mass spectra [4]. this study aimed to give a comprehensive characterization of volatile chemical constituents of iranian tea using chemometric technique due to its ability for resolving the co-eluted peaks of the gc-ms-based data. the results indicated that the gc-mass fingerprint combined with multivariate curve resolution–alternating least squares will open a more full-scale look at the volatile profile of iranian tea and provide valuable guidance of its chemical constituents.