Multivariate curve resolution with alternating least squares optimization (MCR-ALS), as a soft modeling approach based on factor analysis, was proposed to recover the thorough gas chromatography-mass spectrometry (GC-MS) fingerprint analysis of volatile chemical constituents in Iranian tea. This technique for two-dimensional data was intended to resolve the overlapping and/or embedded GC-MS peaks into the pure chromatogram and mass spectrum of each chemical constituent, overcoming some challenging fundamental chromatographic problems occurring during GC-MS analysis of the Iranian tea chromatographic fingerprint, such as spectral background, baseline offset, and type of noise. In this way, the chromatographic fingerprints of Iranian C. aurantium peel are properly segmented to the appropriate chromatographic regions, and then MCR-ALS is used to achieve pure response profiles of the chemical constituents in each segment, as well as their relative concentrations. Retention indices, together with mass spectral profiles of pure chemical constituents, were considered for qualitative identification by matching against the standard ones through MS library searching; an overall volume integration (OVI) technique was also used for the semi-quantitative analysis (to obtain the relative concentrations of chemical constituents)
