Analysis of solvents mixtures employing rank annihilation factor analysis on near infrared spectral data from sequential addition of analyte Mohsen Kompany-Zareh ⁎, Mahdi Vasighi Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, P. O. Box 45195-1159, Iran abstract article info Article history: Received 25 September 2008 Received in revised form 10 May 2009 Accepted 20 August 2009 Keywords: Near-infrared Rank annihilation factor analysis Mixture of organic solvents Gasoline Determination of the analyte concentration in the presence of unknown interfering species is the goal in chemometrics techniques such as rank annihilation factor analysis (RAFA). In this work, using a hard model for sequential addition of analyte to an unknown mixture and a RAFA based approach, concentration of one component in a mixture of organic solvents was determined. Application of hard models causes a unique result from RAFA. The considered analyte was added sequentially to the unknown mixture in a number of steps and a near-infrared (NIR) spectrum was measured in each step. The required information for performing the analysis is the pure spectrum of the analyte (the desired solvent) and density (g mL -1 ) of mixtures obtained from each step of addition. Acceptable results from analysis of simulated data and binary mixture of toluene and cyclohexene, as a synthetic sample, were obtained. Effect of extent of spectral overlap, noise and initial mass fraction of analyte on accuracy and precision of the obtained results were investigated. Quantification of toluene in Gasoline, as a real sample with unknown composition, was successfully performed by the proposed method. © 2009 Elsevier B.V. All rights reserved. 1. Introduction In many conditions quantitative analysis involves the spectro- scopic resolution of mixtures of two components with partially overlapped spectra. This has been the subject of a number of chemometric studies originally purposed for the resolution of binary mixtures and extended to systems with three or more components [1,2]. The multicomponent linear additive model is often used in resolving mixtures. It is essential that the measured response include only the contributions of the known components of the sample. This requires not only the knowledge of the analytes of interest, but also of all interferents potentially present to estimate the concentrations of several components in a mixture [3,4]. The method is not valid in the presence of additive and/or multiplicative interferences in the sample. The method of standard additions (MOSA) could remove the error resulting from the sample matrix (multiplicative interference), but it cannot remove the constant error resulting from other unknown components in the system (additive interference)[5]. The generalized standard addition method (GSAM) [6,7] is a multivariate extension of the conventional standard addition method for simultaneous multi- component determinations in presence of additive and multiplicative error, from presence of unknown components in the sample. The main point in GSAM is that changes in analytical signal due to standard addition should just relate to changes in analytes concen- tration. GSAM overcomes the constraint that the analytical method must be fully selective to the analyte of interest. A reliable analysis requires the absence of any unaccountable source of signal beyond the calibration structure. H-point standard addition method (HPSAM) is based on dual wavelength spectrophotometry and the standard addition method [8,9]. The greatest advantage of HPSAM is its ability to eliminate the errors resulting from the presence of an interfering and blank reagent [10]. The method compensates for both multiplicative and additive errors. However, in order to choose a proper pair of wavelengths, spectrum of blank interference should be know. H-point curve isolation method (HPCIM) for analysis of a binary mixture with a known component B and an unknown one A was proposed by Campins-Falco and co-workers [11]. It is only necessary to have the analyte and the sample spectra. The method cancels the contribution of the B signal in the sample signal and thereby giving a set of possible spectra for the species A. From this set, the real A spectrum can be calculated by finding pairs of wavelengths according to the calibration model of the HPSAM. In the presence of multiplicative error, and occurrence of any change in the spectrum of analyte B in the sample, the method is not applicable. Frequently, there exists extensive spectral overlap between components and unknown background and interferences that make the analysis difficult using above methods. HPCIM is not a general solution method and in many circumstances does not perform well. Obtaining a unique spectrum for A, s A , from spectra of the sample and B (s samp and s B ) by HPCIM is like obtaining a unique solution for s A from equation s samp =b s B +a s A , in which a, b, and s A are unknowns! Fuel Processing Technology 91 (2010) 62–67 ⁎ Corresponding author. Tel.: +98 241 4153123; fax: +98 241 4153232. E-mail address: kompanym@iasbs.ac.ir (M. Kompany-Zareh). 0378-3820/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.fuproc.2009.08.015 Contents lists available at ScienceDirect Fuel Processing Technology journal homepage: www.elsevier.com/locate/fuproc