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Ghorbani-Kalhor, E., Abolhasani, J., Veladi, M. (2015). Quantitative Analysis of Mixtures of Monoprotic Acids Applying Modified Model-Based Rank Annihilation Factor Analysis on Variation Matrices of Spectrophotometric Acid-Base Titrations. Journal of Chemical Health Risks, 5(2), -. doi: 10.22034/jchr.2015.544096
Ebrahim Ghorbani-Kalhor; Jafar Abolhasani; Mahsa Veladi. "Quantitative Analysis of Mixtures of Monoprotic Acids Applying Modified Model-Based Rank Annihilation Factor Analysis on Variation Matrices of Spectrophotometric Acid-Base Titrations". Journal of Chemical Health Risks, 5, 2, 2015, -. doi: 10.22034/jchr.2015.544096
Ghorbani-Kalhor, E., Abolhasani, J., Veladi, M. (2015). 'Quantitative Analysis of Mixtures of Monoprotic Acids Applying Modified Model-Based Rank Annihilation Factor Analysis on Variation Matrices of Spectrophotometric Acid-Base Titrations', Journal of Chemical Health Risks, 5(2), pp. -. doi: 10.22034/jchr.2015.544096
Ghorbani-Kalhor, E., Abolhasani, J., Veladi, M. Quantitative Analysis of Mixtures of Monoprotic Acids Applying Modified Model-Based Rank Annihilation Factor Analysis on Variation Matrices of Spectrophotometric Acid-Base Titrations. Journal of Chemical Health Risks, 2015; 5(2): -. doi: 10.22034/jchr.2015.544096

Quantitative Analysis of Mixtures of Monoprotic Acids Applying Modified Model-Based Rank Annihilation Factor Analysis on Variation Matrices of Spectrophotometric Acid-Base Titrations

Article 1, Volume 5, Issue 2, Summer 2015  XML PDF (827.36 K)
DOI: 10.22034/jchr.2015.544096
Authors
Ebrahim Ghorbani-Kalhor email ; Jafar Abolhasani; Mahsa Veladi
Department of Chemistry, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Receive Date: 08 April 2015,  Revise Date: 19 June 2019,  Accept Date: 29 October 2018 
Abstract
In the current work, a new version of rank annihilation factor analysis was developedto circumvent the rank deficiency problem in multivariate data measurements.Simultaneous determination of dissociation constant and concentration of monoprotic acids was performed by applying model-based rank annihilation factor analysis on variation matrices of spectrophotometric acid-base titrations data. Variation matrices can be obtained by subtracting first row of data matrix from all rows of the main data matrix. This method uses variation matrices instead of multivariate spectrophotometric acid-base titrations matrices to circumvent the rank deficiency problem in the rank quantitation step. The applicability of this approach was evaluated by simulated data at first stage, then the binary mixtures of ascorbic and sorbic acids as model compounds were investigated by the proposed method. At the end, the proposed method was successfully applied for resolving the ascorbic and sorbic acid in an orange juice real sample. Therefore, unique results were achieved by applying rank annihilation factor analysis on variation matrix and using hard soft model combination advantage without any problem and difficulty in rank determination.   
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