Ranking and similarity for quantitative structure-retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons.

Keywords

Chemistry, Ranking (information retrieval), Principal component analysis, Kovats retention index, Similarity (geometry)

References

• Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbersKároly Héberger, K. Kollár‐Hunek — 2011
• Discrimination between linear and non-linear models for retention indices of polycyclic aromatic hydrocarbons in the so-called Lee's scaleBiljana Škrbić, Nataša Đurišić‐Mladenović, Jelena Cvejanov — 2004
• Conditional Fisher's exact test as a selection criterion for pair-correlation method. Type I and Type II errorsRóbert Rajkó, Károly Héberger — 2001
• Comparison of physicochemical and gas chromatographic polarity measures for simple organic compounds.Károly Héberger, И. Г. Зенкевич — 2010
• Factor Analysis in ChemistryLarry G. Blackwood, Edmund R. Malinowski — 1980
• Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network.Biljana Škrbić, Antonije Onjia — 2006
• Sum of ranking differences compares methods or models fairlyKároly Héberger — 2010
• Quantitative structure-(chromatographic) retention relationships.Károly Héberger — 2007
• Role of Hansen solubility parameters in solid phase extraction.Katarzyna Bielicka‐Daszkiewicz, Adam Voelkel, Monika Pietrzyńska — 2010
• Molecular structural vector description and retention index of polycyclic aromatic hydrocarbonsShu‐Shen Liu, Chunsheng Yin, Shaoxi Cai — 2002
• Quantitative structure-retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices.Juan Carlos Drosos, Maricela Viola-Rhenals, Ricardo Vivas‐Reyes — 2010
• Testing panel consistency with GCAP method in food profile analysisK. Kollár‐Hunek, J. Heszberger, Zoltán Kókai — 2008
• Retention indices for programmed-temperature capillary-column gas chromatography of polycyclic aromatic hydrocarbonsMilton L. Lee, Daniel L. Vassilaros, Curt M. White — 1979
• Preventing over‐fitting in PLS calibration models of near‐infrared (NIR) spectroscopy data using regression coefficientsAoife Gowen, Gérard Downey, Carlos Esquerre — 2011
• Generalization of pair correlation method (PCM) for non‐parametric variable selectionKároly Héberger, Róbert Rajkó — 2002
• QSRR: Quantitative Structure-(Chromatographic) Retention RelationshipsRoman Kaliszan — 2007
• Factor analysis in chemistry1992
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd EditionT. Hastie, R. Tibshirani, J. Friedman — 2009

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• Radial basis function neural networks based on projection pursuit approach and solvatochromic descriptors: single and full column prediction of gas chromatography retention behavior of polychlorinated biphenylsZeinabe Hassanzadeh, Parastoo Ebrahimi, Mohsen Kompany‐Zareh — 2016

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