Linear Correlations of the Gas Chromatographic Retention Indices of Compounds from Various Taxonomic Groups

• Published in: Journal of analytical chemistry (New York, N.Y.) Vol. 78; no. 6; pp. 766 - 775
• Main Author: Zenkevich, I. G.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.06.2023; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Analytical Chemistry; Chemistry; Chemistry and Materials Science; Chromatography; Correlation; Correlation (Statistics); Gas chromatography; Methods; Retention; Substitutes; Taxonomy; Zoological specimens; Russia; taxonomic groups of structural analogs; linear correlation; gas chromatographic retention indices; index correctness control; estimation of unknown values
• ISSN: 1061-9348; 1608-3199
• DOI: 10.1134/S1061934823040160

Despite the constant improvement of complex computer algorithms for the calculation of gas chromatographic retention indices (RIs), the simplest methods for their evaluation based on linear correlations between the indices of structural analogs from different taxonomic groups, RI 1 ≈ a RI 2 + b , remain important. It is shown that symbatic variations of the first numerical differences of retention indices, = RI n + 1 – RI n (equivalent to the first derivatives of the retention indices with respect to the structural parameter varied in the group), are the conditions of correctness for such correlations in the simplest groups (substituted methanes). A monotonic variation of in one of the groups with the presence of extrema in the other group is an unequivocal sign of the absence of a linear correlation between retention indices. If the values of in one of the groups increase and decrease in the other, the ranking order of compounds in any one of them should be reversed. It is shown that the simplest relationship RI 1 ≈ a RI 2 + b is also applicable to more complex taxonomic groups (substituted ethanes, benzenes, and naphthalenes), and it allows one not only to estimate the RIs of compounds not yet characterized but also to refine known reference data.