A new method for the determination of peak distribution across a two-dimensional separation space for the identification of optimal column combinations

Abstract

For the identification of the optimal column combinations, a comparative orthogonality study of single columns and columns coupled in series for the first dimension of a microscale two-dimensional liquid chromatographic approach was performed. In total, eight columns or column combinations were chosen. For the assessment of the optimal column combination, the orthogonality value as well as the peak distributions across the first and second dimension was used. In total, three different methods of orthogonality calculation, namely the Convex Hull, Bin Counting, and Asterisk methods, were compared. Unfortunately, the first two methods do not provide any information of peak distribution. The third method provides this important information, but is not optimal when only a limited number of components are used for method development. Therefore, a new concept for peak distribution assessment across the separation space of two-dimensional chromatographic systems and clustering detection was developed. It could be shown that the Bin Counting method in combination with additionally calculated histograms for the respective dimensions is well suited for the evaluation of orthogonality and peak clustering. The newly developed method could be used generally in the assessment of 2D separations. Graphical Abstract ᅟ.

Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications

“…Leonhardt and co-workers investigated the performance of different orthogonality metrics. 84 They noted that the convex hull and bin-counting methods do not provide information on the peak distribution, whereas the asterisk method does not work optimally when only a limited number of components are used for method development. The authors therefore introduced a new concept for peak distribution assessment across the separation space of two-dimensional separation systems in combination with clustering detection.…”

“…Work has also been invested in studying some of the quality descriptors themselves. Leonhardt and co-workers investigated the performance of different orthogonality metrics . They noted that the convex hull and bin-counting methods do not provide information on the peak distribution, whereas the asterisk method does not work optimally when only a limited number of components are used for method development.…”

Compensate for or Minimize Matrix Effects? Strategies for Overcoming Matrix Effects in Liquid Chromatography-Mass Spectrometry Technique: A Tutorial Review

“…However, it is important to develop appropriate method through a rigorous selection of such chromatographic parameters as stationary and mobile phases, column formats, and chromatographic conditions [151]. One of the most important parameters to be assessed is the orthogonality of single and coupled columns, necessary for the determination of the optimal column combination [153].…”

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

“…The authors found that the asterisk metric is less affected by a change in the number of compounds, unlike, for example, the bin-counting approaches, and that its value is less affected by outliers than the convex-hull strategies. More recently, new metrics were developed by Mani-Varnosfaderani and Ghaemmaghami, based on the maximal information coefficient [261], and by Leonhardt et al [262], based on a combination of bin-counting and calculated histograms for the respective dimensions. Mommers and Van der Waals [263] developed two new metrics based on a polynomial fit and a new bin-counting approach.…”

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