Selection of orthogonal chromatographic systems based on parametric and non-parametric statistical tests

Systematic Evaluation of Liquid Chromatography (LC) Column Combinations for Application in Two-Dimensional LC Metabolomic Studies

In comparison to proteomics, the application of two-dimensional liquid chromatography (2D LC) in the field of metabolomics is still premature. One reason might be the elevated chemical complexity and the associated challenge of selecting proper separation conditions in each dimension. As orthogonality of dimensions is a major issue, the present study aimed for the identification of successful stationary phase combinations. To determine the degree of orthogonality, first, six different metrics, namely, Pearson's correlation coefficient (1 - |R|), the nearest-neighbor distances (H̅_NND), the "asterisk equations" (A_O), and surface coverage by bins (SC_G), convex hulls (SC_CH), and α-convex hulls (SC_αH), were critically assessed by 15 artificial 2D data sets, and a systematic parameter optimization of α-convex hulls was conducted. SG_G, SC_αH with α = 0.1, and H̅_NND generated valid results with sensitivity toward space utilization and data distribution and, therefore, were applied to pairs of experimental retention time sets obtained for >350 metabolites, selected to represent the chemical space of human urine. Normalized retention data were obtained for 23 chromatographic setups, comprising reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), and mixed-mode separation systems with an ion exchange (IEX) contribution. As expected, no single LC setting provided separation of all considered analytes, but while conventional RP×HILIC combinations appeared rather complementary than orthogonal, the incorporation of IEX properties into the RP dimension substantially increased the 2D potential. Eventually, one of the most promising column combinations was implemented for an offline 2D LC time-of-flight mass spectrometry analysis of a lyophilized urine sample. Targeted screening resulted in a total of 164 detected metabolites and confirmed the outstanding coverage of the 2D retention space.

Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method

The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.

Dissimilar or orthogonal reversed-phase chromatographic systems: A comparison of selection techniques

Developing an analytical separation procedure for an unknown mixture is a challenging issue. An important example is the separation and quantification of a new drug and its impurities. One approach to start method development is the screening of the mixture on dissimilar chromatographic systems, i.e. systems with large selectivity differences. After screening, the most suited system is retained for further method development. In a step prior to such strategy dissimilar chromatographic systems need to be selected. In this paper the performance of different chemometric selection approaches, described in the literature, was visually evaluated and compared. Additionally, orthogonal projection approach (OPA) was tested as another potential selection method. All techniques, including the OPA method, were able to select (a set of) dissimilar chromatographic systems and many similarities between the selections were observed. However, the Kennard and Stone algorithm performed best in selecting the most dissimilar systems in the earliest steps of the selection procedure. The generalized pairwise correlation method (GPCM) and the auto-associative multivariate regression trees (AAMRT) were also performing well. OPA and weighted pair group method using arithmetic averages (WPGMA) are less preferable.

Relationship Between Body Weight and Beak Characteristics in One-Day-Old White Leghorn Chicks: Its Implications for Beak Trimming

Beak trimming is a routine practice used in laying hens to prevent feather pecking and cannibalism. The effect of beak trimming on bird well-being depends on multiple factors, including the amount of beak that is trimmed and the quality of the procedure. The aim of this work was to determine if a relationship existed between BW and beak characteristics in 1-d-old chicks, with a future aim to develop an automated system for standardizing beak trimming. Three hundred forty-four 1-d-old chicks (Hy-Line W-36) were sorted into 3 categories based on their BW (heavy, intermediate, and light), and their beaks were photographed. Dimensional measures of beaks, including the lengths of the culmen, gonys, maxillary tomia, mandibular tomia, and the width of the upper mandible and lower mandible measured at 2, 3, and 4 mm from the tip of the upper and lower beaks, were calculated using imaging software. Correlations between BW and beak measures were evaluated using Pearson product-moment, Spearman rank-order, Kendall's tau, and Hoeffding's dependency tests. Results showed there were no significant correlations between beak dimensions and BW in the light BW group. In contrast, correlations were present between BW and the width of the upper mandible measured at 4 mm from the tip of the upper beak (P < 0.05) and the width of the lower mandible measured 2 to 4 mm from the tip of the lower beak (P < 0.05) in the intermediate BW group. In the heavy BW group, BW was positively correlated with mandibular tomia, gonys, and the width of the lower mandible measured at 2 mm from the tip of the lower beak (P < 0.05). However, in general, these correlations were too low (all below 0.23) to have any practical use for predicting beak size. Overall, the data indicated that BW cannot be used as a reliable predicator of beak size in 1-d-old Hy-Line W-36 chicks.

Evaluation of chemometric techniques to select orthogonal chromatographic systems

Several chemometric techniques were compared for their performance to determine the orthogonality and similarity between chromatographic systems. Pearson's correlation coefficient (r) based color maps earlier were used to indicate selectivity differences between systems. These maps, in which the systems were ranked according to decreasing or increasing dissimilarities observed in the weighted-average-linkage dendrogram, were now applied as reference method. A number of chemometric techniques were evaluated as potential alternative (visualization) methods for the same purpose. They include hierarchical clustering techniques (single, complete, unweighted-average-linkage, centroid and Ward's method), the Kennard and Stone algorithm, auto-associative multivariate regression trees (AAMRT), and the generalized pairwise correlation method (GPCM) with McNemar's statistical test. After all, the reference method remained our preferred technique to select orthogonal and identify similar systems.

Selection of orthogonal reversed-phase HPLC systems by univariate and auto-associative multivariate regression trees

In order to select chromatographic starting conditions to be optimized during further method development of the separation of a given mixture, so-called generic orthogonal chromatographic systems could be explored in parallel. In this paper the use of univariate and multivariate regression trees (MRT) was studied to define the most orthogonal subset from a given set of chromatographic systems. Two data sets were considered, which contain the retention data of 68 structurally diversive drugs on sets of 32 and 38 chromatographic systems, respectively. For both the univariate and multivariate approaches no other data but the measured retention factors are needed to build the decision trees. Since multivariate regression trees are used in an unsupervised way, they are called auto-associative multivariate regression trees (AAMRT). For all decision trees used, a variable importance list of the predictor variables can be derived. It was concluded that based on these ranked lists, both for univariate and multivariate regression trees, a selection of the most orthogonal systems from a given set of systems can be obtained in a user-friendly and fast way.