Intro

Mchrom Scout allows you to develop chromatographic separations for very different types of mixtures of compounds. In many cases, the number and the nature of these components are known and peaks can be tracked along the chromatograms during the optimization process. These will be called type I (known or named peaks) projects and we will give you some hints here to get you started.

Additionally, Mchrom Scout also works for mixtures of unknown number and nature of components. In these cases, only the number and/or the relative position of peaks appearing in the chromatogram and their shape with the time needed to complete the elution, provide information about the chromatographic separation. These will be called type II (blind or unnamed peaks) projects. We recommend you to get familiar with type I projects before you move to type II projects.

The application creates completely independent projects for each case using different strategies and working tools but always within the same interface.

For type I projects, two different approaches are available depending whether you require an isocratic or gradient method. The latter are the simplest ones to work with so let’s start showing you an example of this type of workflow:

Once you open Mchrom Scout application you will be able to start a project using the main menu bar as shown below:

Projects

You can start a new project by selecting the different options:

  • Type I (known or named): Known components “Full peak’s tracking”
    • Scouting gradients
    • Composite (gradient + isocratic)
  • Type II (blind or unnamed): Unknown components “No peak’s tracking”

 

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For currently ongoing projects you can always resume from where you last exited.

When you start a new project, a temporary file is created. Temporary project files have a *.ppry extension. Once the optimization is performed, it becomes a *.prmp file.

Define your components in the mixture

Component names can be either loaded from an external document, written down on the table provided or selected if they were previously added to the component list.

Note that area and asymmetry values can also be loaded if available.

 

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Define your system components

You just need to select the instrument, mobile phase composition and column to be configured.

 

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The software provides a list of available options using Mchrom Scout’s database.

 

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You can also add any record by pressing the “new” button on the instrument manager. This will be added to your Mchrom Scout’s database.

You can do the same for the column of choice for your analysis:

 

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Similarly for the mobile phase, system components definition is common in any kind of type I projects and database dialogue forms are also shared in type II projects.

Once you have added all your main components, you will obtain a new window showing the experimental design:

 

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This experimental design window is very similar for all the different project types. A few set of controls may change depending on the selected one.

You have to select the variables to be considered in your optimization (i.e. modifier composition and nature, temperature, buffer pH and concentration) additionally to the gradient time and modifier composition ranges.

Mchrom Scout will build up the experimental design needed using uniform shell Doehlert and/or hybrid designs to develop the retention models.

Additional test runs are used to validate the regression model. These are points placed in the experimental design which will not be entered in the regression model calculation. They will be used to test the predictive ability of the model instead.

Once you are happy with the selected variables and value ranges, an input tablet is created with the experimental plan.

 

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This window is divided into two halves; the first one is for your experimental plan to be followed in the lab and the second one is for your data entries (either manually or pasting the values directly from your clipboard).

Additionally, clicking on the Elution methods template button, you will be able to produce a more convenient sample set of experiments.

 

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Now it is your turn to run all the experiments in the lab! You can halt the optimization project and come back to paste your experimental results when you are ready.

Developing retention models

When pasting your data on the retention time table, Mchrom Scout will help you to detect any empty cell*, either for isocratic and/or gradient modes. An error message, as displayed below, would appear to alert you.

*Note: It is recommended to fill in any blanks with zeroes when using for instance an excel spreadsheet or a similar data input application as well as double checking cell positions.

Once you are happy with the retention results table the regression models are calculated.

When the model generation is completed (it takes just a few seconds) Mchrom Scout will produce the retention model graphs and verification data. This allows the user to assess the quality of the retention model generated. Graphs corresponding to the isocratic and gradient retention data will be available.

 

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A graph displaying “measured” vs “predicted” values is produced for the gradient retention data (as well as for isocratic data if a combined grd+isoc project type was selected). This provides a simple view of the goodness of fit for the different peaks. In this way you can observe if any of the peaks are not correctly modeled.

The system also provides a graph of the absolute errors (see below) or box-whisker plots, enabling you the identification of poorly modeled peaks. Provided you accepted the program suggestion to perform validation runs, it is especially important to verify the behavior of the model against these validation runs (represented by means of different marks in the graphs). Similar error levels for validation and calibration runs is an indication of the model reliability. Otherwise, bad predictions for validation runs should alert even if calibration runs perform apparently satisfactory.

 

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If the model is not considered appropriate, you should revise the configuration and check the reliability and accuracy of the experimental data. No model can be better than the experimental data used to build it.

Now, the system is ready to perform the automatic optimization or alternatively to perform some manual tweaks aiming a convenient separation. Once you are happy with the optimization just press the “save model” button.

 

Optimizing your separations

Selection of the detectors:

Mchrom Scout allows you two different pathways depending on if you have a mass spectrometer detector or a conventional one. (e.g. UV, FLU).

 

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For mass spectrometer detectors, Pareto optimality is adopted using the selectivity matrix and run time as optimization criteria. Mchrom Scout will allow you to load your mass spectrum data.

 

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For conventional detectors, two options are available:

Classic Chromatographic Response Functions (CRFs) and Pareto Optimality:

 

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Configure the search space for optimization:

In all cases, the controls to configure the processes are contained in a single multipage window having the following structure:

 

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For gradient elutions the parameters defining the optimization search space are organized in two pages comprising the common parameters (image above). These are the ones affecting any gradient profile and those particular for each type of gradient (image below).

 

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In this way, a fully flexible definition can be made. Additionally, parameters affecting the optimization engine can be modified. In general, default values perform well but experienced users may prefer to configure the genetic algorithm parameters. Although the automatic optimization tends to run for any possible type of elution mode, you can omit one or several modes (and thus speeding the optimization process if any of these modes has no interest to you), or even all modes if you re-enter an already revised and saved retention model to perform some manual simulations.

 

Viewing the optimization results

 

In Mchrom Scout, project optimization results are produced in an independent way for isocratic elutions and the different types of gradient elution available (linear, curved, multilinear and step-wise). These results can therefore be explored and compared easily by switching between tabs in the results page.

 

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Optimal solutions are presented as chromatograms with superimposed gradient shapes (see figure above) and as tables providing both numerical values of peak’s retention and the gradient program to allow the easy implementation for verification in the instrument (see figure below).

 

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Optimum results for isocratic elutions and each type of gradient are available simply by selecting the upper labeled tabs in the results’ page.

For projects using Pareto optimality, there is a non-unique optimal solution produced. However a set of Pareto optimal solutions that need to be inspected to finally select one (or several) among them. In this way, appropriate tools are provided to enable the inspection and selection of these solutions.

 

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Solutions are selected on the dropdown menu, or more intuitively, by clicking on the optimal Pareto fronts’ graphical representations. This can be shown in 2D or 3D modes depending on the number of variables and optimization objectives defined on launching the project. The above graphs show some optimal Pareto fronts for isocratic and curved gradients. You can click near any point inside the graph to obtain the chromatogram and tables corresponding to that particular solution.

 

For projects using chromatographic response functions, unique optimal solutions are produced. Results are presented using a fully similar formatting although there is no need of selection tools.

 

If the project has been defined using a mass spectrometer detector, some specific graphs and tools are additionally presented as shown previously. The most important one, is the selectivity plot which is a graphical representation of the selectivity matrix (Journal of Chromatography A, 1028, 2008, 116), used as one of the objective functions. This plot allows you to make decisions in a very easy and quick way since the ideal state would be a blank grid. Hence the less colored squares and colors belonging to scale low values the more convenient the optimal solution will be.

 

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Last modified: April 6, 2017 by Enrique Sánchez