Installing and starting Chrombox QWindows computersMac computers (OS X)Linux computersStarting Chrombox Q from the Matlab desktop (on all systems)Changing settingsUpdatingUsing Chrombox Q with NIST MS-Search 2.0.Tutorial 1. Basic functions1.1. Startup and select method1.2. Loading a file:1.3. Baseline subtraction1.4. Peak region detection1.5 Quantification1.6. Identification and reportingChrombox Q – Tutorial 22.1. Startup and select method2.2. Peak region detection:2.3. Quantification2.4. Identification and reportingTutorial 3. Baseline subtraction and calibration3.1. Importing data3.2. Baseline removal3.3. Peak region detection:3.4. Quantification3.5. Building the calibration.3.6. Loading the PAH-mixture3.7. Background removal3.8. Peak region detection3.9. Quantification3.10. Identification

The following text styling is applied in this document. Commands, paths or filenames are denoted by: command, or path\filename.ext. Buttons in the graphical user interface are shown as [Button]. Keys on the keyboard are denoted by [Key]. A parameter to be set is denoted by parameter, and a value of a parameter or an option in a menu is denoted by option.

Installing and starting Chrombox Q

Windows computers

If installed on a network disk you may have to use one of the methods described below:

An example of qstart.m is shown below:

You can also create a desktop shortcut by copying the shortcut to Matlab and adding the following to the destination /automation /r qstart An example of how it can look is shown below:

C:\MATLAB6p5\bin\win32\matlab.exe /automation /r qstart

Mac computers (OS X)

As an alternative to the above procedure, Chrombox Q can be started by the following method:

An example of qstart.m is shown below:

Linux computers

As an alternative to the above procedure you can also start Chrombox Q by qstart.m as described for Mac computers above.

Starting Chrombox Q from the Matlab desktop (on all systems)

On all operating systems you can use the following procedure to start Chrombox Q.

In a minimized Matlab session (running in terminal without Matlab desktop) you can use the cd command to set the working directory and run qq_startscript to start the program.

Changing settings


The part to edit in qq_localsettings.sdv is between the two semicolons in the line shown below.

Alternatively, you may select the new code by the following procedure:

Using Chrombox Q with NIST MS-Search 2.0.

On Mac (OS X) and LInux, first install Wine to be able to run NIST MS-Search and verify that you can run windows exe-files. Thereafter proceed as described for Windows below. On Mac, the installer for WineBottler is a convenient way to install Wine. On Mac you currently need the beta version (Q-16-05b) of Chrombox to use the NIST MS-Search.

Tutorial 1. Basic functions

The main purpose of this tutorial is to get used to the most basic functions in the program. Fatty acid methyl esters (FAME) in a reference mixture will be identified using an existing libraries and an already calibrated method.

1.1. Startup and select method

Figure 1.1. Main window at startup

1.2. Loading a file:

Figure 1.2. Load at startup

1.3. Baseline subtraction

After baseline subtraction the chromatogram should look like in Figure 1.3.

Figure 1.3. Chromatogram after baseline subtraction

1.4. Peak region detection

Figure 1.4. The peak detection window

1.5 Quantification

Figure 1.5. The Quantify window

1.6. Identification and reporting

The results window is shown in Figure 1.6. .The first thing you have to do is to calculate retention indices for the peaks. The retention indices for fatty acid methyl esters are equivalent chain lengths (ECL).

Figure 1.6. The Results window

The compound list shows the 40 best matches from the library. The correlation plot shows similarity between the spectrum from the chromatogram and the library spectra. Ideally, all masses should be near the diagonal blue line.

The match plot shows calculated scores for the compounds in the compound list in decreasing order. Compounds with the same identity as the spectrum selected in the list (the one with highest score by default) are shown in red. Other compounds are shown in blue. For a reliable identification the situation should be similar to the one shown in the figure, where all the best matches have the same identity, there is a clear difference to the next compound and the best matches are above the threshold value marked by the green field.

The info box show additional info, such as the difference in retention indices and the source of the matching compound. In the list of search parameters you can set how the library search should be performed, for instance if and how retention indices should be used. By selecting Exclude instead of Gaussian funct. you can see the effect of omitting the retention indices and match the compounds using only spectra.

If you are confident about the content of your sample you can also use the [Search all] option that will identify all peaks with match above the threshold, without opening the Identify window.

Figure 1.7. The Identify window

You can save your results by [Save results] in the main window and recall the results by typing * or any relevant search string, such as Tut* in the search string field, and thereafter import the selected file by the [Load] button.

Chrombox Q – Tutorial 2

The purpose of this tutorial is to get used to peak detection and quantification in real samples, which usually have a more challenging peak pattern than reference mixtures. The sample is fatty acid methyl esters (FAME) from a fish oil. You will use the same method as in Tutorial 1.

2.1. Startup and select method

After baseline subtraction the chromatogram should look like in Figure 2.1

Figure 2.1. Chromatogram after baseline subtraction

2.2. Peak region detection:

The challenge with this sample compared to the reference mixture is that there is large variation in the peak size. Some peaks are also partially overlapping. In the peak detection window the best way to detect the peaks in usually to start with a high threshold value and then gradually reduce the threshold.

Figure 2.2. Peak region detection with a threshold of 100 000
Figure 2.3. The three first peaks in unlocked mode

There are several alternative methods for adding and adjusting peak regions. If you left-click in the chromatogram above or below a peak while the shift button is pressed on the keyboard a peak region is added around the selected peak. Left-click in front or behind a single or a group of peaks while the [ctrl] button is pressed on the keyboard adds a region with user defined width. Right-click on the area that marks a peak region provides a menu with several more options.

When the end of the chromatogram is reached there should be approximately 50 peaks. There are many minor peaks that are not detected at this level, but the threshold level is sufficient for this exercise.

2.3. Quantification

Contrary to the reference mixture in Tutorial 1, where all chromatographic peaks were resolved, you may now have overlapping or partially resolved peaks. You must therefore evaluate in each case whether the peak is pure or not. In cases with overlapping peaks you have four possibilities

At approximately 21 minutes there is a peak with a deviating peak shape with a shoulder to the left, but the two overlapping peaks have nearly identical spectra and there is no valley between them (Figure 2.4). The only option is therefore to quantify them as one peak or to jump to the peak window and split the peak manually.

Figure 2.4. Overlapping peaks with highly similar spectra

The next peak cluster is found around 24.7 min. In this case there is a clear shoulder to the right, but there are also impurities in the main peak.

Figure 2.5. Initial estimates of peak cluster at around 24.7 min
Figure 2.6. The three spectra from the cluster at 24.7 min after resolution and refinement

At around 27.7 there is a new peak cluster that clearly consists of two peaks that are partially resolved (Figure 2.7). When peaks are only partially overlapping like this the best strategy is usually to use the estimates of the spectra for resolution.

Figure 2.7. The peak cluster at around 27.6 min

At around 36.3 there is a new cluster of two severely overlapping peaks (Figure 2.8). In this case there are selective ions but the peaks are too overlapping to give good estimates of the spectra.

Figure 2.8. The peak cluster at around 36.3 min

At approximately 48.4 min there is a new cluster with one large and one small peak. There is a quite good resolution between the peaks and this cluster is best resolved using the spectra.

The remaining peaks are rather pure and can be quantified as single peaks.

2.4. Identification and reporting

Table 2.1. Reported results: retention times, retention indices, code, short name, name, area and area percent

Tutorial 3. Baseline subtraction and calibration

The purpose of this tutorial is to learn advanced baseline subtraction using CODA, building a retention index calibration from scratch, and use the calibration to identify compounds in a mixture of PAH.

3.1. Importing data

The chromatograms show a dilute sample of every n-alkane from C9 to C40. The last compounds are completely hidden in the column bleed.

3.2. Baseline removal

The window should look like in Figure 3.1. Important functions are CODA and the baseline finder. CODA [Windig et al. Anal. Chem. 68 (1996) 3602] is a filter for removal of ions that are basically found in the background. The baseline finder is a background subtraction function.

Figure 3.1. The baseline subtraction window
Figure 3.2. Chromatogram after filtering with CODA using window size 5 and a threshold of 0.94

3.3. Peak region detection:

It may be necessary to adjust the borders of the last peaks that is poorly separated from the noise.

Figure 3.3. Detected alkanes after adjustment of peak widths

3.4. Quantification

3.5. Building the calibration.

Figure 3.4. Final calibration

3.6. Loading the PAH-mixture

3.7. Background removal

3.8. Peak region detection

3.9. Quantification

Figure 3.5. The three-peak after resolve and after splitting the double-peak

3.10. Identification

Table 3.1. List of expected compounds