Agilent MassHunter: Unknowns Analysis Guide

Agilent MassHunter unknowns analysis. Welcome. In this short video I will show you how you can set up a method in Agilent MassHunter software to quickly screen your unknown sample by applying deconvolution to detect the individual components in your sample followed by comparing the obtained against mass spectral libraries. Within just a few minutes this will provide a list of components of your sample, the deconvoluted spectrum of each component, the match factor based on the MS library search and more. As part of the MassHunter software suite, there is a dedicated program available called Unknowns Analysis. It will be automatically installed on your PC during installation of the MassHunter quantitative analysis software. In case you don't find it on your desktop, please navigate to Agilent MassHunter quantitative analysis tools, open the application called Setup Desktop Icons, and enable MassHunter unknowns analysis. Before we access the software and set up our method, let's have a look at co-eluting compounds in our chromatogram. A situation that may be well known to all of you working on GC-MS data. A chromatographic peak may be composed of multiple compounds that coelute. In this example, the one peak shown in the total ion chromatogram, or TIC signal, is really composed of three compounds. Not only is it very difficult and time-consuming to figure this out manually, but we also have no real possibility to extract a clean spectrum from any of these compounds. The goal of deconvolution is to computationally separate co-eluting components and create a pure spectrum for each component. Econvolution will be the peak detection method that we will use in the following example for automated processing of our samples in MassHunter Unknowns Analysis. Let's start setting up a generic method in MassHunter Unknowns Analysis. First of all, we will open the program by double-clicking on the icon. Once the program is open, you can create a new analysis by clicking on the icon in the upper left corner. In the next step, we will navigate to the location of our raw data files, enter a name for our new analysis, and finish the process by clicking on Create. Now we can add samples to our sample table by just selecting them here. If you want to analyze different files from different locations within one batch, you can use the Browse to Copy function shown here. The selected data files will now appear in our sample table. Here we can, for example, change the type of data file, like in this case from sample to matrix blank. Now we will go through the setup of a new method and enter the parameters that should be applied for data evaluation. The first tab is about the peak detection method. As you can see from the drop-down menu, three options exist. But as said at the beginning, we will focus on deconvolution to identify the individual components in our samples. We also have the possibility here to exclude single M over Z values that may come from background noise. There's also the possibility to apply some filters in terms of peak area or peak height in order to exclude very small components. The next step will allow us to set the parameters used for the deconvolution process. The key parameter here is the retention time window size factor. This parameter controls the grouping of the extracted ion signals into components. The larger the value, the fewer components will be returned and vice versa. For really complex chromatograms with a lot of co-illusion and interferences, you would need to enter lower values in order to get them separated. You can see the default values that are typically used in GC-MS. Unknown's analysis can handle several retention window size factors in one analysis, so you can see which value returns the highest match scores. We will go ahead with 75 in our example. In the extraction window below, we can leave the default settings, because we are looking at unit mass resolution data in this case. Now we move over to the settings for the library search. Right at the top, we can specify the libraries we want to use for our method. MassHunter unknowns analysis can handle multiple libraries at the same time. So you can, for example, enter your own customized libraries at the top, but also include commercial ones, like the NIST library, to see if something else can be found in your sample that might not be covered by your own or specific library. Below, you have options to adjust the library search by weighting the search function toward forward or reverse search, for example. In the second part of this tab, you can define if you want to make use of the retention time criteria. This is incredibly useful in terms of reducing false positives. If you do not only consider the mass spectral match but also the chromatography by using retention indices. In order to make use of retention indices, we need to provide a retention calibration file based on, for example, an alkane standard. This can be a simple CSV file with the compound name, CAS number, retention index and actual retention time using our method. Above you can decide how you want to penalize the library match score in case the retention time or index is not the way we would expect for that compound. Let's move to the compound identification tab. The minimum match factor is a threshold which defines if a component will be showing up with the tentative identification of the library or just as an unknown, in case the mass spectral match was below the defined threshold. In the next field, we can enter the lower limit for the m over z values to be considered for a spectral match. This is important in case we have started acquisition only at higher masses to avoid penalties for low masses that may be in the library, but not in our spectrum. In case we are using multiple libraries, we can specify the search process here. The target match tab is not that relevant for our current example, because we have not done any target analysis of our samples prior entering the unknowns analysis software. But it would give us the possibility to do some semi-quantitation based on response factor. The last tab will allow us to perform a blank subtraction. This requires us to change the type of data file in the sample section to blank or matrix blank. We can then set certain criteria to define when a compound that is also found in a blank will be subtracted or excluded from the peak table of our sample. In the MassHunter quantitative analysis supplemental section, Many videos can be found that provide an in-depth explanation of the individual parameters for the method setup in MassHunter unknowns analysis. Once we have finished with the setup of our method, we can apply it to all our samples and get them analyzed. Once the analysis has finished, we can start reviewing our results. On the left side, you can find a sample table. From here you will also see how many components have been identified in each of your samples and how many of them could be identified based on the library search. The details for each of the samples can now be found in the components table below. You will find for example the retention Time, compound name, match factor and many more categories. If you right-click and go to Add Remove Column, you can display other options in the table. On the right side, we see the chromatogram of the selected sample. In black, it's the TIC signal. And in green, the individual components that have been identified in this chromatogram. Below is the information from the mass spectrum. At the top, you can see the deconvoluted spectra of the currently selected compound, and below, the corresponding spectra from the library. At the bottom, you will find the raw data at this point in the chromatogram. There are different possibilities to rearrange the layout and of course you can also save it and reload it at any time. Since we have also used the retention time calibration file in our method, let's have a look at one example where this could be really useful. For example, if we move one compound further into the table, we can see that it was identified as the delta isomer of hexachlorocyclohexane. If we look to the spectra or to the match factor, we can see that it's quite good. However, the retention index is far off from the one from the library. So we can go ahead and right click on the compound and go to show alternative hits. This will open another window and we can see an extended hit list. Based on retention time difference, you can see now that there is a second compound with a similar spectral match factor, but a much better match for the retention time or index. It's the alpha isomer of the compound. We can select this one and set it as the best hit. Once we are finished with the review of our data, we have different possibilities to export the data or to generate a report. For example, you can export the data to a CSV file, directly to a library, or you can go ahead and do a quantitative analysis method from these compounds. There are also possibilities to generate a report based on different available report templates. Thank you for watching this brief introduction into Agilent MassHunter Unknowns Analysis.

Within just a few minutes this will provide a list of components of your sample, the deconvoluted spectrum of each component, the match factor based on the MS library search and more. As part of the MassHunter software suite, there is a dedicated program available called Unknowns Analysis. It will be automatically installed on your PC during installation of the MassHunter quantitative analysis software.

In case you don't find it on your desktop, please navigate to Agilent MassHunter quantitative analysis tools, open the application called Setup Desktop Icons, and enable MassHunter unknowns analysis. Before we access the software and set up our method, let's have a look at co-eluting compounds in our chromatogram. A situation that may be well known to all of you working on GC-MS data.

A chromatographic peak may be composed of multiple compounds that coelute. In this example, the one peak shown in the total ion chromatogram, or TIC signal, is really composed of three compounds. Not only is it very difficult and time-consuming to figure this out manually, but we also have no real possibility to extract a clean spectrum from any of these compounds. The goal of deconvolution is to computationally separate co-eluting components and create a pure spectrum for each component. Econvolution will be the peak detection method that we will use in the following example for automated processing of our samples in MassHunter Unknowns Analysis.

Let's start setting up a generic method in MassHunter Unknowns Analysis. First of all, we will open the program by double-clicking on the icon. Once the program is open, you can create a new analysis by clicking on the icon in the upper left corner.

In the next step, we will navigate to the location of our raw data files, enter a name for our new analysis, and finish the process by clicking on Create. Now we can add samples to our sample table by just selecting them here. If you want to analyze different files from different locations within one batch, you can use the Browse to Copy function shown here. The selected data files will now appear in our sample table.

Here we can, for example, change the type of data file, like in this case from sample to matrix blank. Now we will go through the setup of a new method and enter the parameters that should be applied for data evaluation. The first tab is about the peak detection method.

As you can see from the drop-down menu, three options exist. But as said at the beginning, we will focus on deconvolution to identify the individual components in our samples. We also have the possibility here to exclude single M over Z values that may come from background noise. There's also the possibility to apply some filters in terms of peak area or peak height in order to exclude very small components. The next step will allow us to set the parameters used for the deconvolution process.

The key parameter here is the retention time window size factor. This parameter controls the grouping of the extracted ion signals into components. The larger the value, the fewer components will be returned and vice versa.

For really complex chromatograms with a lot of co-illusion and interferences, you would need to enter lower values in order to get them separated. You can see the default values that are typically used in GC-MS. Unknown's analysis can handle several retention window size factors in one analysis, so you can see which value returns the highest match scores.

We will go ahead with 75 in our example. In the extraction window below, we can leave the default settings, because we are looking at unit mass resolution data in this case. Now we move over to the settings for the library search. Right at the top, we can specify the libraries we want to use for our method. MassHunter unknowns analysis can handle multiple libraries at the same time.

So you can, for example, enter your own customized libraries at the top, but also include commercial ones, like the NIST library, to see if something else can be found in your sample that might not be covered by your own or specific library. Below, you have options to adjust the library search by weighting the search function toward forward or reverse search, for example. In the second part of this tab, you can define if you want to make use of the retention time criteria.

This is incredibly useful in terms of reducing false positives. If you do not only consider the mass spectral match but also the chromatography by using retention indices. In order to make use of retention indices, we need to provide a retention calibration file based on, for example, an alkane standard. This can be a simple CSV file with the compound name, CAS number, retention index and actual retention time using our method.

Above you can decide how you want to penalize the library match score in case the retention time or index is not the way we would expect for that compound. Let's move to the compound identification tab. The minimum match factor is a threshold which defines if a component will be showing up with the tentative identification of the library or just as an unknown, in case the mass spectral match was below the defined threshold. In the next field, we can enter the lower limit for the m over z values to be considered for a spectral match. This is important in case we have started acquisition only at higher masses to avoid penalties for low masses that may be in the library, but not in our spectrum.

In case we are using multiple libraries, we can specify the search process here. The target match tab is not that relevant for our current example, because we have not done any target analysis of our samples prior entering the unknowns analysis software. But it would give us the possibility to do some semi-quantitation based on response factor. The last tab will allow us to perform a blank subtraction.

This requires us to change the type of data file in the sample section to blank or matrix blank. We can then set certain criteria to define when a compound that is also found in a blank will be subtracted or excluded from the peak table of our sample. In the MassHunter quantitative analysis supplemental section, Many videos can be found that provide an in-depth explanation of the individual parameters for the method setup in MassHunter unknowns analysis.

Once we have finished with the setup of our method, we can apply it to all our samples and get them analyzed. Once the analysis has finished, we can start reviewing our results. On the left side, you can find a sample table. From here you will also see how many components have been identified in each of your samples and how many of them could be identified based on the library search.

The details for each of the samples can now be found in the components table below. You will find for example the retention Time, compound name, match factor and many more categories. If you right-click and go to Add Remove Column, you can display other options in the table. On the right side, we see the chromatogram of the selected sample.

In black, it's the TIC signal. And in green, the individual components that have been identified in this chromatogram. Below is the information from the mass spectrum. At the top, you can see the deconvoluted spectra of the currently selected compound, and below, the corresponding spectra from the library. At the bottom, you will find the raw data at this point in the chromatogram.

There are different possibilities to rearrange the layout and of course you can also save it and reload it at any time. Since we have also used the retention time calibration file in our method, let's have a look at one example where this could be really useful. For example, if we move one compound further into the table, we can see that it was identified as the delta isomer of hexachlorocyclohexane.

If we look to the spectra or to the match factor, we can see that it's quite good. However, the retention index is far off from the one from the library. So we can go ahead and right click on the compound and go to show alternative hits.

This will open another window and we can see an extended hit list. Based on retention time difference, you can see now that there is a second compound with a similar spectral match factor, but a much better match for the retention time or index. It's the alpha isomer of the compound. We can select this one and set it as the best hit. Once we are finished with the review of our data, we have different possibilities to export the data or to generate a report.

For example, you can export the data to a CSV file, directly to a library, or you can go ahead and do a quantitative analysis method from these compounds. There are also possibilities to generate a report based on different available report templates. Thank you for watching this brief introduction into Agilent MassHunter Unknowns Analysis.

Transcript for:Agilent MassHunter: Unknowns Analysis Guide

Transcript for:
Agilent MassHunter: Unknowns Analysis Guide