by Sarah Srokosz, Marketing Communications Specialist, ACD/Labs
Interpreting the pattern of lines on a mass spectrum may feel overwhelming at first glance. However, with a little background knowledge of the theory behind mass spectrometry techniques, you can begin to recognize what to look for in your mass spectrum and obtain the information you need. And while this desired information you are hoping to obtain from your MS data may differ from the next person, the underlying principles are the same.
How Ionization Source Impacts Interpretation of Mass Spectra
One piece of information that is crucial to interpreting any mass spectrum is the ionization source used to record it. You’ll see why this is important later, but first you should know that mass spectrometry ionization sources can be divided into 2 categories: hard ionization sources and soft ionization sources.
MS Data from Hard Ionization Sources
Hard ionization sources impart a lot of excess energy on the sample molecules during the ionization process, resulting in fragmentation. With mass spectra from hard ionization sources, the molecular ion (M+) peak is relatively small and may not even be observed. The most commonly encountered hard ionization source is electron impact (EI).
MS Data from Soft Ionization Sources
Soft ionization sources ionize the sample with significantly less excess energy, resulting in little fragmentation and a larger M+ peak. Soft ionization sources include chemical ionization (CI), electrospray ionization (EI), atmospheric pressure chemical ionization (APCI), and matrix assisted laser desorption and ionization (MALDI).
Fragmentation Patterns Give Structural Information about the Sample
The fragmentation of a molecule in a mass spectrometer depends on the stability of the molecular ion relative to that of the fragment. Because of this, the fragmentation pattern of a molecule is reproducible, and contains valuable information about its chemical structure.
Some common fragments of hydrocarbons and their corresponding nominal and exact masses are tabulated below.
|Fragment Ion||Nominal Mass||Corresponding Functional Group||Note|
|[OCOH]+||45||Carboxylic acid or ester|
|[C7H7]+ (Tropylium ion)||91||Aromatic|
|Carboxylic acid or ester||Aromatic|
|[CnH2n-1O2]+||14n+31||Carboxylic acid or ester|
|[M-OH]+||M-17||Carboxylic acid or ester|
|Carboxylic acid or ester||Aromatic|
|[M-CO2H]+||M-45||Carboxylic acid or ester|
Adduct Ions Help Identify the Molecular Ion for Soft Ionization Sources
For mass spectra obtained with a soft ionization source (i.e., without fragment ions to provide information about the sample) a good way to start interpreting your data is to look for common adduct ions.
According to the Definitions of terms relating to mass spectrometry (IUPAC Recommendations 2013),
“Adduct ions are formed by the interaction of a precursor ion with one or more atoms or molecules to form an ion containing all the constituent atoms for the precursor ion as well as the additional atoms from the associated atoms or molecules.”
These precursor ions may be intentionally or unintentionally part of your sample solution. In the case of ESI, which relies largely on adduct formation as an ionization method, it is not uncommon to include additives in the sample solution to enhance adduct formation when using this ionization source.
Some amount of adduct formation is unavoidable. But we don’t really want to avoid it entirely, as we can combine it with some general chemical knowledge to help us interpret MS data. However, adduct formation can also be difficult to control. For example, older glassware can be a source of unwanted sodium ions.
Adduct formation depends on many factors including the presence and location of lone pairs on the analyte molecule. Other compounds present in the sample mixture, known as the matrix, can also form adducts with the precursor ions. The resulting adduct ions that do not contain the analyte fall into the category of background ions. Like adduct ions in general, background ions are unavoidable. However, you want to avoid the case where adducts form preferentially with matrix components over the analyte. This undesirable effect is known as matrix suppression.
In a well-run spectrum, you expect to see some, but not all, possible adducts. Knowing how to spot these adducts based on their relative mass differences can help you make sense of your data. To help you out, we’ve assembled some of the most common adducts and their corresponding masses in the tables below.
Common Adducts in Positive Ion Mode
|Adduct Ion||Nominal Adduct Mass||Exact Adduct Mass|
Common Adducts in Negative Ion Mode
|Adduct Ion||Nominal Adduct Mass||Exact Adduct Mass||Notes|
|[M+Cl]–||M+35||M+34.969402||Note M+37 isotope peak with ~1/4 the intensity|
|[M+Br] –||M+79||M+78.918885||Note M+81 isotope peak with approximately equal intensity|
Software Can Help Predict and Identify Fragment and Adduct Ions in MS Data
The full scope of adduct formation and fragmentation in mass spectral data is beyond what can be covered in a single blog post. Luckily today, modern software tools can be used to combat some of this complexity and speed up MS data analysis. All of ACD/Labs’ analytical data processing tools have functionality to help you process and interpret MS data from fragment and adduct ions.
Tools for Basic MS Data Processing
Spectrus Processor and ChemAnalytical Workbook help you keep track of fragments with labels and allow you to label adduct ions by default when interpreting components.
Fragment Prediction and Component Characterization
For more advanced functionality, MS Fragmenter can predict mass spectral fragmentation to help you identify components and provide insights into fragmentation mechanisms. This functionality is also incorporated into our most advanced MS data processing tools (MS Workbook Suite and MS Structure ID Suite) where it can be used to automatically assign peaks in your spectra.
You can learn more about ACD/Labs mass spectrometry software tools here.
Definition of terms relating to mass spectrometry (IUPAC Recommendations 2013), Murray, K. K.; Boyd, R. K.; Eberlin, M. N.; Langley, G. J.; Li, L.; Maito, Y. Pure Appl. Chem., 2013, Vol. 85, No. 7, 1515-1609.