GC-MS is a powerful analytical technique that can identify and quantify the components of complex mixtures. However, to achieve reliable and accurate results, you need to select and use appropriate reference standards and libraries. In this article, you will learn how to do that in six steps.
Reference standards are pure substances that have known properties and can be used to calibrate and validate your GC-MS method. There are different types of reference standards, such as internal, external, surrogate, and matrix-matched. Depending on your analytical goal, sample matrix, and detection limit, you need to choose the most suitable type for your GC-MS analysis. For example, if you want to correct for matrix effects and recoveries, you might use a surrogate standard that is similar to your analyte but not present in your sample.
Once you have chosen the type of reference standard, you need to obtain or prepare it. You can buy certified reference standards from reputable suppliers, or you can synthesize or isolate them yourself. However, you need to ensure that your reference standard is pure, stable, and traceable. You also need to store it properly and check its quality regularly. For example, you might use a GC-MS method to confirm the identity and purity of your reference standard, or you might use a mass spectrometer to measure its exact mass.
A reference library is a collection of spectra and information of known compounds that can be used to compare and identify your GC-MS peaks. There are different types of reference libraries, such as commercial, public, or custom. Depending on your analytical scope, availability, and budget, you need to select the most relevant and comprehensive library for your GC-MS analysis. For example, if you are analyzing environmental samples, you might use a library that contains spectra of pollutants, pesticides, and metabolites.
GC-MS reference libraries are a powerful tool to assist with the identification of unknown compounds that may turn up in your day-to-day analyses. Having access to reference libraries that are comprehensive for your compound classes of interest is important in obtaining good quality results from these libraries. The largest and most trusted commercially available GC-MS libraries for general use are: - Wiley Registry of Mass Spectral Data - NIST Mass Spectral Library - KnowItAll Mass Spectral Database Collection There are also smaller application-specific libraries for a variety of industries. Some examples include: - Forensics - Toxicology - Omics - Food and Beverage - Flavors and Fragrances - Oil and Gas - Chemical Manufacturing
Before you use your reference library, you need to validate it. You need to ensure that your reference library is compatible with your GC-MS instrument and software, and that it covers the range of compounds and conditions that you are interested in. You also need to check the quality and accuracy of the spectra and information in your reference library. For example, you might use a reference standard to test the match quality and similarity index of your reference library, or you might use a peer-reviewed source to verify the chemical name and structure of your library entries.
Now that you have your reference standard and library ready, you can use them to perform your GC-MS analysis. You need to follow the proper procedures and protocols to prepare, inject, separate, detect, and quantify your samples and standards. You also need to use the appropriate settings and parameters to search, compare, and identify your GC-MS peaks using your reference library. For example, you might use a retention index or a retention time window to narrow down your search results, or you might use a mass accuracy or a mass tolerance to filter out false positives.
Finally, you need to interpret and report your results. You need to understand the limitations and uncertainties of your GC-MS method, reference standard, and reference library. You also need to acknowledge the sources and references that you used to support your identification and quantification. For example, you might use a confidence level or a probability score to express the degree of certainty of your identification, or you might use a standard deviation or a relative standard error to indicate the precision of your quantification.