Microplastics have emerged as one of the most widely discussed environmental contaminants of our time. Regardless of where we live, daily exposure to microplastics has become virtually unavoidable—these tiny plastic particles are present in the air we breathe, the water we drink, and the environments we inhabit. As researchers work to better understand the sources, transport pathways, and impacts of microplastics, the demand for reliable microplastic analysis methods continues to grow.While techniques such as FTIR and Raman spectroscopy have become standard tools for identifying microplastics, their utility is limited for smaller particles (i.e. nanoplastics) and in complex matrices. More advanced microplastic analysis methods can provide a deeper understanding of what these particles contain and how they may change over time in the environment.<div><h3>The Limits of Spectroscopic Microplastic Analysis Methods</h3>Fourier-Transform Infrared (FTIR) and Raman spectroscopy are widely used microplastic analysis methods because they can identify polymer types by measuring how electromagnetic radiation interacts with a material. These techniques are valuable for determining whether a particle is polyethylene, polypropylene, polystyrene, or another common plastic. For many studies, this information is sufficient to confirm the presence of microplastics, especially in clean samples with sufficient microplastic density.However, <a target="_blank" rel="noopener noreferrer" href="https://www.leco.com/application/environmental/">environmental samples</a> are rarely clean and compositionally simple. Microplastic particles can become weathered and obscured by other materials in the environment, making their accurate identification with spectroscopy difficult. Additionally, if researchers want to understand what additives, degradation products, or pollutants are associated with the plastic, particle identification alone is not enough. Thus, FTIR and Raman spectroscopy methods alone struggle to thoroughly characterize these complex chemical components, particularly in challenging environmental matrices. As questions surrounding microplastic exposure become increasingly important, researchers require analytical techniques capable of examining the chemistry of a sample in greater detail, not simply confirming the presence of the physical particle itself.</div><div><h3>Thermal Desorption and Pyrolysis GCxGC-TOFMS For Deeper Microplastic Analysis</h3>Thermal Desorption (TD) and Pyrolysis (Py) coupled with comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry (<a target="_blank" rel="noopener noreferrer" href="https://www.leco.com/core-analysis/gc-and-gc-ms/">GCxGC-TOFMS</a>) offer a powerful alternative for investigating nano- and microplastics in complex samples. These techniques provide complementary information about sample composition.&nbsp;Thermal desorption releases volatile and adsorbed compounds, including environmental contaminants, degradation products, and VOCs associated with microplastic particles. This can reveal important information about chemicals present within or on the plastic.Pyrolysis thermally deconstructs the polymeric material, breaking it into GC-amenable characteristic degradation compounds. These compounds act as chemical markers that can be used to identify the various polymers present. When analyzing complex or heavily weathered microplastics that may be difficult to characterize using traditional spectroscopic techniques, pyrolysis is particularly advantageous.The most significant benefit comes from pairing these techniques with comprehensive two-dimensional gas chromatography. Environmental samples often contain hundreds or even thousands of compounds that coelute in 1D GC-MS, making it difficult or impossible to identify each compound. GCxGC provides significantly greater separation capability, helping analysts distinguish important polymer markers from background matrix components. This enhanced separation improves the mass spectrometer’s ability to identify characteristic polymer pyrolysis products, even in highly complex environmental samples. As a result, researchers can detect and identify challenging microplastics without the extensive purification steps often required by other analytical workflows.Another unique advantage of this approach is that thermal desorption and pyrolysis can be used in series on the same sample. Together, they create a more complete chemical profile of the sample.</div><div><h3>A Powerful Microplastics Analysis Workflow for Deeper Insight into the Effects on Our Environment</h3>FTIR and Raman spectroscopy remain valuable tools for rapid screening and flagging samples containing microplastic particles. However, by combining thermal desorption, pyrolysis, and GCxGC-TOFMS, analysts can obtain a much more comprehensive view of plastics in their samples and their environmental implications. As environmental research continues to advance and concerns surrounding microplastic contamination grow, researchers will increasingly require information that extends beyond answering the question,&nbsp;“What plastic is present?”&nbsp;&nbsp;Ultimately, this deeper level of characterization can provide new insights into the environmental fate of microplastics and help scientists better understand their potential impacts on ecosystems and human exposure.</div>

Surpassing the Limits of Traditional Microplastic Analysis Methods with Thermal Desorption and Pyrolysis GCxGC-TOFMS

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NIST26 introduces integrated chromatogram processing, deconvolution, and library searching for EI GC-MS and LC-MS/MS data, but the workflow begins with one essential requirement: chromatographic data must be available in the mzML format. In his presentation <a target="_blank" rel="noopener noreferrer" href="https://labrulez.com/pdf/Conversion_to_mz_ML_63e134bb34.pdf">Conversion to mzML Format for NIST26 Chromatogram Processing</a>, James Little of Mass Spec Interpretation Services explains how vendor data can be converted to mzML using either instrument-vendor software or the freely available MSConvert tool from ProteoWizard, and highlights several settings that are critical for successful processing in NIST26.<h4>NIST26 Chromatogram processing requires mzML</h4>For the chromatogram-processing workflow described in the presentation, mzML is the accepted input format. Proprietary LC-MS or GC-MS data therefore need to be converted before they can be loaded into the NIST26 chromatogram-processing environment.There are two principal ways to obtain an mzML file:<ul><li>conversion using software supplied by the instrument manufacturer,</li><li>conversion using MSConvert, which is included in the free ProteoWizard software package.</li></ul>Using an open format such as mzML also provides a practical way to move mass spectrometry data between different software environments without relying exclusively on proprietary vendor formats.<h4>Using ProteoWizard and MSConvert</h4>MSConvert is installed as part of the complete ProteoWizard package rather than as a separate application. After installing ProteoWizard, users can access either the graphical MSConvert interface or the command-line version.The presentation recommends the stable release for routine work, while noting that newer or nightly builds may sometimes provide improved support for recently introduced vendor formats or additional control over data-processing filters. This can be particularly relevant when handling vendor files in which centroiding behavior or peak picking requires closer attention.For most users, the graphical interface provides a straightforward route from proprietary raw data to an mzML file suitable for further NIST26 processing.<h4>Start by selecting mzML as the output format</h4>The first step in MSConvert is to select the source file or dataset using the Browse function.The software supports a broad range of mass spectrometry data formats, and the file-selection window can either be restricted to a particular vendor format or set to display any supported spectral format.After selecting the source data, the output format must be set to mzML. The screenshots in the presentation show this setting directly in the MSConvert options panel before the conversion is started.The user can also select the destination folder and other file-output options before adding the selected dataset to the conversion queue.<h4>Add the data and start the conversion</h4>Once the source file and output settings have been selected, the file is added to the conversion list using the Add command.The conversion itself is then launched with Start.For simple datasets that are already stored in the correct spectral representation, this may be all that is required. However, James Little emphasizes an additional requirement that is particularly important for NIST26: the data must be centroided.<h4>Centroided data are required</h4>According to the presentation, profile-mode data are not acceptable for this NIST26 chromatogram-processing workflow. The spectra must instead be in centroided, or peak-picked, form.Profile spectra contain the full measured shape of each mass spectral signal across the m/z axis. Centroided data reduce these profiles to discrete peaks represented by their characteristic m/z and intensity values.If the original vendor data are still in profile mode, centroiding can be performed during conversion by adding the Peak Picking filter in MSConvert.This means that the conversion step can perform two operations at once:vendor raw data → centroiding / peak picking → mzMLThe resulting mzML file can then be used as input for NIST26 chromatogram processing.<h4>Peak Picking must be the first filter</h4>One of the most important practical details in the presentation is the position of the Peak Picking operation in the MSConvert filter sequence.If centroiding is required, Peak Picking must be the first algorithm or filter in the list. Placing another processing function before it can interfere with the intended conversion workflow.MSConvert allows the user to select the Peak Picking filter, define the relevant MS levels, and add it to the processing sequence before starting conversion. The example shown in the presentation applies vendor-based peak picking to selected MS levels.If there is uncertainty about whether the source data are already centroided, the Peak Picking filter can still be included. According to the presentation, applying it to data that are already centroided does not alter the file unnecessarily.This provides a useful safeguard when the acquisition mode of an older dataset is uncertain.<h4>Selecting the appropriate MS levels</h4>Peak picking does not necessarily have to be applied identically to every spectrum in a dataset. MSConvert allows users to specify the MS levels to which the filter should be applied.This is especially relevant for LC-MS/MS data, where a file can contain both precursor-level MS spectra and tandem MS spectra.The conversion settings should therefore reflect the way the original data were acquired and how those data will subsequently be processed in NIST26.<h4>Be aware of changes in file size</h4>The presentation also notes that converted files may become substantially different in size from the original vendor files.This is worth considering when working with large LC-MS/MS datasets or processing batches of chromatograms. The size of the resulting mzML file can depend on the original acquisition mode, centroiding, compression settings, the number of MS levels, and other output parameters.Storage requirements should therefore be considered before converting large collections of raw data.<h4>A simple workflow for preparing data for NIST26</h4>The overall procedure presented by James Little can be summarized as a short sequence:<ol><li>Obtain the original vendor LC-MS or GC-MS data.</li><li>Open the data in MSConvert or use the vendor's own conversion software.</li><li>Select mzML as the output format.</li><li>Check whether the spectra are centroided.</li><li>If necessary, add Peak Picking as the first processing filter.</li><li>Select the appropriate MS levels.</li><li>Add the file to the conversion list.</li><li>Start the conversion.</li><li>Use the resulting mzML file for NIST26 chromatogram processing.</li></ol>The procedure itself is straightforward, but the centroiding requirement is important. A file can have the correct <code>.mzML</code> extension and still be unsuitable for the workflow if its spectra remain in profile mode.<h4>From vendor data to integrated NIST26 processing</h4>File conversion may appear to be a relatively minor step compared with deconvolution, spectral interpretation, and library searching, but it establishes the foundation for the entire downstream workflow.By using mzML as the common input format, NIST26 can process data originating from different instrument platforms through a more consistent workflow. ProteoWizard and MSConvert provide a practical bridge between proprietary mass spectrometry files and this standardized format.The key message from the presentation is therefore simple: convert to mzML, ensure the spectra are centroided, and place Peak Picking first whenever centroiding is required. Once those conditions are met, the data are ready for the integrated chromatogram-processing and library-searching capabilities available in NIST26.

Conversion to mzML Format for NIST26 Chromatogram Processing

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