@article{knizner_eisenberg_muddiman_2024, title={Prototyping an ionization source for non-engineers}, volume={59}, ISSN={["1096-9888"]}, DOI={10.1002/jms.4995}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF MASS SPECTROMETRY}, author={Knizner, Kevan T. and Eisenberg, Seth M. and Muddiman, David C.}, year={2024}, month={Jan} }
 @article{eisenberg_knizner_muddiman_2023, title={Development of an object-based image analysis tool for mass spectrometry imaging ion classification}, volume={5}, ISSN={["1618-2650"]}, DOI={10.1007/s00216-023-04764-x}, abstractNote={Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.}, journal={ANALYTICAL AND BIOANALYTICAL CHEMISTRY}, author={Eisenberg, Seth M. and Knizner, Kevan T. and Muddiman, David C.}, year={2023}, month={May} }
 @article{eisenberg_knizner_muddiman_2023, title={Metabolite Annotation Confidence Score (MACS): A Novel MSI Identification Scoring Tool}, volume={8}, ISSN={["1879-1123"]}, DOI={10.1021/jasms.3c00178}, abstractNote={Mass spectrometry imaging (MSI) is an analytical technique capable of measuring and visualizing the spatial distribution of thousands of ions across a sample. Measured ions can be putatively identified and annotated by comparing their mass-to-charge ratio (m/z) to a database of known compounds. For high-resolution, accurate mass (HRAM) imaging data sets, this is commonly performed by the annotation platform METASPACE. Annotations are reported with a metabolite-signal-match (MSM) score as a measure of the annotation's confidence level. However, the MSM scores reported by METASPACE often do not reflect a reasonable confidence level of an annotation and are not assigned consistently. The metabolite annotation confidence score (MACS) is an alternative scoring system based on fundamental mass spectrometry imaging metrics (mass measurement accuracy, spectral accuracy, and spatial distribution) to generate values that reflect the confidence of a specific annotation in HRAM-MSI data sets. Herein, the MACS system is characterized and compared to MSM scores from ions annotated by METASPACE.}, journal={JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY}, author={Eisenberg, Seth M. and Knizner, Kevan T. and Muddiman, David C.}, year={2023}, month={Aug} }