## 2 Methodology
Once the phenotypic (observable) characteristics such as gram stain, absence or presence of capsule, shape and arrangement of bacteria, presence of flagella or pili, and the type of flagella or pili are examined, then they can be further analyzed to determine their type. Four attributes should be understood to successfully interpret a decision tree. These attributes are a decision tree's root node, internal nodes, leaf nodes, and branches. Figure 5 is a visual representation of the decision tree for _Acinetobacter baumannii_. The root node is the leftmost node (blue box) in the tree; it is what everything stems from and indicates the scientific name. Following this, the internal nodes are the phenotypic characteristics (light grey boxes) including gram stain, absence or presence of capsule, shape and arrangement of bacteria, presence of flagella or pili, and the type of flagella or pili present. Each of the yellow boxes are considered leaf nodes that are terminated based on the prior selection from the internal node. This is the reason why there are yellow boxes identified as \"stop\". The green box is the results box and can be considered a leaf node as  
Figure 5: Visual representation of the decision tree for _Acinetobacter baumannii_. Blue box: Root node; Grey boxes: internal nodes; Yellow boxes: leaf nodes; Green box: result. Branches connect the nodes and are the drop-down menu options. well. Branches are what connect all of the nodes and in this proof-of-concept and indicate the drop-down menu options. Another example is shown in Figure 6 where five different branches stemming from the internal shape node represent five shape options in the shape drop-down menu. Pathogen Shape Detection Enhancement using Edge Detection: Captured aerosol pathogens often do not appear sharp and clear by optical microscopy owing to movement within the imaging media. One method of improving the outcomes of automated morphological detection of unknown pathogens using a decision matrix is employing edge detection. Figure 7 shows representations of bacterial images that were processed with a Sobel convolution edge detection algorithm, MathCad, that detects edges by convolving various kernels over the matrix [21]. These kernels provide uniform edge detection with increased weight to the orthogonal pixels over the diagonal pixels. Without suitable edge enhancement, artifacts due to debris in the fluid around the target pathogen, lack of image contrast, and out of focus elements can all lead to a false boundary of the pathogen target, thus impairing the probability of identification of the target when referenced to library examples. \"Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Common edge detection algorithms include Sobel, Canny, Prewitt, Roberts, and fuzzy logic methods\" [22]. Figure 6: Typical decision matrix drop down menu with 5 branches for automated pathogen identification. Sorting Virus, Bacterial, Debris: The reader will no doubt ask that in an aerosol sampling system, the electrospray source will tend to charge both potential pathogens as well as non-organic particulate matter. How do we parse out the debris from bacteria and/or viruses? The answer may be found by using Charge Detection Mass Spectrometry or CDMS. Conventional mass spectrometry is less effective at the mass ranges one would see when looking at viruses or bacteria. \"As the analyte mass approaches the mega Dalton regime, the m/z spectrum is often comprised of a broad distribution of unresolved charge states. In such cases, mass determination is precluded. Charge Detection Mass Spectrometry (CDMS) is an emerging MS technique for determining the masses of heterogeneous, macromolecular complexes. In CDMS, the m/z and z of single ions are measured concurrently so that mass is easily calculated.