ORCID Profile
0000-0002-5748-147X
Current Organisation
Università degli Studi di Milano-Bicocca
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Publisher: Royal Society of Chemistry (RSC)
Date: 2016
DOI: 10.1039/C6MB00162A
Abstract: Oblique rotation of factors would be advantageous for improvement of classification models in challenging biochemical and omics systems.
Publisher: American Vacuum Society
Date: 20-09-2023
DOI: 10.1116/6.0002788
Publisher: American Vacuum Society
Date: 11-2020
DOI: 10.1116/6.0000614
Abstract: The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox™ multilayer standard s le was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled s le, in which the mass spectral similarity of in idual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and s le degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.
Publisher: Wiley
Date: 15-02-2023
Abstract: Biofilm formation is a major cause of hospital‐acquired infections. Research into biofilm‐resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high‐throughput approach to enable the efficient discovery of novel biofilm‐resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a erse set of polymeric surfaces. The relationships between time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self‐organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment.
Publisher: American Chemical Society (ACS)
Date: 13-12-2022
Publisher: American Chemical Society (ACS)
Date: 04-2020
Publisher: American Chemical Society (ACS)
Date: 02-07-2020
No related grants have been discovered for Davide Ballabio.