Emmanuel Cruz brilliantly defended his PhD thesis ‘Geochemical Study of Pyrite Persistence in Sedimentary Records’ at the Università degli Studi di Milano-Bicocca. Emmanuel undertook an impressive interdisciplinary effort, bridging two fields that often speak different scientific “languages.” He facilitated their interaction by developing new analytical methods and applying techniques such as RAMAN imaging, Design of Experiments (DoE), and chemometrics to investigate chemical factors linked to climate change. To top it all off, Emmanuel earned his PhD in Chemical, Geological, and Environmental Sciences with honors! The PhD thesis is available for downlaod here: https://michem.unimib.it/download/phd-thesis/
Category Archives: Scientific publications
Condensed phase membrane introduction mass spectrometry
Condensed phase membrane introduction mass spectrometry: A new frontier for the real-time monitoring of hazardous chemical migration from food contact materials:check the manuscript (open access) at the following link: https://doi.org/10.1016/j.greeac.2024.100199
Multivariate comparison of cluster validity indices
Cluster validity indices (CVIs) are used to detect a reliable number of clusters. We revised and evaluated 68 validity indices for crisp clustering by comparison on 21 real and simulated datasets. Have a look to the paper: https://doi.org/10.1016/j.chemolab.2024.105117
Molecular fingerprints for exploring the chemical space of natural products
We evaluated the effectiveness of multiple types of fingerprints for representing the chemical space of natural substances. The code and data to reproduce the results are also available in the study: Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., Sieber, S.A. (2024) Effectiveness of molecular fingerprints for exploring the chemical space of natural products, Journal of Cheminformatics 16, 35 (2024), https://doi.org/10.1186/s13321-024-00830-3
A new metric to assess the degree of accuracy of consensus predictions
We proposed a new heuristic metric to assess the degree of accuracy of consensus predictions. It can assist the mapping of reliability in prediction and enhance the delineation of a safe zone, where consensus predictions are expected to have better accuracy. All details are available in the following publication, have a look! We also provide data and code to calculate it, here!
V. Consonni, R. Todeschini, M. Orlandi, D. Ballabio (2024) Kernel-based mapping of reliability in predictions for consensus modelling, Chemometrics and Intelligent Laboratory Systems 246, 105085, https://doi.org/10.1016/j.chemolab.2024.105085
Which is the effect of different molecular fingerprints for exploring the chemical space?
Which is the effect of different types of molecular fingerprints for exploring the chemical space of natural products? Have a look here: https://doi.org/10.26434/chemrxiv-2023-0m355
Chemometrics to predict the taste of molecules
We have published a comprehensive review on classification-based chemoemtric approaches to predict taste of molecules, have a look!
Rojas, C., Ballabio, D., Consonni, V., Suárez-Estrella, D., Todeschini, R. (2023) Classification-based machine learning approaches to predict the taste of molecules: a review. Food Research International, 171, 113036 [link]
Characterization of pyrite weathering with Raman hyperspectral imaging and chemometrics
Here our latest poublication, which was the first product of the PhD project of Emmanuel Cruz in collaboration with the Laboratory for Provenance Studies (University of Milano-Bicocca), the Department of Analytical Chemistry (University of the Basque Country) and IKERBASQUE (Basque Society for the Promotion of Science):
Cruz Muñoz, E., Gosetti, F., Ballabio, D., Andò, S., Gómez-Laserna, O., Amigo, J.M., Garzanti, E. (2023) Characterization of pyrite weathering products by Raman hyperspectral imaging and chemometrics techniques. Microchemical Journal, 190, 108655 [link]
Condensed Phase Membrane Introduction Mass Spectrometry (MIMS): a review!
Membrane introduction mass spectrometry (MIMS) is a direct mass spectrometry technique used to monitor online chemical systems or quickly quantify trace levels of different groups of compounds in complex matrices without extensive sample preparation steps and chromatographic separation. Here a recent review:
V. Termopoli, M. Piergiovanni, D. Ballabio, V. Consonni, E. Cruz Muñoz, F. Gosetti (2023) Condensed phase membrane introduction mass spectrometry: a direct alternative to fully exploit the mass spectrometry potential in environmental sample analysis, Separations, 10, 139, https://doi.org/10.3390/separations10020139
New chapter: tutorial for multitask learning for QSAR
We have published a new chapter, which is a tutorial for training multitask neural networks in the framework of QSAR modelling, have a look here: https://doi.org/10.1007/978-3-031-20730-3_8
Valsecchi, C., Grisoni, F., Consonni, V., Ballabio, D., Todeschini, R. (2023). Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial. In Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. (Hong, H., eds), Springer