The characterization of microplastics (MPs) in environmental matrices is frequently hampered by the spectral complexity of heterogeneous mixtures and the presence of interferents. This study has validated a new analytical framework using portable Near-Infrared (NIR) spectroscopy combined with chemometric modelling to move beyond qualitative detection toward field quantification: Cruz Muñoz, E., Marchesi, C., Rigo, M., Ali, S., Depero, L., de Lucia, G., Camedda, A., Prati, S., Ballabio, D., Sciutto, G., Federici, S. (2026) Near-infrared (NIR) spectroscopy for quantitative modelling of quaternary microplastic mixtures and the effect of interferents, Microchemical Journal, 225, 118027 [link]
Category Archives: News
QSPR to predict the chromatographic retention time of bioactive compounds
We used chemometrics to establish a QSPR (Quantitative Structure Property Relationship) model between the chemical structure of bioactve compounds and their chromatographic retention time. The open access paper is now available: Sepehri, B., Consonni, V., Ballabio, D., Cruz Muñoz, E., Abbasi, E., Todeschini, R. (2025) Application of QSRR models for predicting the retention times of plant food bioactive compounds, Journal of Chromatography A, 1758, 466194 [link]
Moreover, data to reproduce the proposed QSPR models are available at the following link: https://michem.unimib.it/download/data/retention-times-bioactive-compounds-qspr/
Chemometrics and NMR to detect turmeric adulteration in saffron
We used NMR and chemometrics to demontsrate that it is possible to detect turmeric adulteration (even at low levels) in Italian saffron, here the open access publication we made in collabroation with the University of Bolzano: Angeli, L., Cruz Muñoz, E., Ballabio, D., Morozova, K., Scampicchio, M. (2026) 1H NMR spectroscopy combined with chemometrics for detection of turmeric adulteration in Italian saffron (Crocus sativus L.), Food Control, 179, 111560 [link]
Kernel-based reliability potential to assist QSPR prediction and system transfer of SFC−MS retention time
We have recently developed a QSPR framework for the in-silico prediction and system transfer of SFC−MS retention time on the basis of molecular structures. Check the open access publication: Consonni, V., Rojas, C., Guerrero, J., Mendoza, M., Termopoli, V., Ballabio, D. (2025) Kernel-based reliability potential to assist QSPR prediction and system transfer of SFC−MS retention time, Chemometrics and Intelligent Laboratory Systems, 263,105435 [link]
Data to reproduce the models proposed in the manuscript are available in the download section of our website, at this link.
ChemTastesDB: A Curated Database of Molecular Tastants
ChemTastesDB is a database that includes curated information of 4075 molecular tastants. ChemTastesDB is distributed to the scientific community to expand the information of molecular tastants, which could assist the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR/QSPR) studies for taste prediction.
The latest version of ChemTastesDB (released in March 2025) is available at the following link: https://zenodo.org/records/15051366
The 4075 molecular tastants are categorized into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, non-bitter, multitaste and miscellaneous). The molecules are categorized into following ten classes: sweet (1313), bitter (1615), umami (220), sour (49), salty (16), multitaste (179), tasteless (232), non-sweet (304), non-bitter (28), and miscellaneous (119).
Examples of QSPR approaches for the prediction of molecular taste are given in the following publication: Rojas, C., Abril-González, M., Ballabio, D. & García, F. (2025). ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants. Chemometrics and Intelligent Laboratory Systems. 261, 105380. https://doi.org/10.1016/j.chemolab.2025.105380.
Cosmic spikes and saturated pixels in hyperspectral Raman spectroscopy
ARCHER is a new algorithm for automatic removal of cosmic spikes and saturated pixels in hyperspectral Raman spectroscopy; here the full open access publication: Cruz Muñoz, E., Ballabio, D., Amigo, J,M. (2025) ARCHER. A new algorithm for Automatic Removal of Cosmic Spikes and Saturated Pixels in Hyperspectral Raman spectroscopy, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 336, 126041 [link]
PhD defense by Emmanuel Cruz
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/
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
Lecture of prof. Todeschini at the Istituto Lombardo – Accademia di Scienze e Lettere
Thursday 9 November 2023 at the Istituto Lombardo – Accademia di Scienze e Lettere in Milan prof. Todeschini was invited to hold a conference on the scientific activity of his great-grandfather prof. Wilhelm Körner entitled “Wilhelm Körner: dalla molecola alla struttura molecolare”. Slides are available at this link.
