Prof. Dr. Francesca Venturini

Prof. Dr. Francesca Venturini

ZHAW School of Engineering
Spectroscopy Team
Technikumstrasse 9
8400 Winterthur

+41 58 934 47 14
francesca.venturini@zhaw.ch

Arbeit an der ZHAW

Tätigkeit

Leitung Gruppe Spectroscopy
Stv. Schwerpunktleitung Angewandte Optik
Professorin für angewandte Optik
Koordinatorin MSE-Profil Photonics
Reviewer für Springer Nature, Elsevier, Optica, MDPI, Royal Society of Chemistry
Reviewer für European Research Council

Arbeits- und Forschungsschwerpunkte

Optik, Laserspektroskopie, Fluoreszenzspektroskopie, Raman Spektroskopie, Sensorik, Fiberoptik, Raman-Spektroskopie, Nicht-Lineare Optik, Festkörperphysik, Supraleitung, Magnettechnologie

Lehrtätigkeit

Physik 1 und 2
Case Studies Stock and Flow - Systeme 1 und 2
MSE: Optical Engineering and Metrology
CAS Digitale Technologien und Innovation

Lehrtätigkeit in der Weiterbildung

CAS Digitale Technologien und Innovation

Berufserfahrung

Expertin bei Innosuisse
Innosuisse
01 / 2023 - heute
Mitgründerin und Leiterin Forschung und Entwicklung
TOELT GmbH
11 / 2018 - heute
Professorin für angewandte Optik
ZHAW
12 / 2017 - heute
Projektleiterin, Physikdozentin
ZHAW
09 / 2013 - 11 / 2017
Leiterin Produktentwicklung R&D
Mettler-Toledo AG
06 / 2011 - 08 / 2013
Senior Projektleiter R&D
Mettler-Toledo AG
08 / 2008 - 05 / 2011
Projektleiter R&D
Bruker BioSpin AG
08 / 2003 - 07 / 2008

Aus- und Weiterbildung

Ausbildung

Promotion / Physik
Technische Universität München, München, DE
09 / 1998 - 04 / 2003
Forschungsaufenthalt im Ausland
The George Washington University, Washington DC, USA
08 / 1997 - 07 / 1998
Master of Science (M.Sc.) / Physik
Università degli Studi di Firenze, IT
10 / 1992 - 07 / 1997

Weiterbildung

CAS Hochschuldidaktik
Pädagogische Hochschule Zurich, PHZH
01 / 2016
PGCert Business Administration
The Open University Business School, UK
11 / 2007

Netzwerk

Mitglied in Netzwerken

ORCID digital identifier

ORCID ID: 0000-0003-2562-9932

Social Media

Projekte

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

Carella, A. et al. (2026) 'Large-scale orange fruit dataset for localization, classification and ripening assessment under varying environments', Computers and Electronics in Agriculture, 248(111833). doi: 10.1016/j.compag.2026.111833.
Michelucci, U. and Venturini, F. (2026) 'The infinite-dimensional nature of spectroscopy and why models succeed, fail, and mislead', The Analyst, 151(10), pp. 2978–2994. doi: 10.1039/d6an00346j.
Di Maria, M. et al. (2026) 'How can we sustainably assess the shelf life of EVOO? A systematic review on analytical strategies and food waste reduction', Frontiers in Nutrition, 12(1722145). doi: 10.3389/fnut.2025.1722145.
Venturini, F. et al. (2025) 'Multi-mycotoxin detection using fluorescence spectroscopy and machine learning', Food Control, 181(111728). doi: 10.1016/j.foodcont.2025.111728.
Smeesters, L. et al. (2025) '2025 photonics for agrifood roadmap : towards a sustainable and healthier planet', Journal of Physics: Photonics, 7(3), p. 032501. doi: 10.1088/2515-7647/adbea9.
El Ghazouali, S. et al. (2024) 'FlightScope : an experimental comparative review of aircraft detection algorithms in satellite imagery', Remote Sensing, 16(24), p. 4715. doi: 10.3390/rs16244715.
Michelucci, U. and Venturini, F. (2024) 'Deep learning domain adaptation to understand physico-chemical processes from fluorescence spectroscopy small datasets and application to the oxidation of olive oil', Scientific Reports, 14(22291). doi: 10.1038/s41598-024-73054-y.
Ahmad, A. et al. (2024) 'AI can empower agriculture for global food security : challenges and prospects in developing nations', Frontiers in Artificial Intelligence, 7(1328530). doi: 10.3389/frai.2024.1328530.
Venturini, F. et al. (2024) 'Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques', LWT - Food Science and Technology, 191(115679). doi: 10.1016/j.lwt.2023.115679.
Eggertson, E. C. and Venturini, F. (2023) 'Resonant Raman spectroscopy of carotenoids in aging of extra virgin olive oil', Sensors, 23(17), p. 7621. doi: 10.3390/s23177621.
Michelucci, U. and Venturini, F. (2023) 'New metric formulas that include measurement errors in machine learning for natural sciences', Expert Systems with Applications, 224(120013). doi: 10.1016/j.eswa.2023.120013.
Venturini, F., Fluri, S. and Baumgartner, M. (2023) 'Dataset of fluorescence EEM and UV spectroscopy data of olive oils during ageing', Data, 8(5), p. 81. doi: 10.3390/data8050081.
Venturini, F. et al. (2022) 'Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil', Journal of Food Engineering, 336(111198). doi: 10.1016/j.jfoodeng.2022.111198.
Michelucci, U. and Venturini, F. (2021) 'Estimating neural network's performance with bootstrap : a tutorial', Machine Learning and Knowledge Extraction, 3(2), pp. 357–373. doi: 10.3390/make3020018.
Michelucci, U. et al. (2021) 'A model-agnostic algorithm for Bayes error determination in binary classification', Algorithms, 14(11), p. 301. doi: 10.3390/a14110301.
Venturini, F. et al. (2021) 'Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques', Foods, 10(5), p. 1010. doi: 10.3390/foods10051010.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) 'Dual oxygen and temperature luminescence learning sensor with parallel inference', Sensors, 20(17), p. 4886. doi: 10.3390/s20174886.
Michelucci, U. and Venturini, F. (2019) 'Multi-task learning for multi-dimensional regression : application to luminescence sensing', Applied Sciences, 9(22). doi: 10.3390/app9224748.
Michelucci, U., Baumgartner, M. and Venturini, F. (2019) 'Optical oxygen sensing with artificial intelligence', Sensors, 19(4), p. 777. doi: 10.3390/s19040777.
Venturini, F., Baumgartner, M. and Borisov, S. (2018) 'Mn4+-doped magnesium titanate : a promising phosphor for self-referenced optical temperature sensing', Sensors, 18(2/668). doi: 10.3390/s18020668.
Chelwani, N. et al. (2018) 'Magnetic excitations and amplitude fluctuations in insulating cuprates', Physical Review B, 97(2). doi: 10.1103/PhysRevB.97.024407.
Venturini, F. et al. (2017) 'Characterization of light-gas interaction in strongly-scattering nanoporous materials and its implications for tunable diode laser absorption spectroscopy', Applied Physics B, 123(4), pp. 123–136. doi: 10.1007/s00340-017-6705-z.
Michelucci, U. and Venturini, F. (2017) 'Novel semi-parametric algorithm for interference-immune tunable absorption spectroscopy gas sensing', Sensors, 17(10), p. 2281. doi: 10.3390/s17102281.
Venturini, F. et al. (2015) 'Optical temperature sensing using a new thermographic phosphor', Sensors and Actuators A: Physical, 233, pp. 324–329. doi: 10.1016/j.sna.2015.07.009.
Munnikes, N. et al. (2011) 'Pair breaking versus symmetry breaking : origin of the Raman modes in superconducting cuprates', Physical Review B, 84(144523). doi: 10.1103/PhysRevB.84.144523.
Prestel, W. et al. (2010) 'Quantitative comparison of single- and two-particle properties in the cuprates', The European Physical Journal Special Topics, 188(1), pp. 163–171. doi: 10.1140/epjst/e2010-01304-2.
Hackl, R. et al. (2006) 'Raman study of ordering phenomena in copper–oxygen systems', Journal of Physics and Chemistry of Solids, 67(1-3), pp. 289–293. doi: 10.1016/j.jpcs.2005.10.126.
Tassini, L. et al. (2005) 'Dynamical properties of charged stripes in La(2-x)SrxCuO4', Physical Review Letters, 95(117002). doi: 10.1103/PhysRevLett.95.117002.
Zhang, Q. et al. (2003) 'New electronic Raman scattering results in underdoped La2−xSrxCuO4', Physica C: Superconductivity and its Applications, 286, pp. 282–285. doi: 10.1016/S0921-4534(02)02132-9.
Opel, M. and Venturini, F. (2002) 'Raman scattering in solids', European Pharmaceutical Review, 7(3), pp. 76–82.
Venturini, F. et al. (2002) 'Observation of an unconventional metal-insulator transition in overdoped CuO2 compounds', Physical Review Letters, 89(10), pp. 107003–1–107003–4. doi: 10.1103/PhysRevLett.89.107003.
Michelucci, U., Venturini, F. and Kampf, A. (2002) 'Quantum interference phenomena between impurity states in d-wave superconductors', Journal of Physics and Chemistry of Solids, 63(12), pp. 2283–2286. doi: 10.1016/S0022-3697(02)00238-X.
Venturini, F. et al. (2002) 'Doping dependence of the electronic Raman spectra in cuprates', Journal of Physics and Chemistry of Solids, 63(12), pp. 2345–2348. doi: 10.1016/S0022-3697(02)00239-1.
Venturini, F. et al. (2002) 'Raman scattering versus infrared conductivity : evidence for one-dimensional conduction in La(2-x)Sr(x)CuO(4)', Physical Review B, 66(6). doi: 10.1103/PhysRevB.66.060502.
Venturini, F., Hackl, R. and Michelucci, U. (2002) 'Comment on : nonmonotonic $d_{x^{2}-y^{2}}$ superconducting order parameter in Nd$_{2-x}$Ce$_x$CuO$_4$', Physical Review Letters, 90(14), pp. 149701–1. doi: 10.1103/PhysRevLett.90.149701.
Opel, M. et al. (2001) 'A light-scattering study of dynamical carrier properties in cuprate systems', Ferroelectrics, 249(1), pp. 155–164. doi: 10.1080/00150190108214977.
Venturini, F. et al. (2000) 'Collective spin fluctuation mode and raman scattering in superconducting cuprates', Physical Review B, 62, pp. 15204–15207. doi: 10.1103/PhysRevB.62.15204.
Opel, M. et al. (2000) 'Superconducting gap and pseudogap in Bi-2212', Physica B: Condensed Matter, 284-288, pp. 669–670. doi: 10.1016/S0921-4526(99)02344-3.
Opel, M. et al. (1999) 'Pseudogap and superconducting gap in YBa2Cu3o6+x : a raman study', Journal of Low Temperature Physics, 117(3-4), pp. 347–351. doi: 10.1023/A:1022513531861.
Opel, M. et al. (1999) 'Raman spectroscopy in YBa2Cu3O6+x and Bi2Sr2(CaxY1-x)Cu2O8+d : pseudogap and superconducting gap', Physica status solidi B, 215(1), pp. 471–476. doi: 10.1002/(SICI)1521-3951(199909)215:1<471::AID-PSSB471>3.0.CO;2-K.

Bücher, peer-reviewed

Venturini, F. (2003) Raman scattering study of electronic correlations in cuprates : observation of an unconventional metal insulator transition. Doctoral dissertation. Mensch-und-Buch-Verlag.

Buchbeiträge, peer-reviewed

Hackl, R. et al. (2005) 'Ordering phenomena in cuprates', in Kramer, B. (ed.) Advances in solid state physics. Heidelberg: Springer, pp. 227–238. doi: 10.1007/11423256_18.
Venturini, F. (2003) 'Evidence for a metal-insulator transition in overdoped Cuprates : new Raman results', in Kramer, B. (ed.) Advances in solid state physics. Springer, pp. 253–266. doi: 10.1007/978-3-540-44838-9_18.

Schriftliche Konferenzbeiträge, peer-reviewed

Michelucci, U. and Venturini, F. (2026) 'How high dimensionality enables perfect accuracy in machine learning applied to spectroscopy', in Bocklitz, T. (ed.). Society of Photo‑Optical Instrumentation Engineers. doi: 10.1117/12.3098897.
Magnus, I. et al. (2026) 'Predicting deoxynivalenol contamination in cereals using optical spectroscopy and regression analysis', in Berghmans, F., Zergioti, I., and Chiavaioli, F. (eds) Optical Sensing and Detection IX. Society of Photo‑Optical Instrumentation Engineers. doi: 10.1117/12.3100104.
Venturini, F. (2025) 'Innovative sensor technologies for agrifood quality assessment', in SMSI 2025 Conference Proceedings. AMA Service, pp. 188–189. doi: 10.5162/SMSI2025/D2.1.
Venturini, F. et al. (2024) 'Machine learning-enhanced fluorescence spectroscopy for the quality assessment of extra virgin olive oil during ageing', in Berghmans, F. and Zergioti, I. (eds) Optical Sensing and Detection VIII. SPIE, p. 129991F. doi: 10.1117/12.3016879.
Gucciardi, A. et al. (2024) 'Machine learning feature extraction for predicting the ageing of olive oil', in Bocklitz, T. (ed.) Data Science for Photonics and Biophotonics. SPIE, p. 130110A. doi: 10.1117/12.3017680.
Mathys, M., Gebbers, P. and Venturini, F. (2024) 'Probing carotenoids in the skin using resonant Raman spectroscopy', in SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. Available at: https://spie.org/photonics-europe/presentation/Probing-carotenoids-in-the-skin-using-resonant-Raman-spectroscopy/13010-48?enableBackToBrowse=true.
Schmid, C. et al. (2023) 'Explainable AI for the olive oil industry', in Hinkelmann, K., López-Pellicer, F. J., and Polini, A. (eds) Perspectives in Business Informatics Research. Cham: Springer, pp. 158–171. doi: 10.1007/978-3-031-43126-5_12.
Michelucci, U. et al. (2023) 'Deep learning super resolution for high-speed excitation emission matrix measurements', in Jalali, B. and Kitayama, K.-i. (eds) AI and Optical Data Sciences IV. SPIE. doi: 10.1117/12.2647589.
Venturini, F. et al. (2023) 'Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra', in Jalali, B. and Kitayama, K.-i. (eds) AI and Optical Data Sciences IV. SPIE. doi: 10.1117/12.2647809.
Sperti, M. et al. (2022) 'Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks', in Optical Sensing and Detection VII. SPIE, p. 121391K. doi: 10.1117/12.2621666.
Venturini, F. et al. (2022) 'One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge : explainability techniques applied to olive oil fluorescence spectra', in Berghmans, F. and Zergioti, I. (eds) Optical Sensing and Detection VII. SPIE, p. 1213917. doi: 10.1117/12.2621646.
Arnaud, G. et al. (2022) 'Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil', in Berghmans, F. and Zergioti, I. (eds) Optical Sensing and Detection VII. SPIE, p. 121391J. doi: 10.1117/12.2621588.
Gucciardi, A. et al. (2022) 'Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil', in SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022. doi: 10.1117/12.2621588.
Sperti, M. et al. (2022) 'Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks', in SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022. Available at: https://spie.org/EPE/conferencedetails/optical-sensing-detection.
Venturini, F., Michelucci, U. and Baumgartner, M. (2021) 'New approach for temperature-immune oxygen sensing based on Pt-TFPP', in OSA Technical Digest. Optica Publishing Group, p. SW5H.2. doi: 10.1364/SENSORS.2021.SW5H.2.
Venturini, F., Michelucci, U. and Baumgartner, M. (2021) 'Implementation of multi-task learning neural network architectures for robust industrial optical sensing', in Lehmann, P., Osten, W., and Albertazzi Gonçalves Jr., A. (eds) Optical Measurement Systems for Industrial Inspection XII. Bellingham: SPIE. doi: 10.1117/12.2593469.
Venturini, F., Michelucci, U. and Baumgartner, M. (2021) 'Implementation of multi-task learning neural network architectures for robust industrial optical sensing', in Lehmann, P., Osten, W., and Albertazzi Gonçalves Jr., A. (eds) Optical Measurement Systems for Industrial Inspection XII. Bellingham: SPIE, p. 117822H. doi: 10.1117/12.2593469.
Michelucci, U. and Venturini, F. (2021) 'New autonomous intelligent sensor design approach for multiple parameter inference', in Engineering Proceedings. MDPI, p. 96. doi: 10.3390/engproc2020002096.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) 'Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks', in Proceedings Volume 11354 : Optical Sensing and Detection VI. Bellingham: SPIE. doi: 10.1117/12.2554941.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) 'Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor', in Proceedings Frontiers in Optics / Laser Science. Optica Publishing Group.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) 'Multi-task learning approach for optical luminescence sensing', in Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020.
Venturini, F., Baumgartner, M. and Michelucci, U. (2019) 'New approach for luminescence sensing based on machine learning', in Jalali, B. and Kitayama, K.-i. (eds) Proceedings Volume 10937 : Optical Data Science II. SPIE. doi: 10.1117/12.2508969.
Venturini, F., Baumgartner, M. and Borisov, S. M. (2018) 'New opportunities for optical temperature sensing with Mn4+-doped magnesium titanate', in Advanced Photonics 2018. doi: 10.1364/BGPPM.2018.JTu2A.63.
Venturini, F., Schönherr, V. and Adolfsson, E. (2017) 'Ultracompact oxygen sensor using nanoporous materials as strongly-scattering multipass cell for tunable diode laser absorption spectroscopy', in The European Conference on Lasers and Electro-Optics 2017. IEEE.
Venturini, F. and Michelucci, U. (2017) 'Novel algorithm for calibration-free absorption spectroscopy sensor', in Proceedings. Basel: MDPI. doi: 10.3390/proceedings1080833.
Venturini, F. et al. (2016) 'Characterization of light-gas interaction in strongly-scattering nanoporous materials and its implications for tunable diode laser absorption spectroscopy', in International Conference on Field Laser Applications in Industry and Research (FLAIR 2016), Aix-les-Bains, Frankreich, 12.-16. September 2016.
Borisov, S. et al. (2015) 'Temperature sensing and sensor design using a new thermographic phosphor for a wide range of applications', in Proceedings 2015 European Conference on Lasers and Electro-Optics - European Quantum Electronics Conference. Optical Society of America.
Borisov, S. et al. (2015) 'Investigation of the Luminescence Emission of Chromium(III)-Doped Yttrium Aluminum Borate for the Design of an Optical Temperature Sensor', in MAF 14, The 14th Conference on Methods and Applications in Fluorescence, Würzburg, Germany, 13-16 September 2015.
Venturini, F. et al. (2000) 'Collective modes and electronic raman scattering in the cuprates', in Physica C: Superconductivity and its Applications. Elsevier, pp. 2265–2266. doi: 10.1016/S0921-4534(00)00972-2.
Minardi, F. et al. (1997) 'Frequency doubling of 1083 nm diode-laser radiation for locking to iodine absorption', in Zhang, Z. (ed.) Laser Spectroscopy: Xiii International Conference : Hangzhou, China 2-7 June 1997. Singapore: World Scientific Publishing.

Schriftliche Konferenzbeiträge, nicht peer-reviewed

Venturini, F. et al. (2023) 'Dataset of fluorescence spectra and chemical parameters of olive oils', in SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023. arXiv. doi: 10.48550/arXiv.2301.04471.

Mündliche Konferenzbeiträge und Abstracts, peer-reviewed

Venturini, F. et al. (2026) 'Fluorescence spectroscopy for multimycotoxin detection', in Berghmans, F., Zergioti, I., and Chiavaioli, F. (eds) Optical Sensing and Detection IX. SPIE. doi: 10.1117/12.3098856.
Venturini, F. (2026) 'Comparative evaluation of chemometric and machine learning approaches for Raman-based wine analysis', in Data Science for Photonics and Biophotonics II, Stuttgart, Germany, 12-13 April 2026.
Venturini, F. (2025) 'Overcoming intrinsic limitations of fluorescence spectroscopy data in food quality and safety with machine learning and deep learning', in Optica Sensing Congress 2025 (AIS, Sensors, QSM). Optica Publishing Group, p. AM2E.3. doi: 10.1364/AIS.2025.AM2E.3.
Venturini, F. (2025) 'Innovative sensor technologies for agrifood quality assessment', in Sensor and Measurement Science International, Nuremberg, Germany, 6-8 May 2025.
Gucciardi, A. et al. (2024) 'Machine learning feature extraction for predicting the ageing of olive oil', in SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. doi: 10.1117/12.3017680.
Venturini, F. et al. (2024) 'Machine learning-enhanced fluorescence spectroscopy for the quality assessment of extra virgin olive oil during ageing', in SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. doi: 10.1117/12.3016879.
Venturini, F. et al. (2023) 'Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra', in SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023.
Venturini, F. et al. (2022) 'One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge : explainability techniques applied to olive oil fluorescence spectra', in SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022. Available at: https://spie.org/photonics-europe/presentation/One-dimensional-convolutional-neural-networks-design-for-fluorescence-spectroscopy-with/12139-60.
Venturini, F., Michelucci, U. and Baumgartner, M. (2021) 'New approach for temperature-immune oxygen sensing based on Pt-TFPP', in OSA Optical Sensors and Sensing Congress, online, 19-23 July 2021. Available at: https://osa.zoom.us/rec/play/MvNgUQyUZcCHsaYidUEieFhKJSC_0H951ohgwMQLgaJu2hOUG4Cp8c6EbckrxwSFPVgA4icXYkOKpYHl.bke5ll04aQIuRasd?continueMode=true&_x_zm_rtaid=rEeKPUbBRbuPidh8EtTeUQ.1630395453945.87ac6bdfd1c3781b0e74319831d7e46a&_x_zm_rhtaid=928.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) 'Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor', in OSA Frontiers in Optics / Laser Science, online, 14-17 September 2020.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) 'Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks', in SPIE Photonics Europe, Digital Forum, France, 6 - 10 April 2020.
Venturini, F. and Baumgartner, M. (2019) 'New approach for luminescence sensing based on machine learning', in SPIE OPTO, San Francisco, USA, 2-7 February 2019.
Venturini, F. (2016) 'Progress in NIR spectroscopy and its industrial applications', in Swiss Photonics Optical Sensing Workshop : From THz to X-ray - Inventing the Future, Zurich (Switzerland), 31 March 2016. Available at: https://www.swissphotonics.net/workshops/workshop-datenbank?2914.
Venturini, F. et al. (2014) 'From forces of nature to the physics of dynamical systems', in 9th International Conference on Conceptual Change, Bologna, Italy, 26-29 August 2014.

Mündliche Konferenzbeiträge und Abstracts, nicht peer-reviewed

Venturini, F. (2020) 'Applications of ML to oxygen sensing', in Deep learning meets (Astro)physics : A one day tutorial on neural nets, deep learning and TensorFlow 2.0, with applications to (astro)physics and science, ETHZ, Institute for Particle Physics and Astrophysics, Zurich (Switzerland), 22 January 2020. Available at: https://sites.google.com/view/dl-meets-astrophysics-2020/.

Forschungsdaten

Venturini, Francesca; Sperti, Michela; Michelucci, Umberto; Gucciardi, Arnaud; Martos, Vanessa; Deriu, Marco Agostino, 2022. Dataset of fluorescence spectra and chemical parameters of olive oils. Mendeley Data.

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