S
Silvia Serranti
Researcher at Sapienza University of Rome
Publications - 177
Citations - 2744
Silvia Serranti is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 22, co-authored 152 publications receiving 2036 citations. Previous affiliations of Silvia Serranti include ENEA.
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Journal ArticleDOI
Early detection of toxigenic fungi on maize by hyperspectral imaging analysis
A. Del Fiore,Massimo Reverberi,Alessandra Ricelli,Flavia Pinzari,Silvia Serranti,Anna Adele Fabbri,Giuseppe Bonifazi,Corrado Fanelli +7 more
TL;DR: The results show that the hyperspectral imaging is able to rapidly discriminate commercial maize kernels infected with toxigenic fungi from uninfected controls when traditional methods are not yet effective.
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Characterization of post-consumer polyolefin wastes by hyperspectral imaging for quality control in recycling processes
TL;DR: New analytical inspection strategies, based on hyperspectral imaging (HSI) in the VIS-NIR and NIR wavelength ranges, have been investigated and set up in order to define quality control logics that could be applied at industrial plant level for polyolefins recycling.
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Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging.
TL;DR: HSI was revealed as a rapid, non-invasive,non-destructive and reliable technology for the characterization of the microplastic waste, opening a promising way for improving the plastic pollution monitoring.
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Classification of oat and groat kernels using NIR hyperspectral imaging
TL;DR: An innovative procedure based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated, showing that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%).
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Classification of polyolefins from building and construction waste using NIR hyperspectral imaging system
TL;DR: In this paper, a hyperspectral imaging system in the near infrared (NIR) range (1000-1700 nm) was developed to classify polyolefin particles from complex waste streams in order to improve their recovery, producing high purity polypropylene (PP) and polyethylene (PE) granulates, according to market requirements.