Lorenzo Sassu

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In this work, the problem of real-time monitoring of products’ properties from spectrophotoscopic measurements is presented. Light absorbance spectra are used as inputs to software sensors that estimate outputs otherwise difficult to measure on-line. We approached the problems associated to calibrating the estimation models from very high-dimensional inputs(More)
The Delaunay tessellation and topological regression is a local simplex method for multivariate calibration. The method, developed within computational geometry, has potential for applications in online analytical chemistry and process monitoring. This study proposes a novel approach to perform prediction and extrapolation using Delaunay calibration method.(More)
In this work, we report the feasibility study to predict the properties of neat crude oil samples from 300-MHz NMR spectral data and partial least squares (PLS) regression models. The study was carried out on 64 crude oil samples obtained from 28 different extraction fields and aims at developing a rapid and reliable method for characterizing the crude oil(More)
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