Ricerc@Sapienza

Data generated from spectroscopy may be deformed by artefacts due to a range of physical, chemical and environmental factors that are not of interest for the characterization of the samples under study. For example, data acquired by near-infrared (NIR) spectroscopy in the diffuse reflectance mode can be affected by light scattering. This artefact, if not reduced or removed by spectral pre-processing, can complicate the multivariate data analysis. However, different pre-processing approaches correct these effects in different ways.

Near-infrared (NIR) spectroscopy of fuels can suffer from scattering effects which may mask the signals corresponding to key analytes in the spectra. Therefore, scatter correction techniques are often used prior to any modelling so to remove scattering and improve predictive performances. However, different scatter correction techniques may carry complementary information so that, if jointly used, both model stability and performances could be improved. A solution to that is the fusion of complementary information from differently scatter corrected data.

Near infrared (NIR) spectroscopy allows rapid estimation of quality traits in fresh fruit. Several portable spectrometers are available in the market as a low-cost solution to perform NIR spectroscopy. However, portable spectrometers, being lower in cost than a benchtop counterpart, do not cover the complete near infrared (NIR) spectral range. Often portable sensors either use silicon-based visible and NIR detector to cover 400–1000 nm, or InGaAs-based short wave infrared (SWIR) detector covering the 900–1700 nm.