Ricerc@Sapienza

The aim of the present study is to authenticate Grappa spirits and to develop a non-destructive methodology which would allow detecting possible adulteration (by less valuable spirits) on this product. Grappa is an Italian alcoholic drink obtained by distillation of grape marks which has recently received the Geographical Indication (GI) label.

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.

In recent years, due to advances in sensor technology, multi-modal measurement of process and products properties has become easier. However, multi-modal measurements are only of use if the data from adding new sensors is worthwhile, especially in the case of industrial applications where financial justification is needed for new sensor purchase and integration, and if the multi-modal data generated can be properly utilised.

Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics.

With the growing availability of high throughput methodologies for food characterization and analysis, more and more data are being collected on food products that can be used for the authentication of their quality. In this context, the availability of different multi-block strategies, each with its own peculiarities and providing specific details on the investigated samples, allows to integrate the information from the different sources into a richer model with great flexibility.

The potential of Fourier Transform Near-Infrared spectroscopy (FT-NIR) and High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) in combination with multivariate data analysis was examined to classify 70 honey samples (belonging to 7 different varieties) according to their botanical origin. In the first part of the work, classification was achieved by applying PLS-DA to the individual data blocks: this approach led to promising results from the prediction point of view.

Obstacle detection is a fundamental task for Unmanned Aerial Vehicles (UAV) as a part of a Sense and Avoid system. In this study, we present a method of multi-sensor obstacle detection that demonstrated good results on different kind of obstacles. This method can be implemented on low-cost platforms involving a DSP or small FPGA. In this paper, we also present a study on the typical targets that can be tough to detect because of their characteristics of reflectivity, form factor, heterogeneity and show how data fusion can often overcome the limitations of each technology.