Ricerc@Sapienza

Avola almond is part of the “Traditional Italian Agri-food Product” (PAT) list, as established by The Italian Ministry of agricultural food, forestry and tourism policies; this endorsement testifies its status as a high added-value product, and, consequently, it highlights the need of analytical methodologies suitable for its authentication. For these reasons, in the present study, the possibility of developing a non-destructive approach, aimed at distinguishing almonds cultivated in the Avola area from others presenting a different geographical origin, has been investigated.

Modern analytical measurement technologies, such as infrared, NMR, mass spectrometry and chromatography, provide a wealth of information on the chemical composition of all kinds of samples. These instruments are invariably controlled by computers, and the data (spectrum, chromatogram) recorded in digital form. A measurement on a single sample typically comprises thousands of numbers. Usually, this is many more than the number of samples, meaning that the experiment overall is underdetermined.

Darjeeling black tea is a worldwide known tea variety which is currently part of the register of protected designations of origin (PDO) and protected geographical indications (PGI) as established by Commission Implementing Regulation (EU) No 1050/2011 of 20 October 2011. Therefore, preventing frauds against this product became increasingly important in order to protect producers and consumers from possible economic losses.

Pasta is a typical Italian food item obtained by durum wheat semolina/flour well-known and widely consumed all over the world. Since 2013, Gragnano Pasta, a typical aliment produced in a specific area in the South of Italy, has been awarded with the P.G.I. mark, remarking the high value of this product. Due to its peculiarity and its market value, it is important to characterize and authenticate the Gragnano Pasta. Considering this rationale, the present study aims at developing a non-destructive analytical methodology suitable for this goal.

Durum wheat (Triticum turgidum ssp. durum) is widely grown in the Mediterranean area. The semolina obtained by this grain is used to prepare pasta, couscous, and baked products all over the world. The growing area affects the characteristics of Durum wheat; consequently, it is relevant to trace this product. The present study aims at developing an analytical methodology which would allow tracing durum semolina harvested in 7 different Italian macro-areas.

With the development of technology and the relatively higher availability of
new instrumentations, having multiblock data sets (eg, a set of samples analyzed
by different analytical techniques) is becoming more and more common
and, as a consequence, how to handle this kind of outcomes is a widely
discussed topic. In such a context, where the number of involved variables is
relatively high, selecting the most significant features is obviously relevant.
For this reason, the possibility of joining a multiblock regression method, the

Foodomics is a newly developed discipline that has become more and more important in the last years where focus on food and the understanding of food systems has increased significantly. In this review, the flow of a typical foodomics study will be followed with a focus on the core components, where chemometric expertise is more deeply involved.

The world Rhinophoridae are catalogued, recognising 33 genera and 177 species. Nomenclatural information is provided for all genus-group and species-group names, including lists of synonyms and namebearing type data. Species distributions are recorded by country. A key to the world genera is presented.

This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects.

Calibrating a classification system consists in transforming the output scores, which somehow state the confidence of the classifier regarding the predicted output, into proper probability estimates. Having a well-calibrated classifier has a non-negligible impact on many real-world applications, for example decision making systems synthesis for anomaly detection/fault prediction. In such industrial scenarios, risk assessment is certainly related to costs which must be covered.