Ricerc@Sapienza

The best estimate plus uncertainty (BEPU) method plays a key role in the development of the innovative Generation IV nuclear reactors, for the improvement of knowledge and the good evaluation of the safety margins for new phenomena. The aim of this paper is to validate an uncertainty quantification (UQ) approach using RAVEN code. RAVEN, developed at the Idaho National Laboratory, is a multipurpose probabilistic and uncertainty quantification framework, capable to communicate with any system code, implemented with an integrated validation methodology involving several different metrics.

In this chapter, a survey of the chemometric (data analytical) methods most used for the characterization of plant varieties and cultivars based on spectroscopic measurements is presented. After an introductory section, illustrating the basics of data representation, the main tools for exploratory (descriptive) data analysis and predictive modeling are discussed. In particular, how to predict quantitative responses by multivariate calibration methods and how to assess qualitative attributes by means of classification techniques are addressed.

Classification methods, i.e., the chemometric strategies for predicting a qualitative response, find many applications in the omic sciences, where often data are collected in order to categorize individuals (e.g. according to whether they were treated or administered a placebo or, for instance, depending on if they were healthy or ill). After a brief discussion of the differences between discriminant and modelling approaches, some of the techniques most commonly used in the omic fields are illustrated in greater detail.

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial “Chemometrics in analytical chemistry.” This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields.

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.

Milk contains a variety of nutrients and is long associated with a number of health benefits. It is rich in high-quality proteins and important vitamins and minerals, including calcium, phosphorus and B vitamins. Recently, however, some people have started to avoid milk due to health problems, such as dietary restrictions, allergies and intolerances, and ethical issues regarding the use of animals. As a result, various types of non-standard dairy milk and nondairy milk beverages are now available (goat milk, donkey milk, soy milk, rice milk, almond milk, oat milk etc.).

Breast milk, the first and irreplaceable source of nourishment for the infant, and the wellbeing of both the mother and baby are increasingly threatened by contamination from environmental toxic agents. In particular, elements can be used as good indicators/tracers of environmental and food contamination. In turn, as well as urine [1–3], and serum [4], breast milk can be considered as suitable biological matrix for biomonitoring studies.

Breast milk guarantees all the nutrients required by infants during their first few months of life and remains the most important food source for their health and growth. However, the mother may transfer potentially toxic chemicals to the suckling infant through breastfeeding. The aim of this study was to optimize and validate a fast method for the determination of a total content of 34 elements (Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, Sb, Se, Si, Sn, Sr, Te, Ti, Tl, U, V, and Zn) in liquid and lyophilized breast milk.

The aim of the study was to develop and validate a rapid method for the analysis of the total Hg concentration in human hair, specifically, Eritrean hair. For total Hg determination, the cold vapour generation atomic fluorescence spectrometry (CV-AFS) technique was used, and the results were compared with those of the more frequently used advanced mercury analyser (AMA). Samples were prepared by washing the hair and collecting two samples; the first was used for the direct analysis of Hg by AMA and the second was digested for Hg determination by CV-AFS.

The determination of major and trace elements in non-invasive biological matrix (i.e. human hair, breast milk, meconium, and urine) is potentially useful for assessing an individual's health status and monitoring occupational and environmental exposure [1-3]. On the other hand, owing to the lack of standardised biological matrix analysis procedures (including sample treatment methods), it is difficult to compare and interpret the results (intervals and reference values) from different studies and reach significant conclusions.