Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Ricerca di Escherichia coli produttori di verocitotossine e definizione di obiettivi di performance nella produzione primaria di alimenti di origine animale

La presenza di Escherichia coli produttori di verocitotossine (VTEC o STEC) rappresenta una tra le più importanti cause di malattia alimentare attualmente presenti in Europa. La sua presenza negli allevamenti di animali destinati alla produzione di alimenti rappresenta un importante rischio per la salute del consumatore. In conseguenza di comuni contaminazioni che si realizzano nel corso della macellazione, della mungitura i VTEC possono essere presenti nelle carni e nel latte e rappresentano un grave rischio se la preparazione per il consumo o i processi di lavorazione non comportano trattamenti in grado d’inattivarli (es. carni crude o poco cotte, latte non pastorizzato, formaggi freschi a latte crudo). La contaminazione dei campi coltivati conseguente alla dispersione di letame o attraverso acque contaminate può veicolare questi stipiti che sono normalmente albergati nell’intestino di ruminanti (domestici e selvatici) e anche prodotti vegetali consumati crudi, succhi e perfino sementi sono stati implicati in gravi episodi di malattia con gravi manifestazioni enteriche e complicazioni in grado di causare quadri patologici gravi e anche la morte. Stipiti di VTEC patogeni ingeriti con gli alimenti possono causare sintomi gastroenterici, con diarrea acquosa o emorragica (nel 50% dei casi), crampi addominali, febbre lieve e in una percentuale più bassa nausea e vomito. In alcuni casi (circa 5-10%) l’infezione gastroenterica si complica con manifestazioni tossiemiche caratterizzate da Sindrome Emolitico Uremica (SEU o HUS) con anemia emolitica, insufficienza renale grave e coinvolgimento neurologico o con una porpora trombotica trombocitopenica. Il tasso di mortalità dei pazienti che presentano l’infezione da E. coli è inferiore all’1%. I dati forniti dall’ECDC sulle infezioni alimentari nel periodo 2006-2010 hanno evidenziato un trend in leggero aumento del numero di infezioni a partire dal 2007. L’obiettivo degli studi condotti è quello di valutare la prevalenza ed il comportamento dei VTEC per una analisi del rischio più approfondita.