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.

Effects of Global Warming on Berry Composition of cv. Sangiovese: Biochemical and Molecular Aspects and Agronomical Adaptation Approaches

Wine grape must deal with serious problems due to the unfavorable climatic conditions resulted from global warming. High temperatures result in oxidative damages to grape vines. The excessive elevated temperatures are critical for grapevine productivity and survival and contribute to degradation of grape and wine quality and yield. Elevated temperature can negatively affect anthocyanin accumulation in red grape. Particularly, cv. Sangiovese was identified to be very sensitive to such condition. The quantitative real-time PCR analysis showed that flavonoid biosynthetic genes were slightly repressed by high temperature. Also, the heat stress repressed the expression of the transcription factor “VvMYBA1” that activates the expression of UFGT. Moreover, high temperatures had repressing effects on the activity of the flavonoids biosynthetic enzymes “PAL” and “UFGT”.Anthocyanin accumulation in berry skin is due to the balance between its synthesis and oxidation. In grape cv. Sangiovese, the gene transcription and activity of peroxidases enzyme was elevated by heat stress as a defensive mechanism of ROS-scavenging. Among many isoforms of peroxidases genes, one gene (POD 1) was induced in Sangiovese under thermal stress condition. This gene was isolated and evaluated via the technique of genes transformation from grape to Petunia. Reduction in anthocyanins concentration and higher enzymatic activity of peroxidase was observed in POD 1 transformed Petunia after heat shock compared to untrasformed control. Moreover, in wine producing regions, it is inevitable for the grape growers to adopt some adaptive strategies to alleviate grape damages to abiotic stresses. Therefore, in this thesis, the technique of post veraison trimming was done to improve the coupling of phenolic and sugar ripening in Vitis vinifera L. cultivar Sangiovese. Trimming after veraison showed to be executable to slow down the rate of sugar accumulation in grape (to decrease the alcohol potential in wines) without evolution of the main berry flavonoids compounds.