Ricerc@Sapienza

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.

Optical analysis of reaction parameters such as enantiomeric excess (ee), diastereomeric excess (de), and yield are becoming increasingly useful as assays for differing functional groups become available. These assays typically exploit reversible covalent or noncovalent assemblies that impart optical signals, commonly circular dichroism (CD), that are indicative of the stereochemistry and ee at a stereocenter proximal to the functional group of interest.

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.

Among grains, rice is one of the most widely consumed cereals in the world; it represents a staple food in great part of Asia and Africa, and it is also broadly diffused in America and Europe. One of the main issues of storing rice is to protect it from animal attacks; in particular, it is prone to insect infestation. Despite all the attempts made to avoid it (developing new physical barriers, traps and repellants), often food pests manage to break into granary and parcels, contaminating stored commodities.

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models.

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.

A method to reduce repeatability error in multivariate data for Analysis of variance-Simultaneous Component Analysis (REP-ASCA) has been developed. This method proposes to adapt the acquisition protocol by adding a set containing repeated measures for describing repeatability error. Then, an orthogonal projection is performed in the row-space to reduce the repeatability error of the original dataset. Finally, ASCA is performed on the orthogonalized dataset. This method was evaluated on NIR spectral data of coffee beans.

Applications of high-resolution NMR in liquid-state and in semi-solid matrices in the analysis of food components and entire food samples are described using examples of different food matrices and different problems related to food safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.