Remo Bucci

Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication.

Five different instrumental techniques: thermogravimetry, mid-infrared, near-infrared, ultra-violet and visible spectroscopies, have been used to characterize a high quality beer (Reale) from an Italian craft brewery (Birra del Borgo) and to differentiate it from other competing and lower quality products. Chemometric classification models were built on the separate blocks using soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) obtaining good predictive ability on an external test set (75% or higher depending on the technique). The use of data fusion strategies - in particular, the mid-level one - to integrate the data from the different platforms allowed the correct classification of all the training and validation samples.

Proton NMR relaxation study of swelling and gelatinisation process in rice starch–water samples

Proton transverse magnetization decay curves of rice flour starch-water samples were measured and analysed for the presence of four components in the relaxation curve. T2 values were interpreted on the basis of the diffusive and chemical exchange model that provided evidence for extra granular bulk water and three more water populations whose relaxation rate is governed by diffusive and chemical exchange with starch components. The analysis of relaxation data provided information on dynamics of water molecules as well as on the size and dispersion of diffusive domains. Furthermore, by measuring solid to liquid ratio, transverse and longitudinal relaxation curves of starch-water mixtures at increasing temperatures - from 20 to 77°C - swelling and gelatinisation processes were monitored.

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.

Supervised pattern recognition to discriminate the geographical origin of rice bran oils: a first study

Supervised pattern recognition appears to be a useful tool to authenticate foodstuffs according to their geographical or varietal origin, when a set of samples whose classification is known a priori are available. In this work, linear discriminant analysis and artificial neural networks trained by the back-propagation algorithm have been used to discriminate rice bran oils manufactured in three different countries (Italy, Thailand and Switzerland) according to their geographical origin. The variables to be included in the mathematical models have been chosen by means of Fisher F-ratio value among the chemical indices routinely determined on vegetable oils (particularly fatty acids, triglycerides and sterol composition). The prediction ability of all the classifiers was 100% as evaluated by cross-validation.

Spectroscopic characteristics and thermal properties of divalent metal complexes of diclofenac

Abstract The spectroscopic characteristics (IR and diffuse reflectance) and the thermal properties (TG, DTG and DSC) of the solid compounds obtained by the reaction of diclofenac (DH) with manganese(II), iron(II), cobalt(II), nickel(II), copper(II) and zinc(II) are reported and discussed. The complexes agreed with the empirical formula MeD 2 ·(H 2 O) x and the metal ions are co-ordinated through the carboxylate group of the ligand. The furnace atmosphere (N 2 or O 2 ) greatly influences the thermal decomposition trend of the complexes, less their thermal stability. On the contrary, the initial decomposition temperature depends on the co-ordinated metal ion, especially in the case of iron(II) and copper(II) compounds, which are much less stable than other complexes.

Classification and Class-Modelling

Abstract This chapter describes the basic theory about classification, starting from a general description of the different approaches to classification and then illustrating in detail the principal methods which are used in the framework of assessment of food quality. Examples of application of the methods to different data sets are also provided.

Use of near infrared reflectance and transmittance coupled to robust calibration for the evaluation of nutritional value in naked oats

A rapid accurate and precise method for simultaneous determination of β-glucan and protein content in naked oat samples, based on the coupling of near-infrared spectroscopy and chemometrics, is presented. In particular, three different spectroscopic approaches [near infrared reflectance (NIR) and transmittance (NIT) on flour and NIT on whole grains] and various spectral pretreatments were considered. To account for the possibility of outlying samples, a robust version of the PLS algorithm (namely partial robust M-regression) was used. All the models resulted as accurate as the reference methods, reflectance spectroscopy being the technique providing the best outcomes. Variable reduction by inclusion of the most relevant predictors only (as evaluated by VIP scores) resulted in simpler and, in one case, more parsimonious models, without loss in accuracy.

Geographical characterization by MAE-HPLC and NIR methodologies and carbonic anhydrase inhibition of Saffron components

A microwave-assisted extraction method was optimised for the recovery of bioactive compounds from Crocus sativus L. stigmas with the use of water/ethanol mixture. HPLC-DAD was employed to evaluate the extraction parameters, in particular, solvent type and volume, and the duration of the procedure. Microwave-assisted extraction enhanced the recovery of the active principles, limiting extraction time and solvent waste. Moreover, NIR experiments were performed in order to compare spectra in pseudo-absorbance of Saffron samples with different geographical origins through the application of the chemometric techniques. Moreover, the biological evaluation of crocin 1, safranal and its semisynthetic derivatives as selective inhibitors of five isoforms of human carbonic anhydrase was also explored.

Using thermoanalytical data: Part 1. The power of kinetic parameters determined from thermogravimetry to describe the experimental behaviour of the sample

Abstract Sources of misunderstandings when evaluating kinetic parameters from thermogravimetric (TG) curves are discussed. A simple method for calculating Arrhenius parameters, suitable for implementation on a PC connected to the TG apparatus, is presented. The results obtained with calcium oxalate are discussed with respect to their ability to predict thermal behaviour in several experimental conditions: agreement with experimental data is satisfactory. This is not so when using some literature-cited kinetic parameters, which fail to represent the same TG curves from which they are extracted. Suggestions are derived for design of a modern TG apparatus connected to a PC.

Using thermoanalytical data. Part 2. The dependence of kinetic data available from thermogravimetry on sample and instrument parameters: a method for calculating ‘true’ kinetic parameters

Abstract Kinetic data available from TG measurements are discussed with respect to thermal coefficients such as sample specific heat, reaction enthalpy, instrument heat-transfer coefficient and heat capacity. The dependence of the available kinetic parameters Z, E and N on these thermal coefficients is examined using a numerical model. A relationship is also derived for calculating the kinetic parameters from experimental TG curves whilst taking into account the perturbation induced by the above-cited thermal coefficients. The dehydration of calcium oxalate is used to evaluate experimentally the predictive power of the kinetic parameters obtained in this way, i.e. their ability to describe the sample behaviour under a wide range of operational conditions (different masses and heating rates), including isothermal reactions.