Alessandro Ulrici

Adulteration of the anthocyanin content of red wines: perspectives for authentication by Fourier transform-near infrared and 1H NMR spectroscopies.

In the Italian oenological industry, the regular practice used to naturally increase the colour of red wines consists in blending them with a wine very rich in anthocyanins, namely Rossissimo. In the Asian market, on the other hand, anthocyanins extracted by black rice are frequently used as correctors for wine colour. This practice does not produce negative effects on health; however, in many countries, it is considered as a food adulteration. The present study is therefore aimed to discriminate wines containing anthocyanins originated from black rice and grapevine by using reliable spectroscopic techniques requiring minimum sample preparation. Two series of samples have been prepared from five original wines, that were added with different amounts of Rossissimo or of black rice anthocyanins solution, until the desired Colour Index was reached. The samples have been analysed by FT-NIR and (1)H NMR spectroscopies and the resulting spectra matrices were subjected to multivariate classification. Initially, PLS-DA was used as classification method, then also variable selection/classification methods were applied, i.e. iPLS-DA and WILMA-D. The classification with variable selection of NIR spectra permitted to classify the test set samples with an efficiency of about 70%. Probably these not excellent performances are due to the matrix effect, together with the lack of sensitivity of NIR with respect to minor compounds. On the contrary, very satisfactory results were obtained on NMR spectra in the aromatic region between 6.5 and 9.5 ppm. The classification method based on wavelet-based variables selection, permitted to reach an efficiency in validation greater than 95%. Finally, 2D correlation analysis was applied to FT-NIR and (1)H NMR matrices, in order to recognise the spectral zones bringing the same chemical information.

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration

In the present work, we explored the possibility of using near-infrared spectroscopy in order to quantify the degree of adulteration of durum wheat flour with common bread wheat flour. The multivariate calibration techniques adopted to this aim were PLS and a wavelet-based calibration algorithm, recently developed by some of us, called WILMA. Both techniques provided satisfactory results, the percentage of adulterant present in the samples being quantified with an uncertainty lower than that associated to the Italian official method. In particular the WILMA algorithm, by performing feature selection, allowed the signal pretreatment to be avoided and obtaining more parsimonious models.

WPTER: wavelet packet transform for efficient pattern recognition of signals

Abstract In the present work, we propose a novel algorithm based on the Wavelet Packet Transform (WPT) for pattern recognition of signals, which operates both feature selection and classification at the same time: Wavelet Packet Transform for Efficient pattern Recognition of signals (WPTER). The distinctive characteristics of WPTER with respect to the previously proposed algorithms for the WPT-based classification of signals consist mainly of two aspects: (1) a Classification Ability criterion is introduced into the procedure for selection of the best discriminant basis; (2) the signals are reconstructed in the original domain by using only the selected wavelet coefficients, which allow for chemical interpretation of the results. The algorithm was first tested on an artificial (simulated) set of signals, consisting of a number of subsequent peaks, partially overlapped to each other, with added noise and baseline drift, simulating a three-class system. Then, it was applied to a data set consisting of X-ray diffractograms on fired tiles subjected to different firing cycles, aiming at discriminating the different firing methods on the basis of the phase composition. In both cases, satisfactory classifications were achieved.

Development of Quantitative Structure−Property Relationships Using Calculated Descriptors for the Prediction of the Physicochemical Properties (nD, ρ, bp, ε, η) of a Series of Organic Solvents

Quantitative structure−property relationship (QSPR) models were derived for predicting boiling point (at 760 mmHg), density (at 25 °C), viscosity (at 25 °C), static dielectric constant (at 25 °C), and refractive index (at 20 °C) of a series of pure organic solvents of structural formula X−CH2CH2−Y. A very large number of calculated molecular descriptors were derived by quantum chemical methods, molecular topology, and molecular geometry by using the CODESSA software package. A comparative analysis of the multiple linear regression techniques (heuristic and best multilinear regression) implemented in CODESSA, with the multivariate PLS/GOLPE method, has been carried out. The performance of the different regression models has been evaluated by the standard deviation of prediction errors, calculated for the compounds of both the training set (internal validation) and the test set (external validation). Satisfactory QSPR models, from both predictive and interpretative point of views, have been obtained for all...

Amperometric sensors based on poly (3,4-ethylenedioxythiophene)-modified electrodes: Discrimination of white wines

The voltammetric responses on selected white wines of different vintages and origins have been systematically collected by three different modified electrodes, in order to check their effectiveness in performing blind analysis of similar matrices. The electrode modifiers consist of a conducting polymer, namely poly(3,4-ethylenedioxythiophene) (PEDOT) and of composite materials of Au and Pt nanoparticles embedded in a PEDOT layer. Wine samples have been tested, without any prior treatments, with differential pulse voltammetry technique. The subsequent chemometric analysis has been carried out both separately on the signals of each sensor, and on the signals of two or even three sensors as a unique set of data, in order to check the possible complementarity of the information brought by the different electrodes. After a preliminary inspection by principal component analysis, classification models have been built and validated by partial least squares-discriminant analysis. The discriminant capability has been evaluated in terms of sensitivity and specificity of classification; in all cases quite good results have been obtained.

Multicomponent analysis of electrochemical signals in the wavelet domain

Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN). Despite the good performance of these methods, it is nowadays accepted that they can benefit from data transformations aiming at removing baseline effects, reducing noise and compressing the data. In this context the wavelet transform seems a very promising tool. Here, we propose a methodology, based on the fast wavelet transform, for feature selection prior to calibration. As a benchmark, a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses has been used. Three regression techniques are compared: MLR, PLS and ANN. Good predictive and effective models are obtained. Through inspection of the reconstructed signals, identification and interpretation of significant regions in the voltammograms are possible.

Classification of red wines by chemometric analysis of voltammetric signals from PEDOT-modified electrodes

Nine different types of Italian red wines of four different varieties were analysed, without any sample pre-treatments, by voltammetric techniques using a poly(3,4-ethylenedioxythiophene)-modified electrode. The data matrices consisting of the currents measured at different potentials, by repeated Cyclic Voltammetry or Differential Pulse Voltammetry, are submitted to chemometric analysis. After explorative tests based on Principal Component Analysis, Partial Least Squares-Discriminant Analysis classification models are built both for the training and for the test sets. To this aim, different classification strategies are adopted, considering the responses from the two techniques either separately or joined together to form a data matrix including the whole voltammetric information.

Near Infrared Spectroscopy and multivariate analysis methods for monitoring flour performance in an industrial bread-making process

The present study is aimed at evaluating the possibility to predict bread specifications, for an industrial bread-making process, on the basis of the properties of flour employed in production. The flour delivered at the production plant, of which rheological and chemical properties were available, were analysed by means of Near Infrared Spectroscopy (NIRS). Based on the flour properties and NIR signals, multivariate control charts were constructed in order to detect flour batches leading to a bread with non-optimal behaviour. The results show that it is possible to distinguish flour batches leading to a product with a particularly negative performance, by modelling the properties commonly measured on flours and the acquired Near Infrared signals. In spite of the absence of monitoring of process variables, which could have offered a more sound basis for the interpretation, especially when false positives and negatives are detected, these results are of particular interest from the point of view of raw material evaluation in process monitoring. Also, the potentiality of Near Infrared Spectroscopy allows considering this approach for an on-line implementation in the control of incoming raw materials in this industrial process.

Density and volumetric properties of ethane-1,2-diol + di-ethylen-glycol mixtures at different temperatures

Abstract The density of the ethane-1,2-diol (ED)+di-ethylen-glycol (DEG) binary mixtures has been measured at different temperatures over the complete composition range. The experimental measurements have been used to check the validity of relationships accounting for the dependence of the density on temperature and composition, useful to obtain interpolated values in the correspondence of the experimental data gaps. Starting from the primary data, some derived quantities, such as partial molar volumes, excess and partial excess molar volumes, have been obtained. In these mixtures, VE presents an S-shaped dependence on composition at each temperature, showing negative values in the ED rich-region and positive values at the opposite extreme. The results are compared and discussed to get light to the changes in molecular association and structural effects in this solvent system.

Prediction of compositional and sensory characteristics using RGB digital images and multivariate calibration techniques.

In the present paper, the possibility to use the information contained in RGB digital images to gain a fast and inexpensive quantification of colour-related properties of food is explored. To this aim, we present an approach which consists, as first step, in condensing the colour related information contained in RGB digital images of the analysed samples in one-dimensional signals, named colourgrams. These signals are then used as descriptor variables in multivariate calibration models. The feasibility of this approach has been tested using as a benchmark a series of samples of pesto sauce, whose RGB images have been used to predict both visual attributes defined by a panel test and the content of various pigments (chlorophylls a and b, pheophytins a and b, β-carotene and lutein). The possibility to predict correctly the values of some of the studied parameters suggests the feasibility of this approach for fast monitoring of the main aspect-related properties of a food matrix. The values of the squared correlation coefficient computed in prediction on a test set (R(Pred)(2)) for green and yellow hues were greater than 0.75, while R(Pred)(2) values greater than 0.85 were obtained for the prediction of total chlorophylls content and of chlorophylls/pheophytins ratio. The great flexibility of this blind analysis method for the quantitative evaluation of colour related features of matrices with an inhomogeneous aspect suggests that it is possible to implement automated, objective, and transferable systems for fast monitoring of raw materials, different stages of the manufacture and end products, not necessarily for the food industry only.