Giorgia Foca

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.

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.

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.

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.

Density and Volume Properties of the 2-Chloroethanol + 2-Methoxyethanol + 1,2-Dimethoxyethane Ternary Solvent System at Different Temperatures

AbstractThe density of the 2-chloroethanol (CE) + 2-methoxyethanol (ME) + 1,2-dimethoxye- thane (DME) ternary mixtures has been measured at different temperatures ranging from −10 to 80°C, and over the entire composition range. The experimental data have been used to check the validity of some relationships accounting for the dependence of the density on temperature and composition domains. Starting from the primary data, some derived quantities, such as excess molar volumes VE, partial molar volumes

Minimisation of instrumental noise in the acquisition of FT-NIR spectra of bread wheat using experimental design and signal processing techniques

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