1,720,997 research outputs found
Distribuzione del deperimento nel Parco Regionale della valle del Lambro negli anni 2005-2006
No full textOn the Application of Chemometrics for the study of Acoustic-Mechanical properties of Crispy Bakery products
No full textCrispness is a salient textural property for most fresh and dry food products; its loss, due to the adsorption of moisture is a major cause of rejection by consumer. The food structure and its mechanical properties are related to crispness by their capability to generate an appropriate sound and their ability to dampen or amplify this sound. The perception of crispness is a simultaneous response to mechanical and acoustic stimuli. By simulating these stimuli by means of a new acoustic-mechanical combined technique, useful information on structure of crispy product can be obtained; but new methods to interpret this information content are required, in order to integrate traditional techniques.
Even if multivariate analysis has never been applied on combined acoustic-mechanical measurements, it appears ideal for studying this kind of data. Consequently, acoustic-mechanical properties of sliced toasted breads were analysed by means of chemometric tools, such as Principal Component Analysis and Discriminant Analysis. Final results proved that Chemometrics is able to extract relevant information and offer an easy approach for the interpretation of acoustic-mechanical parameters
Tecniche innovative combinate “naso elettronico” e “lingua elettronica” per la predizione di descrittori sensoriali di vini rossi secchi mediante l’uso degli Algoritmi Genetici
No full textA commercial electronic nose and an home
made electronic tongue were used, together
with spectrophotometric determination of
phenolic compounds and color, in order to
predict the sensorial descriptors and the overall
quality of Italian red dry wines of different
denominations of origin. An expert wine tester
having an O.N.A.V. certificate, selected the
wines on the basis of their well-known sensorial
characteristics, e.g. astringency, bitterness,
aroma, color, body, etc. The analytical data
were used to build predictive models of the
sensorial descriptors by means of the Genetic
Algorithms, proposed as an alternative to the
mostly used PLS analysis and employed to select
subsets of variables that maximize the predictive
power of regression models. On the selected
models accurate validation techniques such as
Leave-one-out and Bootstrap were applied. The
present work demonstrate the possibility of
using innovative devices as the electronic nose
and the electronic tongue to obtain the sensorial
properties of wines. Moreover, the genetic
algorithms could represent a rational operative
procedure for building regression models with
real predictive capability
Geographical characterization of wine and olive oil by means of CAIMAN (Classification and Influence Matrix Analysis)
No full textClassification and influence matrix analysis (CAIMAN) is a new classification method, recently proposed and based on the influence matrix
(also called leverage matrix). Depending on the purposes of the classification analysis, CAIMAN can be used in three outlines: (1) D-CAIMAN
is a discriminant classification method, (2) M-CAIMAN is a class modelling method allowing a sample to be classified, not classified at all, or
assigned to more than one class (confused) and (3) A-CAIMAN deals with the asymmetric case, where only a reference class needs to be modelled.
In this work, the geographic classification of samples of wine and olive oil has been carried out by means of CAIMAN and its results compared
with discriminant analysis, by focusing great attention on the model predictive capabilities. The geographic characterization has been carried out
on three different datasets: extra virgin olive oils produced in a small area, with a “protected denomination of origin” label, wines with different
denominations of origin, but produced in enclosed geographical areas, and olive oils belonging to different production areas.
Final results seem to indicate that the application of CAIMAN to the geographical origin identification offers several advantages: first, it shows
– on an average basis – good performances; second, it is able to deal in a simple way classification problems related to tipicity, authenticity, and
uniqueness characterization, which are of increasing interest in food quality issues
A chemometric approach based on a novel similarity/diversity measure for the characterisation and selection of electronic nose sensors
No full textElectronic nose sensor signals provide a digital fingerprint of the product in analysis, which can be subsequently investigated by means of chemometrics. In this paper, the fingerprint characterisation of electronic nose data has been studied by means of a novel chemometric approach based on the partial ordering technique and the Hasse matrix. This matrix can be associated to each data sequence and the similarity between two sequences can be evaluated with the definition of a distance between the corresponding Hasse matrices. Since all the signals achieved along time are intrinsically ordered, the data provided by electronic nose can be also considered as sequential data and consequently characterized by means of the proposed approach. The similarity/diversity measure has been here applied in order to characterize the class discrimination capability of each electronic nose sensor: extra virgin olive oil samples of different geographical origin have been considered and Hasse distances have been used to select the sensors which appear more able to discriminate the olive oil origins. The distance based on the Hasse matrix has showed some useful properties and proved to be able to link each electronic nose time profile to a meaningful mathematical term (the Hasse matrix), which can be consequently studied by multivariate analysis. (c) 2006 Elsevier B.V. All rights reserved
Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithms regression models
No full textIn the present work, innovative analytical techniques, such as an amperometric electronic tongue and a commercial electronic nose were used, together with spectrophotometric methods, to predict sensorial descriptors of Italian red dry wines of different denominations
of origin. Genetic Algorithms were employed to select variables and build predictive regression models. On the selected models, an accurate validation technique (the Bootstrap procedure) and a procedure for the detection of outliers (Williams plot) were applied.
The results obtained demonstrate the possibility of using these innovative techniques in order to describe and predict a large part of the selected sensorial information. It was not possible to build an acceptable regression model for only one descriptor, sourness.
The proposed analytical methods have the advantage of being rapid and objective; furthermore, the statistical methods applied could be considered a rational operative procedure for building regression models with real predictive capability
Classification of GC-MS measurements of wines by combining a novel strategy for data dimension reduction and variable selection techniques
No full textMonitoring of Alcoholic Fermentation Using NIR and MIR Spectroscopy combined with Electronic Nose and Electronic Tongue
No full textThe rapid pace of change in the wine industry calls for fast methods providing real time information in order to assure the quality of the final product. NIR and MIR spectroscopy combined with sensory-instrumental methods (electronic nose and electronic tongue) can provide an ideal
solution to monitor molecular and sensory changes in wine during alcoholic fermentation. The objective of this work was to investigate the potential of NIR and MIR spectroscopy, electronic nose and electronic tongue associated with chemometric data analysis, to monitor time-related changes
that occur during red wine fermentation. Twenty-three micro-fermentation trials were conducted during the 2008-2009 vintages in Valtellina viticultural area (Northern Italy). During fermentation, at each sampling time, spectra were collected by FT-NIR and FT-IR spectrometers and samples were analysed by electronic nose and electronic tongue. Chemical analyses were performed to evaluate sugar, phenolic compounds, ethanol and glycerol concentrations. Various multivariate statistical methods were applied in order to obtain regression and classification models. Results showed that FT-NIR and FT-IR spectroscopy can provide good regression PLS models to predict the main chemical parameters involved in alcoholic fermentation. Classification techniques (LDA, QDA, genetic algorithms) were applied to electronic nose, electronic tongue and spectral data, obtaining sample classification based on fermentation stages
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