Oxygen is a key factor to achieve wine optimum quality ( Ugliano, 2013). All this makes that this aldehyde accumulates with intensity, particularly in young wines, shortly after wine SO 2 is depleted. Models suggest that non-aromatic Strecker aldehydes share with acetaldehyde a strong affinity toward ARPs and that the specific pattern of phenylacetaldehyde is likely due to a much reduced reactivity toward ARPs, to the possibility that diacetyl induces Strecker degradation of phenyl alanine and to the potential higher reactivity of this amino acid to some quinones derived from catechin. The accumulation of Strecker aldehydes is directly proportional to the wine content in the amino acid precursor, being the proportionality factor much higher for aged wines, except for phenylacetaldehyde, for which the opposite pattern was observed. Models suggest that the ability of a wine to accumulate acetaldehyde is positively related to its content in combined SO 2, in epigallocatechin and to the mean degree of polymerization, and negatively to its content in Aldehyde Reactive Polyphenols (ARPs) which, attending to our models, are anthocyanins and small tannins. Results showed that young wines (3 years-old bottled wines) accumulated acetaldehyde while their content in SO 2 was not null, and the aged wine containing lowest polyphenols accumulated it throughout the whole process. Levels of volatile aldehydes and carbonyls were then determined and processed by different statistical techniques.
#FORMATION XLSTAT PLUS#
For that, eight different wines, extensively chemically characterized, were subjected at 25☌ to three different controlled O 2 exposure conditions: low (10 mg L −1) and medium or high (the stoichiometrically required amount to oxidize all wine total SO 2 plus 18 or 32 mg L −1, respectively). The main aim of the present work is to study the accumulation of acetaldehyde and Strecker aldehydes (isobutyraldehyde, 2-methylbutanal, isovaleraldehyde, methional, phenylacetaldehyde) during the oxidation of red wines, and to relate the patterns of accumulation to the wine chemical composition. 2Laboratory for Flavor Analysis and Enology, Department of Analytical Chemistry, Faculty of Sciences, Instituto Agroalimentario de Aragón, IA2, Universidad de Zaragoza-CITA, Universidad de Zaragoza, Zaragoza, Spain.1Instituto de Ciencias de la Vid y del Vino, Universidad de La Rioja-CSIC-Gobierno de La Rioja, Logroño, Spain.The modelling of data from these studies is illustrated using XLSTAT software.Mónica Bueno 1, Almudena Marrufo-Curtido 2, Vanesa Carrascón 2, Purificación Fernández-Zurbano 1, Ana Escudero 2 and Vicente Ferreira 2 *
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This workshop explains the different contexts where each technique is useful, considers aspects of design including sample size, consumer presentation issues and constraints.
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Maxdiff (also known as Best/Worst Scaling) is a technique widely offered by market research companies. There will also be a demonstration of the Qi apps for designing and analysing MAXDIFF studies, with more flexible designs and additional analyses included as part of the training.
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#FORMATION XLSTAT PDF#
Maxdiff / Best Worst scaling – Theory and PracticeĪs well as the live lecture and workshop, the package includes a pdf of the lecture notes and email support whilst you are completing the workshop after the course for up to 2 weeks.