A sensory approach for the monitoring of accelerated red wine aging processes using multi-block methods
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Universidad de La Rioja
info
ISSN: 0950-3293
Año de publicación: 2013
Volumen: 28
Número: 2
Páginas: 519-530
Tipo: Artículo
beta Ver similares en nube de resultadosOtras publicaciones en: Food Quality and Preference
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Resumen
Two young red wines from D.O. Navarra (Spain) were separately subjected to accelerated aging in tanks with oak chips added and micro-oxygenation. Wine samples were collected during each process and were analyzed by a sensory panel according to 17 descriptors. A two-fold monitoring approach, aimed at both checking panel performance and tracking the impact of chips/micro-oxygenation treatments on sensory properties, was proposed for process control and optimisation. In a first stage this approach focused on investigating panel performance by suitably combining uni- and multivariate methods. Significant product effects were found for 7 and 6 attributes in the first and second aging series, respectively. Although panel performance was acceptable in both cases, moderate differences between assessors were detected, stressing the need for adopting a consensus approach to properly handle them. Thus, the STATIS method was subsequently applied to find an optimal compromise solution for the significant attributes describing each process. The resulting compromise matrix provided the consensus sensory profiles of samples at different aging stages. The assignment of individual weights was consistent with previous findings on assessors' agreement. The first principal component of the compromise matrix, which accounted for 86.5% and 90.9% of the variance in each trial data, revealed a sample trend directly related to the time-course evolution of each process, and contrasted the attributes fruit and herbaceous with the other significant descriptors (related to the wood-wine interaction). This is the first time that a multi-block methodology has been used for monitoring sensory changes during accelerated aging processes. © 2013 Elsevier Ltd.