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二维气相色谱-质谱联用、高效液相色谱-质谱联用与感官数据的融合,以辅助国际单一品种霞多丽和长相思葡萄酒营销中的决策制定。

Fusion of 2DGC-MS, HPLC-MS and Sensory Data to Assist Decision-Making in the Marketing of International Monovarietal Chardonnay and Sauvignon Blanc Wines.

作者信息

Poggesi Simone, Darnal Aakriti, Ceci Adriana Teresa, Longo Edoardo, Vanzo Leonardo, Mimmo Tanja, Boselli Emanuele

机构信息

Oenolab, NOI Techpark Alto Adige/Südtirol, Via A. Volta 13B, 39100 Bolzano, Italy.

Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, 39100 Bolzano, Italy.

出版信息

Foods. 2022 Oct 31;11(21):3458. doi: 10.3390/foods11213458.

Abstract

Monovarietal wines produced in different wine-growing areas may have completely different sensory profiles. As a result, they may be suitable for sale in different regions, depending on local preferences. Better insight into the sensory and chemical profiles of these wines can be helpful in further optimizing commercial strategies and matching supply and demand, which is the main challenge for global wine traders. The training of dedicated sensory panels, together with the correlation of the evaluated attributes with chemical parameters, followed by validation of the obtained models, may yield an improved picture of the overall features associated with products from a specific region. Eighteen samples of international Chardonnay and eighteen samples of international Sauvignon Blanc wines were collected from nine world origins (Northern Italy, Southern Italy, Chile, Argentina, New Zealand, Australia, and South Africa). The overall quality judgement (OQJ) and the sensory attributes were evaluated by a panel trained with a MRATA (Modified Rate-All-That-Apply) method. Moreover, volatile compounds were analysed by HS-SPME-GC × GC-ToF/MS and the phenolic composition, including proanthocyanidins, was determined using HPLC-QqQ/MS. The processing of the data using different multivariate analysis methods, such as multiple factor analysis (MFA), was essential to gain insight into the quality of the samples. The profile of cyclic and non-cyclic oligomeric proanthocyanidins was found to be substantially dependent on the grape variety used in the wines (varietal markers), despite the country of origin of the wine influencing it to a limited extent. The results from the same samples analysed by a sensory panel from Germany and ours were qualitatively compared, highlighting the presence of potential factors inherent to the panels themselves that could influence the different judgments and quality classification of the wines. Consequently, the combination of sensory and chemical analysis, by means of the application of multivariate statistical methods presented in this study proves to be a powerful tool for a deeper and more comprehensive understanding of the quality of the wines under investigation. Overall quality was described as a combination of the sensory attributes, according to the perception process. The attributes were in turn described based on the chemical profiles, which were determined independently by analytical techniques. Eventually, this approach can be very useful not only for basic research on wine quality but also as a tool to aid business-related decision-making activities of wineries and wine traders and to create models that can aid the refinement of marketing strategies.

摘要

不同葡萄酒产区生产的单一品种葡萄酒可能具有完全不同的感官特征。因此,根据当地偏好,它们可能适合在不同地区销售。更深入了解这些葡萄酒的感官和化学特征有助于进一步优化商业策略并平衡供需,这是全球葡萄酒贸易商面临的主要挑战。培训专业的感官小组,将评估的属性与化学参数相关联,然后对所得模型进行验证,可能会更清楚地了解与特定地区产品相关的整体特征。从九个世界产地(意大利北部、意大利南部、智利、阿根廷、新西兰、澳大利亚和南非)收集了18个国际霞多丽葡萄酒样品和18个国际长相思葡萄酒样品。由采用MRATA(改进的适用率法)方法培训的小组对整体质量判断(OQJ)和感官属性进行评估。此外,通过HS-SPME-GC×GC-ToF/MS分析挥发性化合物,并使用HPLC-QqQ/MS测定包括原花青素在内的酚类成分。使用不同的多元分析方法(如多因素分析(MFA))处理数据对于深入了解样品质量至关重要。尽管葡萄酒的原产国对其有一定程度的影响,但发现环状和非环状低聚原花青素的特征在很大程度上取决于葡萄酒中使用的葡萄品种(品种标记)。对来自德国的感官小组和我们的感官小组分析的相同样品的结果进行了定性比较,突出了各小组本身可能存在的潜在因素,这些因素可能会影响对葡萄酒的不同判断和质量分类。因此,通过本研究中提出的多元统计方法进行感官和化学分析相结合,被证明是一种强大的工具,可用于更深入、更全面地了解所研究葡萄酒的质量。根据感知过程,整体质量被描述为感官属性的组合。这些属性又基于化学特征进行描述,而化学特征是通过分析技术独立确定的。最终,这种方法不仅对葡萄酒质量的基础研究非常有用,而且作为一种工具,有助于酒庄和葡萄酒贸易商开展与商业相关的决策活动,并创建有助于完善营销策略的模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d0bd/9657765/cb2ffc9fd19d/foods-11-03458-g001.jpg

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