Monforte Ana Rita, Jacobson Dan, Silva Ferreira A C
CBQF - Centro de Biotecnologia e Química Fina - Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa/Porto , Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal.
J Agric Food Chem. 2015 Mar 11;63(9):2576-81. doi: 10.1021/jf5055084. Epub 2015 Feb 25.
Network reconstruction (NR) has proven to be useful in the detection and visualization of relationships among the compounds present in a Port wine aging data set. This view of the data provides a considerable amount of information with which to understand the kinetic contexts of the molecules represented by peaks in each chromatogram. The aim of this study was to use NR together with the determination of kinetic parameters to extract more information about the mechanisms involved in Port wine aging. The volatile compounds present in samples of Port wines spanning 128 years in age were measured with the use of GC-MS. After chromatogram alignment, a peak matrix was created, and all peak vectors were compared to one another to determine their Pearson correlations over time. A correlation network was created and filtered on the basis of the resulting correlation values. Some nodes in the network were further studied in experiments on Port wines stored under different conditions of oxygen and temperature in order to determine their kinetic parameters. The resulting network can be divided into three main branches. The first branch is related to compounds that do not directly correlate to age, the second branch contains compounds affected by temperature, and the third branch contains compounds associated with oxygen. Compounds clustered in the same branch of the network have similar expression patterns over time as well as the same kinetic order, thus are likely to be dependent on the same technological parameters. Network construction and visualization provides more information with which to understand the probable kinetic contexts of the molecules represented by peaks in each chromatogram. The approach described here is a powerful tool for the study of mechanisms and kinetics in complex systems and should aid in the understanding and monitoring of wine quality.
网络重构（NR）已被证明在检测和可视化波特酒陈酿数据集中存在的化合物之间的关系方面很有用。这种数据视图提供了大量信息，可用于理解每个色谱图中峰所代表的分子的动力学背景。本研究的目的是将网络重构与动力学参数的测定结合起来，以提取更多关于波特酒陈酿所涉及机制的信息。使用气相色谱 - 质谱联用仪（GC - MS）对跨越128年的波特酒样品中的挥发性化合物进行了测量。在色谱图对齐后，创建了一个峰矩阵，并将所有峰向量相互比较，以确定它们随时间的皮尔逊相关性。根据所得的相关值创建并过滤了一个相关网络。对网络中的一些节点在不同氧气和温度条件下储存的波特酒实验中进行了进一步研究，以确定它们的动力学参数。所得网络可分为三个主要分支。第一个分支与与年龄无直接相关性的化合物有关，第二个分支包含受温度影响的化合物，第三个分支包含与氧气相关的化合物。聚集在网络同一分支中的化合物随时间具有相似的表达模式以及相同的动力学顺序，因此可能依赖于相同的工艺参数。网络构建和可视化提供了更多信息，有助于理解每个色谱图中峰所代表的分子可能的动力学背景。这里描述的方法是研究复杂系统中机制和动力学的有力工具，应该有助于葡萄酒质量的理解和监测。
