A new graphical interface as a tool to integrate data from GC-MS and UPLC-MS-QTOF: new compounds related with port wine aging

Abstract

Port wine value is related to its molecular profile resulting from the changes occurring during the ageing period. It is of empirical knowledge that the style is greatly affected by the oxidation regimens, i.e. bottle versus barrel storage. The final quality is rated based on sensory evaluation and the correspondent chemical profile remains largely unknown. This lack of knowledge and understanding significantly limits the ability to improve/drive Port Wine quality and consistency. 

Unravelling the chemical changes, occurring during ageing, that are responsible for the wine flavour, constitutes a critical task when one attempts to address issues related to authenticity and sensory quality. 

It has been demonstrated that some key odorants play a critical role in the perceived oxidized character of wines (1, 2). Nevertheless, the mechanisms for these key odorants formation are not fully understood; only that temperature and mainly oxygen have a synergistic impact on their formation (3). Recently it has been demonstrated that Strecker degradation substrates such as: phenolics, sugars and metals can interact resulting in a unpredictable formation of flavour molecules. 

In order to have an holistic view of the chemical system a pipeline was developed based on UPLS-MS-QTOF and GC-MS data acquisition followed by data fusion. The process is hyphenated with an in-house peak picking interface, coupled with multi- and -univariate statistics to get the most relevant compounds related in this case with Ports stored from 1 to 150 years old. 

In this work the “omics” interface was validated with a set of 37 wines; 42 biomarkers were extracted from GC-MS and 152 from UPLC-MS-QTOF. 

The development of tools such as network reconstruction provided considerable amount of information that contributed to the understanding of the kinetic contexts of the molecules (through ageing time). Clusterization of volatiles and non-volatile compounds brought further new information regarding the interaction between mechanisms and new compounds were identified, such as: SO2-phenolics reactions, phenolics-phenolics , phenolics-aldehydes, amongst other. 

This network-driven approach, integrating data from different equipment’s. has proven to be an useful tool in identifying compounds of interest related to changes occurring during food storage or ageing processes, as well as in better understanding the drivers of quality and authenticity in the final product.