Chromatography separates the different components of complex mixtures and generates a fingerprint representing the chemical composition of the sample. The resulting data structure depends on the characteristics of the detector used, univariate for devices such as a flame ionization detector (FID) or multivariate for mass spectroscopy (MS). This study addresses the potential use of a univariate signal for a nontargeted approach to (i) classify samples according to a given process or perturbation, (ii) evaluate the feasibility of developing a screening procedure to select candidates related to the process, and (iii) provide insight into the chemical mechanisms that are affected by the perturbation. To achieve this, it was necessary to use and develop methods for data preprocessing and visualization tools to assist an analytical chemist to view and interpret complex multidimensional data sets. Dichloromethane Port wine extracts were collected using GC-FID; the chromatograms were then aligned with correlation optimized warping (COW) and subsequently analyzed with multivariate statistics (MVA) by principal component analysis (PCA) and partial least-squares regression (PLS-R). Furthermore, wavelets were used for peak calling and alignment refinement, and the resulting matrix was used to perform kinetic network reconstruction via correlation networks and maximum spanning trees. Network-target correlation projections were used to screen for potential chromatographic regions/peaks related to aging mechanisms. Results from PLS between aligned chromatograms and target molecules showed high X to Y correlations of 0.91, 092, and 0.89 with 5-hydroxymethylfurfural (HMF) (Maillard), acetaldehyde (oxidation), and 4,5-dimethyl-(5H)-3-hydroxy-2-furanone, respectively. The context of the correlation (and therefore likely kinetic) relationships among compounds detected by GC-FID and the relationships between target compounds within different regions of the network can be clearly seen.