Chemometric analysis of gas chromatographic data-investigation of enological parameters of a bag-in-box white wine as affected by storage time and temperature

Abstract

In this study, a bag-in-box white wine was stored at 22, 35, and 45 C for up to 48 days to produce a series of samples that exhibited different enological parameters (absorbance at 420 nm, free SO 2 , total SO 2 , total phenol, and total aldehyde). Wine samples were extracted with dichloromethane and analyzed using gas chromatography (GC) to generate volatile fingerprints. Principal component analysis (PCA) score plots of the first three principal components showed grouping trends that were influenced by storage time and temperature. PCA loading plots revealed that changes in chemical profiles were different for wines held at different storage temperatures. Storage time could be predicted accurately by partial least squares (PLS) regression of the GC data. Coefficients of determination (R 2 ) were >0.99, and the standard error of prediction values were 0.4, 0.5, and 1.9 days over the test period of 15, 30, and 48 days, respectively. Using the same GC data with PLS analyses, the enological parameters could be accurately predicted from GC fingerprints, except for the predictions of SO 2 in a wine stored at 22 C and total phenol in a wine stored at 45 C.

Chemometric Characterization of Alembic and Industrial Sugar Cane Spirits from Cape Verde and Ceará, Brazil

“…Thus, the first principal component has more statistical information than the second principal component, which, in turn, has more statistical information than the third principal component, and so on. This method allows reduction of the dimensionality of the representative points of the samples [9, 11]. …”

“…The original variables receive the highest weighting, or loading, in the linear combination of the first principal components and are thus the most important from a statistical point of view. The task of the professional who works with multivariate statistics is to interpret the point distributions on the graphs of the principal components and identify the original variables with the most weight in a linear combination of the most important principal components [11, 12]. …”

Study of the Stability of Wine Samples for 1H-NMR Metabolomic Profile Analysis through Chemometrics Methods

“…In this regard, chemometrics investigation of gas chromatography (GC) data and enological parameters (absorbance at 420 nm, free SO 2 , total SO 2 , total phenol, and total aldehyde) of a bag-in-box white wine stored at different temperatures and times showed a grouping trend that was influenced by both variables. In addition, the maximum storage time could be predicted accurately by partial least squares (PLS) regression of the GC data [ 19 ]. In another study, hierarchical and non-hierarchical cluster analyses were employed to monitor the level of biogenic amines in opened bottles against time and other conditions such as different temperatures and stopper type (screw cap or cork), and use of vacuum devices by dispersive liquid–liquid microextraction–gas chromatography–mass spectrometry (ME–GC–MS) to reveal latent relationships between wine brands, the conditions for their storage, and their amine content [ 20 ].…”