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

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  • Published: Dec 12, 2011
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Chemometric analysis of gas chromatographic data - investigation of enological parameters of a bag-in-box white wine as affected by storage time and temperature

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

Journal of Chemometrics 201125, 610-619
Yucheng Fu, Loong-Tak Lim and Yukio Kakuda

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 SO2, total SO2, 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 (R2) 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 SO2 in a wine stored at 22 °C and total phenol in a wine stored at 45 °C.

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Score plots from the principal component analysis of gas chromatography data for wine extracts 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 regression of the GC data, and, in general, the enological parameters could be predicted accurately from GC fingerprints.

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