Using Two-Dimensional Correlation Spectroscopy To Screen For Smoke Taint in Wines: A Novel Approach

Smoke taint is a significant threat to wine quality, and is relatively common in places that are more prone to wildfires (specifically, Australia and parts of California). Smoke taint in wine is created when grapevines are exposed to a significant amount of smoke during the sensitive maturation process of the grapes, with certain stages in the process more vulnerable than others. This smoke exposure during this maturation period results in increases in particular chemicals which result in undesirable “smoky” and “ash” characteristics in the finished wine. Physiologically, the volatile compounds in the smoke are absorbed by the leaves of the grapevines and then bind to sugars within the

Photo source: By Andrea Booher (This image is from the FEMA Photo Library.) [Public domain], via Wikimedia Commons

Photo source: By Andrea Booher (This image is from the FEMA Photo Library.) [Public domain], via Wikimedia Commons

plant. Once bound with sugar, these glycolated volatiles move throughout the grapevine via the resource transport system (i.e. the xylem), which ultimately accumulates in the grape berries.

There are several techniques used to measure smoke taint in wines, with some of the first techniques being time and resource-intensive HPLC and other chromatography methods. The use of spectroscopy is becoming more popular in terms of developing new and faster techniques for assessing smoke taint in wine, which could not only save time for the winemaker, but also a lot of money in the long run. 2-dimensional correlation spectroscopy (2D-COS) is one of the newer techniques being analyzed for efficiency in assessing smoke taint in wines, and basically works by determining the structural make-up of the chemicals in the samples, with any changes from “normal” being represented in a 2D spectral contour map. So far, very few studies have looked at 2D-correlation spectroscopy in the analysis of wine.

The short study presented today aimed to determine if 2D-COS could be used as a quick tool for determining smoke taint in wines, by looking for the structural signatures of know smoke taint-derived compounds in the contour maps.

Methods

59 samples of wine were utilized from a previous study examining MIR spectroscopy analysis of smoke tainted wines. The different treatments included: 1) experimental wines made from grapes exposure to smoke in the field; 2) commercial wines that were exposed to smoke during the growing season; 3) red wines made in oak barrels with no smoke exposure; 4) white wines made in oak barrels with no smoke exposure; and 5) red and white wines make in oak barrels with an addition of 30mg/L of guaiacol (a compound found in oak-aged wines as well as significantly increased in wines that were exposed to smoke during the growing season).

Spectroscopy analysis included MIR spectroscopy of all samples, as well as correlation with the 2D-COS contour map spectra.

Results

• Analysis showed there are more differences between red and white wines than simply the guaiacol content (duh?).
o White wines showed the lowest levels of guaiacol.
• MIR spectroscopy showed two distinct groups of wine samples: white wines with low guaiacol levels, and red wines with higher guaiacol levels.
• 2D-COS analysis showed structural changes of the compounds in wines with smoke treatment, specifically guaiacol and other smoke-derived volatile compounds.
• Determining smoke taint in red wines was more complex than with white wines, since red wines in oak barrels have higher guaiacol levels already without smoke exposure compared with white wines, so comparing red wines exposed and not exposed to smoke may require further analytical method analysis.

Conclusions

The results of this study indicate that 2D-Correlation Spectroscopy may be a good quick and dirty way to separate possible smoke-tainted wines from definitely not smoke tainted wines when time and money is a factor. Rather than sending in every single sample to a commercial laboratory, using 2D-COS in the wine cellar to reduce the number of samples needed for further smoke taint

Photo By Jynto [CC0], via Wikimedia Commons

Photo By Jynto [CC0], via Wikimedia Commons

analysis will save time and money for all involved. Say you have 4 out of 10 wines that end up being tainted with smoke: rather than send all 10 wines for expensive HPLC or other time-consuming analysis, the winemaker can do a quick screening in the cellar to tease out those wines that may be tainted with smoke based on their 2D-COS spectral profile, then only send that small subset off for further analysis (in this made-up example, the winemaker would have found 4 to send instead of the entire 10).

According to the authors, 2D-COS does not all one to quantify the levels of smoke taint in wine, but acts more like a screening technique to quickly determine if the sample is potentially tainted or not. This c0uld provide a time and money-saving technique for winemakers, as the method will allow them to save money by not performing more complicated and expensive analyses on wines that aren’t even affected by the smoke taint. Of course, theoretically, if every single one of their wines in affected by the smoke taint, then I suppose in the long run more money would be spent overall by performing the 2D-COS analysis followed by a more throughout quantitative analysis at a commercial laboratory, however, on average, this technique could help reduce the number of samples needed to send out for analysis in the event not every single wine was affected by smoke exposure during the growing season.

How about you all? Have you tried this type of quick analysis for yours or something else’s wines? Would you use this technique as a quick screening method for smoke taint prior to sending samples off to be further analyzed? Please feel free to comment!

Source: Fudge, A.L., Wilkinson, K.L., Ristic, R., and Cozzolino, D. 2013. Synchronous two-dimensional MIR correlation spectroscopy (2D-COS) as a novel method for screening smoke tainted wine. Food Chemistry 139: 115-119.

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