The prospect of producing wine in North Carolina has created a small but rapidly growing wine industry in the state. There are currently 48 North Carolina wineries with 7 more scheduled to open by the end of 2005. One of the primary beneficiaries of this growth is the North Carolina farmer who has found in grapes a high value substitute crop for tobacco. The state wine industry today exists primarily due to tourism. Most North Carolina wines are sold straight from the wineries to tourists interested in a wine experience that goes beyond the taste and appearance of the wine. However, if the North Carolina wine industry is to continue to grow and become a world recognized wine producing region, the ability to produce high quality wines is critically important.

While there are numerous general guidelines for producing a quality wine, the specific chemical components that contribute to wine quality are in general poorly quantified. This is in large part due to the enormous diversity of chemical compounds found in wine. Picking out the components that are responsible for a particular wine receiving a high rating seems a daunting (if not impossible) task. However, developments over the past 15 years in the field of chemometrics have facilitated the analysis of complex mixtures using standard spectroscopic techniques. Spectroscopic analysis of wine yields data far too complicated to interpret visually, but chemometric algorithms such as principle component analysis and partial least squares regression are capable of producing qualitative as well as quantitative information from such data.

We propose to use chemometrics coupled with nuclear magnetic resonance spectroscopy (NMR) to analyze a variety a wine samples and attempt to correlate spectral characteristics to wine quality. Our approach will involve collecting a number of wine samples that are from similar regions, made from the same type of grape, produced the same year etc. but differ in their quality (as determined by Wine Spectator and Robert Parker rating). All wine samples will be analyzed via NMR spectroscopy and the combined data will be processed using a commercially available chemometrics software package. Areas of the NMR spectrum that contribute to high or low rating will then be further analyzed in hopes of identifying the specific chemical compounds responsible.

The production of ethanol from renewable resources (corn, wheat, cellulosic biomass etc.) is of great interest to countries all around the world. This is especially true in the US where the availability of feedstocks for fuel ethanol production could reduce dependence on foreign oil. Ethanol is also a more ecologically sound fuel than gasoline being both renewable and cleaner burning. However, if ethanol is to be economically competitive with gasoline, then its production process must be highly efficient.

Efficiency improvements in the production of fuel ethanol are largely dependent upon increasing the rate or yield of the ethanol production process. Ultimately, both of these are limited by the microorganism being used to make the ethanol. Today, ethanol is produced by fermentation of glucose (typically derived from corn) by yeast. Ethanol is produced as a byproduct of the glycolytic pathway which the yeast uses to supply energy for growth. Ultimately, ethanol production by yeast slows down and stops due to the toxic effect ethanol. Also a portion of the carbon source (glucose) is utilized by the yeast for biosynthesis instead of conversion to ethanol. These and other factors put limits on the efficiency of ethanol production by yeast. While strain improvements are possible, the constraints of maintaining a viable organism will eventually limit success in this area.

These limitations may be avoided by getting rid of the microorganism altogether and using only the enzymes involved in the ethanol production pathway. This ‘cell free’ approach to ethanol production has a number of advantages over the microbial process including greater process flexibility (i.e. ability to operate at higher temperature, or higher ethanol concentration), more freedom to manipulate enzymes (i.e. removing feedback inhibition mechanisms), and the ability to easily optimize the production process by altering enzyme levels.

Preliminary investigations have been conducted by employing a mathematical model of the twelve enzymatic reactions involved in the production of ethanol from glucose. While this study is still ongoing, initial results have indicated that it is possible to substantially increase the rate of ethanol production relative to the conventional microorganism based process by changing the enzyme levels. Current work is focusing on optimizing enzyme levels to allow increased ethanol production with the minimum amount of enzyme and evaluation of the economic feasibility of this approach. Upon completion of this, we hope to demonstrate this concept in the laboratory.