Figure 2: Identification of methanol in red wine based on its unique Raman features different from ethanol.

Further component analysis was performed by adding methanol into the wine and measuring as before. As seen in Figure 2, most of the Raman features from pure red wine could be easily identified as ethanol. For the Raman spectrum of red wine with methanol added, a Raman peak centered around 1020 cm^-1 is apparent, and this peak can be utilized for the quantification of the methanol concentration profile in different red wine samples as seen in Figure 3. Slight variances in spectral intensity on different measuring spots were found due the heterogeneity of the bottle glass.

Figure 3: Quantification of methanol concentrations in red wine based on the intensity of one major Raman feature.

Based on these experiments, 1064 nm dispersive Raman is demonstrated as a viable new option to determine wine composition and contamination in the bottle. Chemometrics tools such as principal component analysis (PCA) and partial least squares regression (PLS) can significantly increase the accuracy and precision. It can be expected that future development of such applications may provide the wine industry a fast and non-destructive quality control and assurance tool for composition monitoring. This methodology can be easily applied to distilled spirits, beer, and others.

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