Chemical discrimination of arabica and robusta coffees by Fourier transform Raman spectroscopy.

This article deals with the potential of Fourier transform (FT) Raman spectroscopy in discrimination of botanical species of green and roasted coffees. There are two species of commercial importance: Coffea arabica (arabica) and Coffea canephora (robusta). It is recognized that they differ in their lipid fraction, especially in the content of the diterpene kahweol, which is present at 0.1-0.3% dry matter basis in arabica beans and only in traces (<0.01%) in robusta. The visual examination of the Raman spectra of the lipid fraction extracted from arabica, robusta and liberica samples shows differences in the mid-wavenumbers region: arabica spectra have two characteristic scattering bands at 1567 and 1478 cm(-1). The spectrum of the pure kahweol shows the same bands. Principal component analysis is applied to the spectra and reveals clustering according to the coffee species. The first principal component (PC1) explains 93% of the spectral variation and corresponds to the kahweol concentration. Using the PC1 score plot, two groups of arabica can be distinguished as follows: one group with high kahweol content and another group with low kahweol content. The first group includes samples coming from Kenya and Jamaica; the second group includes samples from Australia. The main difference between these coffees is that those from Kenya and Jamaica are well-known for growing at a high altitude whereas those ones from Australia are grown at a low altitude. To our knowledge, the application of Raman spectroscopy has never been used in coffee analysis.

Monitoring large scale wine fermentations with infrared spectroscopy.

Negative effects on wine quality and productivity caused by stuck and sluggish fermentations can be reduced significantly, if such problems are detected early through periodic chemical analysis. Infrared spectroscopy (IR) has been used successfully for monitoring fermentations, since many compounds can be measured quickly from a single sample without prior treatment. Nevertheless, few applications of this technology in large scale winemaking have been reported, and these do not cover the entire fermentation from must to finished wine. In this work, we developed IR calibrations for analyzing the fermenting must at any stage of fermentation. The calibration model was obtained with multivariable partial least squares and proved effective for analyzing Cabernet Sauvignon fermentations for glucose, fructose, glycerol, ethanol, and the organic acids; malic, tartaric, succinic, lactic, acetic, and citric. Upon external validation we found an average relative predictive error of 4.8%. Malic acid showed the largest relative predictive error (8.7%). In addition, external validation found that insufficient data for these calibrations made the analysis of fermenting musts using other grape varieties less reliable.