Using Electronic Odor Sensors To Discriminate among Oak Barrel Toasting Levels

Abstract

Toasting changes both the quantity and the quality of the extractable substances in oak wood of barrels used for the aging of fine wines and spirits. Mastery and repeatability of toasting are vital in the production of quality barrels to be used for aging wines and spirits. Toasted wood components, which can be extracted by the wines or spirits during the aging process, are normally analyzed by maceration in standard alcohol solutions at concentrations adapted to the various products and can be used to control the intensity of the wood toasting. These kinds of analyses are accurate but time-consuming and need specialized laboratories. In this work, the feasibility of monitoring barrel toasting levels using an electronic nose with a metal oxide odor sensor array (MOS) was studied. The results of oak toasting level differentiation obtained via the MOS network were identical to those obtained by analyzing extractable compounds in liquid or gas phase as described in a previous paper. The results presented in this work at the laboratory scale could be used to implement a nondestructive monitoring system based on the analysis of headspace of barrels under industrial conditions.

Maturity discrimination of snake fruit (Salacca edulis Reinw.) cv. Pondoh based on volatiles analysis using an electronic nose device equipped with a sensor array and fingerprint mass spectrometry

“…This is a non-destructive method using a simple device 9 and the results are acquired relatively quickly. 10,11 The electronic nose is designed to specifically measure an entire aroma or odour in a way similar to humans. It will evaluate and even tolerate some ambiguity, just as a human would.…”

Quantitative (Technical) Wine Assessment

“…For example, it is being investigated as a tool in discriminating among oak-barrel toasting levels (Chatonnet and Dubourdieu, 1999), monitoring aroma production during fermentation , and detecting cork and wine contamination with 2,4,6-TCA. In addition, it has shown some success in differentiating among wines from a single winery (García et al, 2006), or wine from the same cultivar but from different regions (Buratti et al, 2004).…”

Signal Processing Architectures for Chemical Sensing Microsystems

“…Separate discriminant function analyses directed at smaller sets of analytes are successful in distinguishing between spoiled and unspoiled milk samples, between the two types of yeasts, and between two levels of spoilage. Despite its limitations in discriminating among large numbers of analytes and in interpreting mixtures, discriminant function analysis has been successfully applied to such problems as the discrimination of oak barrel toasting levels [36] and the differentiation of carbon dioxide from forane R134A [37] using arrays of metal oxide sensors, as well as evaluating the quality of oranges with shear mode quartz resonator arrays [38]. In many cases, discriminant function analysis, like principal component and cluster analysis, has been applied as a precursor to determining the suitability of a more complex, nonlinear pattern recognition technique such as a neural network.…”