Characterization and classification of the aroma of beer samples by means of an MS e-nose and chemometric tools

Abstract

An electronic nose based on coupling of headspace (HS) with a mass spectrometer (MS) has been used in this study to classify and characterize a series of beers according to their production site and chemical composition. With this objective, we analyzed 67 beers of the same brand and preparation process but produced in different factories. The samples were also subjected to sensory evaluation by a panel of experts. Linear discriminant analysis (LDA) was used as the classification technique and stepwise LDA based on Wilk's lambda criterion was used to select the most discriminating variables. To interpret the aroma characteristics of the beers from the m/z ions obtained, score and loading bi-plots were obtained by applying canonical variables. Because the beers analyzed were marketed with the same name and brand, we expected to be working with the same product irrespective of its origin. However, results from both sensory evaluation and use of the e-nose revealed differences between factories. With the e-nose it was possible to relate these differences to the presence (and abundance) of characteristic ions of different compounds typically found in beer. These results demonstrate that the HS-MS e-nose is not only an aroma sensor capable to classify and/or differentiate samples but it can also provide information about the compounds responsible for this differentiation.

Correlation between volatile profiles of Italian fermented sausages and their size and starter culture

“…To use LDA it is necessary that the number of samples is less than the number of variables (Vera et al, 2011). In this case, LDA was applied, choosing as classification class the size of sausages according to Table 1.…”

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

“…In mid-level fusion, a feature extraction is applied to each data source followed by merging the extracted features. For high level of abstraction, data from each source are initially analyzed, and a model is derived, separately, from each data source, being the model classification outputs then merged [26,28,36]. In this work, a low-level data fusion was adopted.…”

“…Indeed, data fusion enabled to obtain compatible measurements originated from different sources [25]. Data fusion approaches, with different abstraction levels (low, mid, or high level), were effectively applied to differentiate or classify different food matrices or their attributes, namely beers [25,26], snack foods [27], fruit juices [28], and black teas [29].…”

Recognition of the geographical origin of beer based on support vector machines applied to chemical descriptors

“…Beer, one of the oldest known alcoholic beverages, is produced by the yeast fermentation of cereals germinated in water and flavoured and stabilized with hops, this being the ingredient responsible for the bitter taste of this beverage (Polshin et al, 2010;Vera et al, 2011). Brewing is a complex process demanding the control of many parameters to ensure reproducibility of the quality of the finished product: a very complex mixture of constituents varying widely in nature and concentration levels (Da Silva, Augusto, & Poppi, 2008).…”