When Machine Tastes Coffee:  Instrumental Approach To Predict the Sensory Profile of Espresso Coffee

Abstract

A robust and reproducible model was developed to predict the sensory profile of espresso coffee from instrumental headspace data. The model is derived from 11 different espresso coffees and validated using 8 additional espressos. The input of the model consists of (i) sensory profiles from a trained panel and (ii) on-line protontransfer reaction mass spectrometry (PTR-MS) data. The experimental PTR-MS conditions were designed to simulate those for the sensory evaluation. Sixteen characteristic ion traces in the headspace were quantified by PTR-MS, requiring only 2 min of headspace measurement per espresso. The correlation is based on a knowledge-based standardization and normalization of both datasets that selectively extracts differences in the quality of samples, while reducing the impact of variations on the overall intensity of coffees. This work represents a significant progress in terms of correlation of sensory with instrumental results exemplified on coffee.The perception elicited from drinking a freshly prepared espresso coffee represents a complex scientific phenomenon. 1,2 This multisensory experience involves all our senses such as olfaction, taste, texture, trigeminal, and visual sensation. Furthermore emotions and cognitive processes constructed during drinking experiences, such as interactions between senses 3 and product familiarity, 4 modulate perception. Among the various sensory modalities, the aroma (smell) and taste, often referred to as flavor, are of paramount importance to the quality of coffee. The flavor compounds in a roast and ground (R&G) coffee depend on many factors, two of which are of particular importance. First, the green coffee variety and quality with its specific composition on precursors sets the stage for the later flavor development during roasting. Second, the roasting process which unlocks the flavor potential of the green coffee beans and creates the coffee flavor so much appreciated by coffee aficionados all over the world. Changes in these two factors affect most of the flavor compounds.Trained sensory panelists are capable to characterize subtle differences between espresso coffees. The set of sensory descriptors that are employed by a coffee sensory specialist are generally adjusted to the type of coffee being evaluated and pertain either to green coffee tasting, the two commercial coffee varieties being Robusta (species Coffea canephora) and Arabica (species Coffea arabica), or to finished product tasting such a specific espresso blend from a coffee roaster.Besides sensory analysis, coffee scientists have long been searching for instrumental approaches to complement and eventually replace human sensory profiling. Yet, the prediction of sensory profiles on the basis of instrumental data (e.g., PTR-MS) has remained a challenge that still waits to be resolved. [5][6][7][8] Most attempts to relate aroma perception to analytical measurements probably failed because one was trying to establish a too direct relationship between the two datasets that are fundamentally d...

Online monitoring of coffee roasting by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS): towards a real-time process control for a consistent roast profile

“…Atmospheric pressure chemical ionization mass spectrometry (APCI-MS) [8][9][10][11][12] and proton transfer reaction mass spectrometry (PTR-MS) [13][14][15][16][17][18][19][20], both equipped with a quadrupole mass filter, have been successfully applied to the online and fast analysis of complex VOC mixtures.…”

Impact of protein, lipid and carbohydrate on the headspace delivery of volatile compounds from hydrating powders

“…Previous studies have shown that delivery of volatile compounds from a matrix to the headspace on hydration is strongly dependent on the physicochemical properties (e.g., hydrophobicity, volatility, molecular size) of the molecule of interest [40,41]. Studies on coffee have shown a tentative identification of compounds by m/z with acetaldehyde, 2,3 butanedione, pyridine, methylacetate and pyrrole predicted to be assigned to m/z of 45, 87, 80, 75 and 68, respectively [42,43]. When evaluating across the calibration curves (n = 3), 68, 75 and 80 were shown to be released to the headspace quickly and 87 and 45 were shown to be released more slowly.…”

“…When evaluating across the calibration curves (n = 3), 68, 75 and 80 were shown to be released to the headspace quickly and 87 and 45 were shown to be released more slowly. Assuming that the mass to charge ratio markers indicated by Lindinger et al [42,43] are correct, this would indicate that pyrrol, methylacetate and pyridine could be classified as ''rapid releasers'' and 2,3 butanedione and acetaldehyde as ''slow releasers.'' The TMAX ± SD for the model coffee sample is shown in Fig.…”

On‐Line Monitoring of Flavour Processes

“…The techniques are highly relevant to industry, but for obvious reasons of confidentiality, publications from flavour and food companies are relatively rare. However, the contribution of direct mass spectrometry to research at Nestle was reviewed at the 2007 PTR-MS meeting (Lindinger et al, 2007) and the Nestle team has published some other general reviews of mass spectrometry within the company (Fay et al, 2001;Lindinger et al, 2008). Although direct mass spectrometry was originally developed to measure aroma release during eating, it has found several other uses since it became readily available in the late 1990s, and some applications are described in the following sections.…”

“…A reasonable fit was achieved if an improved version of the Overbosch adaptation effect was included in the Steven's law relationship. A recent example of the second approach involves predicting the sensory quality of coffee from headspace analysis of brewed espresso samples (Lindinger et al, 2008). Here, the mean maximum release intensity was correlated with sensory attributes of coffee using a multivariate statistical approach.…”