Instrumental measurements of beer taste attributes using an electronic tongue

1. Sixth Winter Symposium on Chemometrics Kazan 18 – 22 February 2008 _______________________________________________________ Instrumental measurements of beer taste attributes using an electronic tongue Alisa Rudnitskaya Evgeny Polshin Dmitry Kirsanov Katrien Beullens Jeroen Lammertyn Bart Nicolai Freddy Delvaux Andrey Legin

2. Purpose of the study • Brewing and aging of beer are complex processes • Several parameters have to be controlled to ensure reproducible quality of the finished product • One of the most important – taste and flavour are evaluated by the sensory panel • Similarly to all sensory analyses of any foodstuffs assessment of beer taste and flavour is slow, expensive and suffering from irreproducibility of the assessors. • The aim of the present study is evaluation of the electronic tongue sensor system as a screening tool for the beer taste and flavour attributes

3. ExperimentalSamples • Samples • 50 Belgian and Dutch beers of different types • Sensory evaluation • trained sensory panel has estimated 72 attributes in total • beer aroma • taste • mouthfeel • appearance • global quality

4. ExperimentalET measurements • Sensor array • 29 potentiometric chemical sensors of different types • multichannel custom-made voltmeter • Sample preparation • filtering • dilution • thermostatting at 270C; • 7-9 replicated measurements

5. Data processing • Data exploration: • Principal Component Analysis (PCA) • Canonical Correlation Analysis (CCA) • Prediction of sensory attributes: • Partial Least Square Regression (PLS)

6. Data setFlavour and taste attributes • 72 attributes were evaluated: • 27 aroma attributes intensity, sour, fruity, alcoholic, … • 29 taste and flavour attributes sour, sweet, bitter, hoppy, … • 9 aftertaste attributes intensity, duration, body, … • 4 mouth feel attributes astringency, CO2, warming, … • 2 foam attributes colour, texture • global quality • Sets of attributes are overlapping • Aroma and taste attributes sets include the same parameters • Taste and aftertaste attributes include the same parameters

7. Data setFlavour and taste parameters • Correlation between beer taste and aroma attributes • The same attributes related to the taste and aroma or aftertaste were highly correlated (correlation coef. 0.8-0.9) with exception of attributes fusty and metallic Considering high correlation of the same aroma and taste attributes and the fact that ET is measuring liquid only attributes pertaining to the beer taste were chosen for the further data processing, i.e. 45 in total • Correlation was also observed between following groups of attributes (correlation coef. 0.9-0.7): • aroma, taste and after taste intensity and mouthfeel, duration and body; • mouthfeel, CO2, warming and aftertaste intensity, duration and body; • taste, aftertaste and aroma sour, artificial and fruity; • taste and aroma alcoholic and mouthfeel and warming; • taste and aroma ester and solvent; • taste and aroma sulphury, DMS and rubber

8. Data setSensory panel • Problem of the sensory panel data set • Sensory panel consisted of 18 people • Not all of them were present at each tasting session • Each sample was tasted by a “sub-panel” of 7-11 assessors • None of them tasted all samples • Data sets used • 7 tasters that tasted more than half of the samples • Average values of each attribute were calculated using scores of those of the 7 tasters that assessed this particular sample, which resulted in 50x45 data matrix. • Sensory attributes data set was centered and standardized

9. Beer samples discriminationSensory panel data

10. Beer samples discriminationSensory panel data

11. Beer samples discriminationET data – comparison with sensory panel

12. Beer samples discriminationET data – comparison with sensory panel

13. Beer samples discriminationET data

14. Beer samples discriminationET data

15. Comparison of ET and sensory panel data sets using CCA • Four significant canonical roots were extracted • Correlations between first four pairs of canonical variables were: 0,96, 0,91, 0,79 and 0,77 Similarity maps

16. Prediction of the attributes using ET

17. Conclusions • Electronic Tongue multisensor system seems to be very promising tool for instrumental beer taste screening • Determination of taste attributes with ET in some cases allows to achieve lower error values than sensory panel • Sensory panel data set handling and methods of ET data processing are still to be improved

18. Acknowledgments • RFBR project 05-03-34824-МФ_а • Centre for Malting and Brewing Sciences, Leuven, Belgium • InBev Brewery Company, Belgium • Sensor Systems LLC, St Petersburg, Russia

19. THANK YOU FOR YOUR ATTENTION !

20. Data setFlavour and taste attributes • 72 attributes pertaining to the beer aroma and taste were evaluated: • 27 aroma attributes (intensity, sour, fruity, alcoholic, hoppy, floral, spicy, caramel, liquorice, worty, artificial, ester, solvent, burned, yeast, autolysis, sulphury, H2S, DMS, diacetyle, fusty, oxidation, metallic, chlorophenol, vinylguaiacol, rubber, acetaldehyde) • 29 taste and flavour attributes (intensity, sour, sweet, bitter, fruity, alcoholic, hoppy, floral, spicy, caramel, liquorice, worty, artificial, ester, solvent, burned, yeast, autolysis, sulphury, H2S, DMS, diacetyle, fusty, oxidation, metallic, chlorophenol, vinylguaiacol, rubber, acetaldehyde) • 9 aftertaste attributes (intensity, duration, bitter, sour, sweet, fruity, liquorice, artificial, body) • 4 mouth feel attributes (mouthfeel, astringency, CO2, warming) • 2 foam attributes (colour, texture) • global quality • Sets of attributes are overlapping • Aroma and taste attributes sets include the same parameters with exception of sweet and bitter that are only taste characteristics • Taste and aftertaste attributes include the same parameters (e.g. intensity, bitter, sour, sweet, fruity, liquorice, artificial)