The Effect of Soundwaves on Foamability Properties and Sensory of Beers with a Machine Learning Modeling Approach

The use of ultrasounds has been implemented to increase yeast viability, de-foaming, and cavitation in foods and beverages. However, the application of low frequency audible sound to decrease bubble size and improve foamability has not been explored. In this study, three treatments using India Pale Ale beers were tested, which include (1) a control, (2) the application of audible sound during fermentation, and (3) the application of audible sound during natural carbonation. Five different audible frequencies (20 Hz, 30 Hz, 45 Hz, 55 Hz, and 75 Hz) were applied daily for one minute each (starting from the lowest frequency) during fermentation (11 days, treatment 2) and carbonation (22 days, treatment 3). Samples were measured in triplicates using the RoboBEER to assess color and foam-related parameters. A trained panel (n = 10) evaluated the intensity of sensory descriptors. Results showed that samples with sonication treatment had significant differences in the number of small bubbles, alcohol, and viscosity compared to the control. Furthermore, except for foam texture, foam height, and viscosity, there were non-significant differences in the intensity of any sensory descriptor, according to the rating from the trained sensory panel. The use of soundwaves is a potential treatment for brewing to improve beer quality by increasing the number of small bubbles and foamability without disrupting yeast or modifying the aroma and flavor profile.

[1]  Sigfredo Fuentes,et al.  Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers , 2019, Physiology & Behavior.

[2]  Claudia Gonzalez Viejo,et al.  Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications , 2018, Food Control.

[3]  D. Torrico,et al.  Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers. , 2018, Journal of food science.

[4]  D. Torrico,et al.  Analysis of thermochromic label elements and colour transitions using sensory acceptability and eye tracking techniques , 2018 .

[5]  Claudia Gonzalez Viejo,et al.  Images and chocolate stimuli affect physiological and affective responses of consumers: A cross-cultural study , 2017 .

[6]  Sigfredo Fuentes,et al.  Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER. , 2016, Food research international.

[7]  J. Welti‐Chanes,et al.  Application of Novel Processing Methods for Greater Retention of Functional Compounds in Fruit-Based Beverages , 2016 .

[8]  W. Kim,et al.  Effect of ultrasonication on fermentation kinetics of beer using six-row barley cultivated in Korea , 2015 .

[9]  J. Gallego‐Juárez,et al.  Ultrasonic defoaming and debubbling in food processing and other applications , 2015 .

[10]  Baoming Li,et al.  Advances in Effects of Sound Waves on Plants , 2014 .

[11]  N. Abdullah,et al.  Application of thermosonication treatment in processing and production of high quality and safe-to-drink fruit juices , 2014 .

[12]  Da‐Wen Sun,et al.  Cell viability and proteins release during ultrasound-assisted yeast lysis of light lees in model wine. , 2013, Food chemistry.

[13]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[14]  D. P. D. Sousa Application of ultrasounds for transformation processes of agroalimentary products , 2012 .

[15]  J. Piggott Alcoholic beverages : sensory evaluation and consumer research , 2012 .

[16]  S. Villas-Bôas,et al.  Sonic vibration affects the metabolism of yeast cells growing in liquid culture: a metabolomic study , 2011, Metabolomics.

[17]  M. D. Fumi,et al.  How Foam Appearance Influences the Italian Consumer's Beer Perception and Preference , 2011 .

[18]  J. Dayou,et al.  Experimental Investigation on the Effects of Audible Sound to the Growth of Escherichia coli , 2009 .

[19]  Arthur F. A. Teixeira,et al.  Food Physics: Physical Properties - Measurement and Applications , 2007 .

[20]  F. Sánchez Iniciación a la física , 2004 .

[21]  C. Bamforth,et al.  The path analysis method of eliminating preferred stimuli (PAMEPS) as a means to determine foam preferences for lagers in European judges based upon image assessment , 2003 .

[22]  A. Gulya,et al.  Glasscock-Shambaugh surgery of the ear , 2002 .

[23]  Antonio Delgado,et al.  Ultrasonic Velocity – A Noninvasive Method for the Determination of Density during Beer Fermentation , 2001 .

[24]  F. López,et al.  Membrane fouling by turbidity constituents of beer and wine: characterization and prevention by means of infrasonic pulsing , 2001 .

[25]  J. Mcmurry,et al.  GENERAL CHEMISTRY:ATOMS FIRST , 2000 .

[26]  C. Bamforth Perceptions of Beer Foam , 2000 .

[27]  J. Packer Quality in perspective. , 1998, Australian nursing journal.

[28]  K. Suslick,et al.  The Temperature of Cavitation , 1991, Science.

[29]  Jan A. Delcour,et al.  Principles of cereal science and technology , 1986 .

[30]  J. Effenberger Acoustical properties of , 1982 .

[31]  R. Bard,et al.  Real Time Opthalmic Ultrasonography , 1978, Springer New York.

[32]  J. Hoggan Ultrasonic hop extraction , 1968 .