A Low-Cost Image Analysis System to Upgrade the Rudin Beer Foam Head Retention Meter

Abstract

Beer foam is one of the first characteristics consumers visually perceive. Even if their opinions vary, generally, beer foam should be stable and long lasting. In this work, the conventional foam head retention method developed by Rudin (1957) was improved by resorting to three different automatic data acquisition systems. The first one made use of a mouse time tracking software offered at no cost. The second one was an Accurate Image Analysis system using a high-definition camera and a script written in MATLAB. Finally, the third one was an automatic low-cost image analysis (LCIA) system incorporating a Raspberry Pi single-board computer and a camera module (Raspberry Pi Foundation, UK). The latter was able not only to acquire the whole foam decay curve but also to fit the time course of the beer-foam interface position and extract the characteristic beer foam halflife (t ½ ). All the data acquisition systems yielded t ½ values practically coincident at the probability level of 0.05, this confirming their substantial equivalence. Once the lager beer had been laced with increasing doses of a foam agent (i.e., tetrahydro-iso-α-acid) to enhance its foam persistence, the resulting foam half-life, as estimated using the above Rudinbased methods, was highly correlated (r 2 = 0.99) to the foam collapse time, as derived from the NIBEM Foam Stability Tester. Finally, thanks to the LCIA acquisition system developed here, the Rudin test might represent a fast, flexible, and cheaper alternative to the generally recommended but expensive NIBEM tester, as well as other commercial automated Rudin apparatuses.

Bubbles, Foam Formation, Stability and Consumer Perception of Carbonated Drinks: A Review of Current, New and Emerging Technologies for Rapid Assessment and Control

“…The methods belonging to category (i) are the National Institute for Malting Barley, Malt and Beer (NIBEM) (NIBEM-T; Haffmans BV, Venlo, Holland) [59,60], foam flashing [59], Rudin [61], Steinfurth foam stability (Steinfurth, Inc., Marietta, GA, USA) [62], shake test [63], Carlsberg automated analysis [64], Blom [51], foam-lacing [65] and the low-cost image analysis system [66]. These methods induce foam formation by applying pressure using CO 2 [11,66]. However, since this is not the natural foam formation process, these methods measure the capacity of foamability and/or foam stability of the beers, but not the real performance of each bottle.…”

“…Therefore, these may be used to assess the quality of raw material and beer formulation but not to assess the quality of individual beer bottles and their sealability. Furthermore, all, except for the shake test and foam-lacing test, measure the beer samples between 20 and 25 • C, which are not the usual consumption temperatures of 4-14 • C according to the beer style [66,67]. On the other hand, the methods belonging to category (ii) are the sigma value [59], Constant method [68], foam cylinder method [69], Ross and Clark [70], foam collapse time and RoboBEER (University of Melbourne, Melbourne, VIC, Australia) [14,71,72].…”

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

“…However, most of those techniques require the use of a charge-coupled device camera and specialized equipment such as a nozzle, water jackets, and a chip attached to the camera, among others; these make them less affordable and available to other users. Therefore, other methods, such as the low-cost image analysis Rudin method developed by Cimini et al [65] require the use of an affordable Raspberry Pi computer and camera module, but it is only able to measure the foam half-time. A newly developed method using Matlab ® algorithms to assess beer color and foam-related parameters were presented by Gonzalez Viejo et al [7].…”

Computer Vision Method in Beer Quality Evaluation—A Review

“…Authors reported that the LCIA system could be successfully used for analyzing beer foam attributes, such as the number of bubbles in the beer, their distribution, diameter and size. Cimini et al [68] presented an inexpensive, automated and flexible image analysis method instead of the standard foam head retention method (developed by Rudin [69]). The standard Rudin's method is improved by using three different systems for automatic data collection: tracking software for mouse movement; Accurate Image Analysis system (AIA) and low-cost image analysis (LCIA) system.…”