Next Article in Journal
Ruminal Fermentation, Milk Production Efficiency, and Nutrient Digestibility of Lactating Dairy Cows Receiving Fresh Cassava Root and Solid Feed-Block Containing High Sulfur
Next Article in Special Issue
Artificial Neural Networks and Gompertz Functions for Modelling and Prediction of Solvents Produced by the S. cerevisiae Safale S04 Yeast
Previous Article in Journal
Influence of Microencapsulation on Fermentative Behavior of Hanseniaspora osmophila in Wine Mixed Starter Fermentation
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Complementing Digital Image Analysis and Laser Distance Meter in Beer Foam Stability Determination

1
Faculty of Food Technology, Josip Juraj Strossmayer University of Osijek, F. Kuhača 18, 31000 Osijek, Croatia
2
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia
*
Authors to whom correspondence should be addressed.
Fermentation 2021, 7(3), 113; https://doi.org/10.3390/fermentation7030113
Received: 28 May 2021 / Revised: 8 July 2021 / Accepted: 12 July 2021 / Published: 14 July 2021
(This article belongs to the Special Issue Machine Learning in Fermented Food and Beverages)

Abstract

:
The aim of this research is to investigate the possibility of applying a laser distance meter (LDM) as a complementary measurement method to image analysis during beer foam stability monitoring. The basic optical property of foam, i.e., its high reflectivity, is the main reason for using LDM. LDM measurements provide relatively precise information on foam height, even in the presence of lacing, and provide information as to when foam is no longer visible on the surface of the beer. Sixteen different commercially available lager beers were subjected to analysis. A camera and LDM display recorded the foam behavior; the LDM display which was placed close to the monitored beer glass. Measurements obtained by the image analysis of videos provided by the visual camera were comparable to those obtained independently by LDM. However, due to lacing, image analysis could not accurately detect foam disappearance. On the other hand, LDM measurements accurately detected the moment of foam disappearance since the measurements would have significantly higher values due to multiple reflections in the glass.

Graphical Abstract

1. Introduction

Foam stability and retention is an important indicator of beer quality and freshness. Beer foam stability is expressed as a change of foam height over a certain period. Brewing industries are devoted to producing stabile and retentive foam head since many consumers like to see a big and rich head of foam in a glass. Different for every beer type, beer foam can also result in the lacy pattern at the bottom of the finished beer, known as lacing or cling, especially appreciated in Belgian beers [1]. Foam quality is described by several characteristics such as stability, retention, viscosity, whiteness, bubble size, density [2] and many research papers specifically describe, quantify and monitor foam stability via different methods [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]. Foam stability is a result of many factors. Some of them include foam-positive proteins (Z, LTP1, hordein fragments), hop acids, non-starch polysaccharides and metal ions, while lipids and high ethanol levels reduce foam stability [2,26].
Recent scientific advances in image analysis methods have led to the popularization of this method and its ubiquitous application in different fields. Image analysis methods display several setbacks such as camera focusing as well as light reflection off the surface and within the beer glass [25]. This demands that the measuring set-up has to be done under controlled directional light conditions which entail lower illuminance values and automated focusing. In beer foam monitoring via image analysis, an additional setback exists as a result of foam lacing or clinging, which makes it impossible to detect the actual level of foam [27].
In our previous research [27], the use of a RGB camera in foam assessment was portrayed as a low-maintenance, cheap and easy method that requires minimal input from the employees and is applicable in every laboratory. However, if applied alone, it gives inaccurate results when foam lacing is present. With the introduction of a 3D camera [27], the problem of lacing is no longer present, but the automated image analysis method based on the analysis of RGB video images and 3D data for accurate detection of the foam height becomes more complex for everyday usage. The proposed solution identifies the necessity to have two cameras and led to two related analyses being conducted. In order to simplify the procedure of obtaining accurate foam height data from the measurements provided by the 3D camera, it was necessary to replace it with an accurate available device whose output data could be detected by an existing recognition system based on an RGB camera. Previous knowledge of working with solids has imposed the laser distance meter (LDM) as a logical solution. Using one RGB camera and one software [27], it is possible to measure the height of the foam and recognize the measured value on the LDM display by image analysis of the recorded video. For a given frame in the region of interest, color segmentation by filtering thresholding in HSV color space was used to generate a binary image. By performing morphological operations of erosion followed by dilation on the binary image, the average height of foam is measured as well as the value on LDM using optical character recognition.
The initial assumption based on a high level of foam reflectivity did not include the possibility of laser beam penetration into the foam, which proved to be the biggest problem in measurement accuracy. What has proven to be crucial to complete this research is that LDM detects the moment of foam disappearance. Another identified challenge was the inability to recognize digits on the LDM display using optical character recognition, due to the refreshing of the display giving the camera unreadable data.
The aim of this paper was to assess the applicability of a non-invasive, objective, and affordable image analysis method based on one RGB camera and LDM measurements under real conditions, in order to monitor and valorize the foam stability of lager beers.

2. Materials and Methods

Sixteen samples of commercially available light lager beers packaged in brown/green glass bottles (0.5 L) underwent analysis using the methods described below. Ten were domestic beers and six others were foreign (Germany, Czech Republic, Denmark, Slovenia and Holland). Beer samples were held at room temperature (23 °C) for two days, in order to reduce the influence of temperature fluctuations, as can been seen from Figure 1.
Glasses (0.5 L; generic brand, model Lilith, h = 185 mm, Ø = 0.75 mm) were washed, and degreased then rinsed in demineralized water and dried. All the glasses were identical and left to cool down at room temperature prior to analysis, which can be seen from Figure 2. Values of the thermogram legend are set by the infrared thermal camera, according to the registered radiation.
For each experiment, beer was hand-poured according to [28], and as described in Nyarko et al. [27].
Figure 3 shows the experimental setup used in data collection. The basic idea was to develop a complementary method that could provide information about foam stability in cases when foam lacing obstructs visual information for the RGB camera.
The optical characteristic of foam to reflect 88% of light (Table 1) [29] led to the idea that LDM could be employed in this research.

2.1. Image Analysis of Video

The automated image analysis procedure described and used in [27] for measuring foam height from recorded RGB videos was implemented in this paper as well. A brief description of the procedure is described herein.
Video recordings, with a frame rate of 30 fps, were taken of each beer sample over a 5 min period using a Canon G16 RGB camera(Canon PowerShot G16; Ota City, Tokyo, Japan). Determining the height of beer foam using image analysis was performed using seven steps (Figure 4):
  • A region of interest (ROI) of known width (w) and height (l) for a given frame of the recorded video is defined. It is imperative the whole height of the foam and part of the beer is covered by the ROI since all the next steps are performed only on this ROI.
  • The ROI is color segmented in HSV color space by thresholding using previously defined lower and upper values of the foam color in the HSV color space.
  • A binary image of the thresholded ROI in HSV color space is generated.
  • Morphological operations of erosion followed by dilation are performed on the binary image to eliminate small white noises or artifacts that appear in the image.
  • The largest contour on the binary image is determined. This contour marks the boundary of the foam/head.
  • The area (A) of the region enclosed by the largest contour is determined.
  • The average height (h) of the beer foam in pixels is then determined using Equation (1):
h = A/w.
The average height of the beer foam in mm can be obtained using the conversion 1 mm = 6.6 px.
The procedure was implemented in the Python programming language [30] using the OpenCV library v.3.4.2 [31]. Since the beer glass is always located in the same position, the algorithm can be run in either offline or online mode in order to automate the process of determining the beer foam height. In this paper, the height of foam was determined from the recorded videos (offline mode). Every 10 s, 5 consecutive frames were taken and the height of foam determined for each frame. The average height in pixels for these 5 measurements was taken to represent the height of foam every 10 s.

2.2. Measurement Using a Laser Distance Meter

To measure the decrease in foam height, a BOSCH GLM 80 Laser Distance Meter (Bosch, Gerlingen-Schillerhöhe, Germany) with an available option to search for maximal and minimal value was used (Figure 5). This option enabled the measurement of foam height decrease during a 5 min interval with a frequency of 1 Hz. This was also recorded by the aforementioned RGB camera in video format. As shown in Figure 3, the LDM measured the distance to the top of the foam head by reflecting off a mirror placed above the beer glass. Hence, with time, the decrease in foam height is measured as an increase in distance by the LDM.
We initially considered automating the readings from the LDM using optical character recognition on each video frame. However, this idea was dropped due to the erratic changes on LDM display during image sampling, as can be seen in Figure 6.

3. Results and Discussion

RGB video recordings were obtained for 16 samples (denoted by S01…S16), each lasting 5 min. Using the image analysis procedures described in Section 2.1, the estimated height (in mm) was obtained from the RGB video every 10 s. The results of the measurements are displayed in Table 2.
Figure 7 shows foam height (mm) obtained by performing image analysis on RGB images. Table 3 shows values of foam height recorded by LDM.
Figure 8 shows foam height (mm) obtained by LDM measurements. Higher values of foam height are noticed when comparing data shown in Table 2 to that in Table 1. This is due to the fact that, despite having a high reflectivity of 88% (Table 1), the laser beam still penetrates the beer foam and reflects between the layers. At this stage, the properties of the foam, especially the size of the bubbles, contribute to the measurement error. Hence, laser measurement can only be used as an indicator of foam disappearance since the measured values are not precise and accurate. Figure 9 represents the difference between the measured values obtained by image analysis and LDM for the maximum height of beer foam measured at the start of the experiment (time = 0 s). A mean discrepancy value of 7.29 mm is obtained for all samples.
Based on the information from Table 2 and Table 3 and by applying the algorithm shown in Figure 10, information about lacing, i.e., the time instance of disappearance of foam, can be obtained. In Figure 10, X denotes the sample measurement of foam height determined by image analysis, while Y denotes that determined by LDM. It should be noted that this procedure can be used in both online and offline measurement modes to determine when to end measurement. In this research case, offline analysis was performed. As mentioned earlier, a video lasting for 5 min (300 s) was taken of each beer sample. Starting with t = 0 s, measurements are performed every 10 s. Foam height is automatically determined via image analysis as previously described, while LDM measurements need to be determined manually. In order to synchronize LDM measurements with those obtained by image analysis, the same five consecutive frames used in image analyses are stored and the LDM measurements are read manually (Figure 6). The instant the current LDM measurement significantly differs from the previous measurement is an indication that the laser has fully penetrated the foam or the foam layer is significantly reduced (marked in bold in Table 3). At this point, the automated measurement procedure via image analyses is stopped and lacing can be denoted as the foam height determined by image analysis (Xn).
Figure 11 shows the information about lacing values for 16 samples analyzed in this research shown in Table 2 and Table 3.
By comparing the numerical values from Figure 11 and photos of the last phase of measurement (Figure 12), it can be concluded that the algorithm gave satisfactory results in all cases.

4. Conclusions

Beer foam stability is easily affected by storage under unfavorable conditions (temperature fluctuations, UV light exposure). The novelty of this paper is the use of a laser distance meter combined with the application of digital image analysis in beer foam stability measurement. The basic hypothesis, however, was to rely on the laser distance meter to provide more precise measurements compared to that obtained by image analysis. The data obtained by laser distance meter turned out to be significantly lower than the expected values. This could be attributed to the multiple reflections between the foam (bubbles) layers. Nevertheless, the use of the laser distance meter clearly identifies the instant the foam disappears. With this knowledge, it can be determined whether the values obtained in measurements obtained by the implemented automated image analysis procedure are due to lacing or not. However, in order to truly corroborate this method, further examinations involving different beers (wheat, craft, black) with different foam characteristics should take place. The main characteristics of this method are affordability and precision in detection of foam disappearance, which cannot be recorded with the human eye. Moreover, with this method we actually tried to investigate whether the foam matrix can be detected and quantified via optical methods.
Since we depleted all optical methods, future aspects of this research include ultrasound application in foam stability detection and measurement. This could also transpire to be a cheap, precise and available detection method.

Author Contributions

Conceptualization, K.H. and E.K.N.; methodology, E.K.N. and H.G.; software, E.K.N.; formal analysis, H.G.; investigation, K.M.; writing—original draft preparation, K.H.; writing—review and editing, E.K.N. and K.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. BJCP. Beer Style Guidelines; Gordon, S., Kristen, E., Eds.; Available online: www.bjcp.org (accessed on 6 March 2021).
  2. Bamforth, C.W. The foaming properties of beer. J. Inst. Brew. 1985, 91, 370–383. [Google Scholar] [CrossRef]
  3. Gonzalez, V.C.; Torrico, D.D.; Dunshea, F.R.; Fuentes, S. Bubbles, Foam Formation, Stability and Consumer Perception of Carbonated Drinks: A Review of Current, New and Emerging Technologies for Rapid Assessment and Control. Foods 2019, 8, 596. [Google Scholar] [CrossRef][Green Version]
  4. Hackbarth, J.J. Multivariate Analyses of Beer Foam Stand. J. Inst. Brew. 2006, 112, 17–24. [Google Scholar] [CrossRef]
  5. Prins, A.; van Marle, J.T. Foam formation in beer: Some physics behind it. Monogr. Eur. Brew. Conv. 1999, 27, 26–36. [Google Scholar]
  6. Ronteltap, A.; Hollemans, M.; Bisperink, C.G.J.; Prims, A.R. Beer foam physics. Tech. Q. Master Brew. Assoc. Am. 1991, 28, 25–32. [Google Scholar]
  7. Fisher, S.; Hauser, G.; Sommer, K. Influence of dissolved gases on foam. Monogr. Eur. Brew. Conv. 1999, 27, 37–46. [Google Scholar]
  8. Evans, D.E.; Sheehan, M.C. Don’t Be Fobbed Off: The Substance of Beer Foam—A Review. J. Am. Soc. Brew. Chem. 2002, 60, 47–57. [Google Scholar] [CrossRef]
  9. ASBC. Methods of Analysis. Method Beer-22. Foam Collapse Rate; American Society of Brewing Chemists: St. Paul, MN, USA, 2018. [Google Scholar] [CrossRef]
  10. Klopper, W.J. Foam stability and foam cling. In Proceedings of the European Brewing Convention Congress, Salzburg, Austria, 21 May 1973; Elsevier Scientific: Amsterdam, The Netherlands, 1973; pp. 363–371. [Google Scholar]
  11. Rudin, A. Measurement of the foam stability of beers. J. Inst. Brew. 1957, 63, 506–509. [Google Scholar] [CrossRef]
  12. Rasmussen, J.N. Automated analysis of foam stability. Carlsberg Res. Commun. 1981, 46, 25–36. [Google Scholar] [CrossRef][Green Version]
  13. Jackson, G.; Bamforth, C. The measurement of foam-lacing. J. Inst. Brew. 1982, 88, 378–381. [Google Scholar] [CrossRef]
  14. Constant, M. A practical method for characterizing poured beer foam quality. J. Am. Soc. Brew. Chem. 1992, 50, 37–47. [Google Scholar] [CrossRef]
  15. Vundla, W.; Torline, P. Steps toward the formulation of a model foam standard. J. Am. Soc. Brew. Chem. 2007, 65, 21–25. [Google Scholar] [CrossRef]
  16. Amerine, M.A.; Martini, L.; Mattei, W.D. Foaming Properties of Wine. Ind. Eng. Chem. 1942, 34, 152–157. [Google Scholar] [CrossRef]
  17. Wilson, P.; Mundy, A. An improved method for measuring beer foam collapse. J. Inst. Brew. 1984, 90, 385–388. [Google Scholar] [CrossRef]
  18. Evans, D.E.; Surrel, A.; Sheehy, M.; Stewart, D.C.; Robinson, L.H. Comparison of foam quality and the influence of hop α-acids and proteins using five foam analysis methods. J. Am. Soc. Brew. Chem. 2008, 66, 1–10. [Google Scholar] [CrossRef]
  19. Evans, D.E.; Oberdieck, M.; Red, K.S.; Newman, R. Comparison of the Rudin and NIBEM Methods for Measuring Foam Stability with a Manual Pour Method to Identify Beer Characteristics That Deliver Consumers Stable Beer Foam. J. Am. Soc. Brew. Chem. 2012, 70, 70–78. [Google Scholar] [CrossRef]
  20. Smith, R.J.; Davidson, D.; Wilson, R.J. Natural foam stabilizing and bittering compounds derived from hops. J. Am. Soc. Brew. Chem. 1998, 56, 52–57. [Google Scholar] [CrossRef]
  21. Cimini, A.; Pallottino, F.; Menesatti, P.; Moresi, M. A low-cost image analysis system to upgrade the rudin beer foam head retention meter. Food Bioprocess Technol. 2016, 9, 1587–1597. [Google Scholar] [CrossRef]
  22. Gonzalez, V.C.; Fuentes, S.; Li, G.; Collmann, R.; Condé, B.; Torrico, D. 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. Food Res. Int. 2016, 89, 504–513. [Google Scholar] [CrossRef] [PubMed]
  23. Gonzalez, V.C.; Fuentes, S.; Torrico, D.D.; Howell, K.; Dunshea, F.R. Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers. J. Food Sci. 2018, 83, 1381–1388. [Google Scholar] [CrossRef] [PubMed]
  24. Gonzalez Viejo, C.; Fuentes, S.; Howell, K.; Torrico, D.D.; Dunshea, F.R. Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers. Physiol. Behav. 2019, 200, 139–147. [Google Scholar] [CrossRef]
  25. Lukinac, J.; Mastanjević, K.; Mastanjević, K.; Nakov, G.; Jukić, M. Computer Vision Method in Beer Quality Evaluation—A Review. Beverages 2019, 5, 38. [Google Scholar] [CrossRef][Green Version]
  26. Evans, D.E.; Sheehan, M.C.; Stewart, D.C. The impact of malt derived proteins on beer foam quality: Part II: The influence of malt foaming proteins and non-starch polysaccharides on beer foam quality. J. Inst. Brew. 1999, 105, 171–177. [Google Scholar] [CrossRef]
  27. Nyarko, E.K.; Glavaš, H.; Habschied, K.; Mastanjević, K. Determination of Foam Stability in Lager Beers Using Digital Image Analysis of Images Obtained Using RGB and 3D Cameras. Fermentation 2021, 7, 46. [Google Scholar] [CrossRef]
  28. Middle European Brewing Analysis Commission (MEBAK); Band II.n Brautechnische Middle European Brewing Analysis Commission (MEBAK). Band II.n Brautechnische Analysenmethoden, 3th ed.; Selbstverlag der MEBAK: Freising-Weihenstephan, Germany, 1997. [Google Scholar]
  29. RIEGL, Operation of a Pulsed Laser Distance Meter, General-Information-Distancemeter.pdf, Riegl Laser Measurement Systems GmbH, Application Note AN-GI002. 2001. Available online: http://www.riegl.com/uploads/tx_pxpriegldownloads/General-Information-Distancemeter.pdf (accessed on 16 April 2021).
  30. Python Programming Language. Available online: https://www.python.org/ (accessed on 21 January 2021).
  31. OpenCV. Available online: https://opencv.org/ (accessed on 21 January 2021).
Figure 1. Infrared thermographic recordings of samples.
Figure 1. Infrared thermographic recordings of samples.
Fermentation 07 00113 g001
Figure 2. Infrared thermographic recordings of glasses.
Figure 2. Infrared thermographic recordings of glasses.
Fermentation 07 00113 g002
Figure 3. Experimental setup.
Figure 3. Experimental setup.
Fermentation 07 00113 g003
Figure 4. Estimation of foam height from image. (a) Region of interest (ROI) of known width (w) and height (l) is initially defined for the image. The ROI is thresholded in HSV color space. The pixels satisfying the threshold are marked in red. (b) Binary image of ROI thresholded in HSV color is generated. (c) Morphological operations of erosion followed by dilation performed on binary image to remove artifacts. The largest contour (marked in red) is found.
Figure 4. Estimation of foam height from image. (a) Region of interest (ROI) of known width (w) and height (l) is initially defined for the image. The ROI is thresholded in HSV color space. The pixels satisfying the threshold are marked in red. (b) Binary image of ROI thresholded in HSV color is generated. (c) Morphological operations of erosion followed by dilation performed on binary image to remove artifacts. The largest contour (marked in red) is found.
Fermentation 07 00113 g004
Figure 5. Photos of experimental setup: (a) general setup; (b) LDM and a sample; (c) laser dot on the beer foam.
Figure 5. Photos of experimental setup: (a) general setup; (b) LDM and a sample; (c) laser dot on the beer foam.
Fermentation 07 00113 g005
Figure 6. Changes of measured values recorded on BOSCH GLM 80 LDM for five consecutive frames of the RGB video.
Figure 6. Changes of measured values recorded on BOSCH GLM 80 LDM for five consecutive frames of the RGB video.
Fermentation 07 00113 g006
Figure 7. Graphic representation of the foam height determined by performing image analysis on RGB videos.
Figure 7. Graphic representation of the foam height determined by performing image analysis on RGB videos.
Fermentation 07 00113 g007
Figure 8. Graphic representation of the foam height determined by laser distance meter.
Figure 8. Graphic representation of the foam height determined by laser distance meter.
Fermentation 07 00113 g008
Figure 9. The difference between the measured values by image analysis and a laser distance meter for first measurement, i.e., maximum height of beer foam.
Figure 9. The difference between the measured values by image analysis and a laser distance meter for first measurement, i.e., maximum height of beer foam.
Fermentation 07 00113 g009
Figure 10. Algorithm used for beer foam analysis and determination of lacing.
Figure 10. Algorithm used for beer foam analysis and determination of lacing.
Fermentation 07 00113 g010
Figure 11. Lacing amount (mm) for individual samples.
Figure 11. Lacing amount (mm) for individual samples.
Fermentation 07 00113 g011
Figure 12. Photos of 16 samples in the last phase of measurement (the first row contains images from samples S01–S08, while the second row contains images from samples S09–S16).
Figure 12. Photos of 16 samples in the last phase of measurement (the first row contains images from samples S01–S08, while the second row contains images from samples S09–S16).
Fermentation 07 00113 g012
Table 1. Reflectivity of certain materials according to [29].
Table 1. Reflectivity of certain materials according to [29].
MaterialReflectivity
White paperup to 100%
Newspaper with print69
Dimension lumber94
Snow80–90
White masonry85
Concrete, smooth24
Beer foam88
Table 2. Height (mm) of foam determined by performing image analysis on RGB videos of 16 beer samples.
Table 2. Height (mm) of foam determined by performing image analysis on RGB videos of 16 beer samples.
Sample
TimeS01S02S03S04S05S06S07S08S09S10S11S12S13S14S15S16
039586048374248492517486246431815
1035545742323843452310455743381211
20315057372835404220845554134108
3028475633243239401754454392986
4025445530223036371544351362675
5021415226202834351324349342364
6019395024182633331123148312054
70173748211624313011-3547311843
8015354719152329289-3446301643
9013324017142227268-2642301543
10012304115132226258-2439291333
11010284014122224248-213829123-
12010253913112224268-143628113-
1309232812102223237-133328113-
1408213612102123237-133128103-
150819351192223237-13262893-
160717341182123257-13252883-
170615321082123237-1324258--
180614251082122237-1224237--
190514291072123237-1224217--
200513301072122226-1324226--
21051229972023226-1223216--
22041128972022226-1224206--
23041026862022226-1223216--
24041025862023226-1223235--
2504924862022226-1222215--
2604919762022226-1222215--
270482276172222--1222225--
280482176202222--1220215--
290472066192222--1120215--
300471465202222--1220215--
The sign “-” in the table indicates that measurements were stopped due to the foam’s disappearance.
Table 3. Height (mm) of foam of 16 beer samples determined by laser distance meter.
Table 3. Height (mm) of foam of 16 beer samples determined by laser distance meter.
Sample
TimeS01S02S03S04S05S06S07S08S09S10S11S12S13S14S15S16
032515140303439451264355383766
1029495037263339411243954353355
202546493323303739923751333043
3018455030172936361023449302734
4018434827152833349331452721310
5015404725142631327−21128442421712
6015394621102229317−212264223182125
7012374419102127285-2339201625−210
801034421791924275-21371714−236−210
90732411591622254-18311611−244−204
100929391261319223-1729149−217−205
110726361261217204-1426116−220-
12042534951015186-1223107−223-
1305233274913165-112086−226-
14032130831012147-71875−247-
1503192974811128-61654−261-
160216286479109-6144432-
170414276478915-51244--
1804132555669−213-51135--
1904122354578−213-5934--
2004122445566−213-12824--
2103102146455−213-7835--
220492146435−213-7625--
230472052143542-−205534--
24057204−183335−212-11534--
25046195−183324−211-7534--
26056185−184223−213-15435--
27054175−181322--−216434--
28094155−181222--−200422--
29064154−179122--−202528--
30064144−179122--−202628--
The bolded data indicate the errors in measurement due to reduction of foam layer or its complete disappearance which caused multiple reflections that resulted in unrealistic readings. The sign “-” in the table indicates that measurements were stopped due to the foam’s disappearance.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Habschied, K.; Glavaš, H.; Nyarko, E.K.; Mastanjević, K. Complementing Digital Image Analysis and Laser Distance Meter in Beer Foam Stability Determination. Fermentation 2021, 7, 113. https://doi.org/10.3390/fermentation7030113

AMA Style

Habschied K, Glavaš H, Nyarko EK, Mastanjević K. Complementing Digital Image Analysis and Laser Distance Meter in Beer Foam Stability Determination. Fermentation. 2021; 7(3):113. https://doi.org/10.3390/fermentation7030113

Chicago/Turabian Style

Habschied, Kristina, Hrvoje Glavaš, Emmanuel Karlo Nyarko, and Krešimir Mastanjević. 2021. "Complementing Digital Image Analysis and Laser Distance Meter in Beer Foam Stability Determination" Fermentation 7, no. 3: 113. https://doi.org/10.3390/fermentation7030113

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop