Evaluation of bubble characteristics of sparkling wine based on MATLAB image processing

  • ZHENG Siwen ,
  • HUANG Shixin ,
  • ZHOU Yingtian ,
  • WANG Kai ,
  • LIU Xuwei ,
  • ZHAO Lei ,
  • HU Zhuoyan
Expand
  • (College of Food Science, South China Agricultural University, Guangzhou 510642, China)

Received date: 2023-02-01

  Revised date: 2023-02-17

  Online published: 2023-11-01

Abstract

In order to characterize the bubble characteristics of sparkling wine and explore the relationship between bubble characteristics and CO2 content, bubble images of sparkling wine were collected by a camera, and after image processing, the bubble characteristic parameters were extracted by using connected component labeling. The accuracy of the identified bubble characteristic parameters was verified by using manual counting and Image J software measurement. The reliability of the method was further explored by comparing the bubble characteristic parameters of two commercial sparkling wines under different shooting times (30-300 s). The results showed that the average error rate of the method to identify the number of bubbles in 30 images and the area of 72 bubbles was 3.62% and 7.32%, respectively. By comparing and analyzing the total number, average bubble area, total area and maximum area of bubbles, it was found that bubbles taken in 270-300 s showed better stability and had no significant difference (P>0.05), and this method had high bubble characteristics evaluation ability. By establishing fruit wine models with different carbonation degrees, it was found that the numbers of bubble have a maximum value at 500 kPa, possibly due to the limitation of nucleation sites. The CO2 content was positively correlated with the parameters related to bubble area. The bubble evaluation method proposed in this research can lay a foundation for further exploring the relationship between bubble characteristics and fruit wine components.

Cite this article

ZHENG Siwen , HUANG Shixin , ZHOU Yingtian , WANG Kai , LIU Xuwei , ZHAO Lei , HU Zhuoyan . Evaluation of bubble characteristics of sparkling wine based on MATLAB image processing[J]. Food and Fermentation Industries, 2023 , 49(19) : 120 -126 . DOI: 10.13995/j.cnki.11-1802/ts.034983

References

[1] CULBERT J, RISTIC R, OVINGTON L, et al.Influence of production method on the sensory profile and consumer acceptance of Australian sparkling white wine styles[J].Australian Journal of Grape and Wine Research, 2017, 23(2):170-178.
[2] CULBERT J, COZZOLINO D, RISTIC R, et al.Classification of sparkling wine style and quality by MIR spectroscopy[J].Molecules, 2015, 20(5):8341-8356.
[3] LIGER-BELAIR G, MARCHAL R, ROBILLARD B, et al.Study of effervescence in a glass of champagne:frequencies of bubble formation, growth rates, and velocities of rising bubbles[J].American Journal of Enology and Viticulture, 1999, 50(3):317-323.
[4] BONHOMMEAU D A, PERRET A, NUZILLARD J, et al.Unveiling the interplay between diffusing CO2 and ethanol molecules in champagne wines by classical molecular dynamics and 13C NMR spectroscopy[J].The Journal of Physical Chemistry Letters, 2014, 5(24):4232-4237.
[5] VIEJO C G, TORRICO D D, DUNSHEA F R, et al.The effect of sonication on bubble size and sensory perception of carbonated water to improve quality and consumer acceptability[J].Beverages, 2019, 5(3):58.
[6] 姚萌萌, 刘姗, 康嘉伟, 等.响应面法优化红枣起泡酒二次发酵工艺[J].中国酿造, 2019, 38(9):112-116.
YAO M M, LIU S, KANG J W, et, al.Optimization of secondary fermentation process of jujube sparkling wine by response surface methodology[J].China Brewing, 2019, 38(9):112-116.
[7] CONDÉ B C, BOUCHARD E, CULBERT J A, et al.Soluble protein and amino acid content affects the foam quality of sparkling wine[J].Journal of Agricultural and Food Chemistry, 2017, 65(41):9110-9119.
[8] ZUBIA C S, DIZON E.Physico-chemical, antioxidant and sensory properties of artificially-carbonated fruit wine blends[J].International Food Research Journal, 2019, 26(1):217-224.
[9] PÉREZ-BERNAL J L, VILLAR-NAVARRO M, MORALES M L, et al.The smartphone as an economical and reliable tool for monitoring the browning process in sparkling wine[J].Computers and Electronics in Agriculture, 2017, 141:248-254.
[10] 黄星奕, 徐海霞, 王顺, 等.基于计算机视觉和嗅觉的菠菜叶绿素含量检测方法研究[J].现代食品科技, 2017, 33(5):247-252;297.
HUANG X Y, XU H X, WANG S, et al.Study on spinach chlorophyll detection method using computer vision and artificial olfactory sensor[J].Modern Food Science and Technology, 2017, 33(5):247-252;297.
[11] 施行, 王巧华, 顾伟, 等.基于机器视觉的红提串无损检测及分级[J].食品科学, 2021, 42(18):232-239.
SHI H, WANG Q H, GU W, et, al.Non-destructive firmness detection and grading of bunches of red globe grapes based on machine vision[J].Food Science, 2021, 42(18):232-239.
[12] 徐灵双. 海水中气泡可视化测量及气泡特性研究[D].天津:天津大学, 2018.
XU L S.Research on visualization measurement and characteristics of bubble in seawate[D].Tianjin:Tianjin University, 2018.
[13] 魏伏佳. 基于卷积神经网络的清水混凝土表面气泡检测与评价[D].重庆:重庆大学, 2020.
WEI F J.Bughole detection and evaluation of fair-faced concrete surface based on convolutional neural network[D].Chongqing:Chongqing University, 2020.
[14] LIMA B, FUENTES S, CARON M, et al.The use of a portable robotic sparkling wine pourer and image analysis to assess wine quality in a fast and accurate manner[J].Acta Horticulturae, 2016(1115):69-74.
[15] 郭丹, 胡卓炎, 梁琳侦, 等.荔枝果汁饮料碳酸化处理的工艺参数优化[J].现代食品科技, 2012, 28(7):819-824.
GUO D, HU Z Y, LIANG L Z, et al.Optimization of processing parameters for carbonated litchi beverages using desirability function approach[J].Modern Food Science and Technology, 2012, 28(7):819-824.
[16] 朱小倩. 基于图像处理的气泡特征提取研究[D].武汉:华中师范大学, 2021.
ZHU X Q.Research on bubble feature extraction based on image processing[D].Wuhan:Central China Normal University, 2021.
[17] 张增康, 马卫红.基于双峰法的纱线图像阈值分割研究[J].化纤与纺织技术, 2018, 47(3):34-38.
ZHANG Z K, MA W H.Threshold segmentation of yarn image based on bimodal method[J].Chemical Fiber & Textile Technology, 2018, 47(3):34-38.
[18] 王乐乐, 雍晓东, 李然, 等.图像处理技术在气泡特性研究中的应用[J].四川大学学报(工程科学版), 2012, 44(S2):188-192.
WANG L L, YONG X D, LI R, et, al.Image processing technology in the application of bubble characteristics research[J].Journal of Sichuan University (Engineering Science Edition), 2012, 44(S2):188-192.
[19] WEI F J, YAO G, YANG Y, et al.Instance-level recognition and quantification for concrete surface bughole based on deep learning[J].Automation in Construction, 2019, 107:102920.
[20] MAHFELI M, MINAEI S, FADAVI A, et al.Precision measurement of physical properties of orchid synthetic seeds produced under various encapsulation conditions using Image J platform[J].Industrial Crops and Products, 2022, 187(Part B):115364.
[21] KANG C, ZHANG W, ZOU Z W, et al.Effects of initial bubble size on geometric and motion characteristics of bubble released in water[J].Journal of Central South University, 2018, 25(12):3021-3032.
[22] LIGER-BELAIR G.The physics and chemistry behind the bubbling properties of champagne and sparkling wines:A state-of-the-art review[J].Journal of Agricultural and Food Chemistry, 2005, 53(8):2788-2802.
[23] GALLART M, TOMÁS X, SUBERBIOLA G, et al.Relationship between foam parameters obtained by the gas-sparging method and sensory evaluation of sparkling wines[J].Journal of the Science of Food and Agriculture, 2004, 84(2):127-133.
[24] LIGER-BELAIR G.Physicochemical approach to the effervescence in Champagne wines[J].Annales de Physique, 2002, 27(4):1-43.
[25] LIGER-BELAIR G.Carbon dioxide in bottled carbonated waters and subsequent bubble nucleation under standard tasting condition[J].Journal of Agricultural and Food Chemistry, 2019, 67(16):4560-4567.
[26] LIGER-BELAIR G, CILINDRE C.How many CO2 bubbles in a glass of beer ?[J].ACS Omega, 2021, 6(14):9672-9679.
[27] LIGER-BELAIR G, PARMENTIER M, JEANDET P.Modeling the kinetics of bubble nucleation in champagne and carbonated beverages[J].The Journal of Physical Chemistry B, 2006, 110(42):21145-21151.
[28] WILT P M.Nucleation rates and bubble stability in water-carbon dioxide solutions[J].Journal of Colloid and Interface Science, 1986, 112(2):530-538.
Outlines

/