The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Sigfredo Fuentes - One of the best experts on this subject based on the ideXlab platform.
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soluble protein and amino acid content affects the Foam Quality of sparkling wine
Journal of Agricultural and Food Chemistry, 2017Co-Authors: Bruna Conde, Sigfredo Fuentes, Eloise Bouchard, Julie A Culbert, Kerry L Wilkinson, Kate HowellAbstract:Proteins and amino acids are known to influence the Foam characteristics of sparkling wines. However, it is unclear to what extent they promote Foam formation and/or stability. This study aimed to investigate the effect of protein content and amino acid composition, measured via the bicinchoninic acid assay and high-performance liquid chromatography, respectively, on the Foaming properties of 28 sparkling white wines, made by different production methods. Foam volume and stability were determined using a robotic pourer and computer vision algorithms. Modifications were applied to the protein determination method involving the use of yeast invertase as a standard in order to improve quantification accuracy. The protein content was found to be significantly correlated to parameters representative of Foam stability, as were the amino acids arginine, asparagine, histidine, and tyrosine. Additionally, the production method was found to influence the Foam collar height, which favored Foaming in Methode Traditionnelle wines over other those made by production methods. Understanding the contributions of key wine constituents to the visual and mouthfeel parameters of sparkling wine will enable more efficient production of high-Quality wines.
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Development of a robotic and computer vision method to assess Foam Quality in sparkling wines
Food Control, 2017Co-Authors: Bruna C. Condé, Richard Collmann, Maeva Caron, Di Xiao, Sigfredo Fuentes, Kate S. HowellAbstract:Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist Quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and Foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of Foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of Foam Quality, including Foamability (the ability of the Foam to form) drainability (the ability of the Foam to resist drainage) and bubble count and allometry. The Foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher Quality scores from trained panelists were positively correlated with Foam stability and negatively correlated with the velocity of Foam dissipation and the height of the collar. Significant correlations were observed between the wine Quality measurements of total protein, titratable acidity, pH and Foam expansion. The percentage of the wine in the Foam was found to promote the formation of smaller bubbles and to reduce Foamability, while drainability was negatively correlated to Foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their Foam and bubble characteristics, Quality scores and chemical parameters. The technique developed in this study objectively assessed Foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and Foam parameters obtained automatically, might assist in unraveling factors contributing to wine Quality and directions for further research.
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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 Research International, 2016Co-Authors: Claudia Gonzalez Viejo, Richard Collmann, Sigfredo Fuentes, Bruna Conde, Guangjun Li, Damir Dennis TorricoAbstract:Abstract There are currently no standardized objective measures to assess beer Quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of Foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on Foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to Quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to Quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.
Kate S. Howell - One of the best experts on this subject based on the ideXlab platform.
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Development of a robotic and computer vision method to assess Foam Quality in sparkling wines
Food Control, 2017Co-Authors: Bruna C. Condé, Richard Collmann, Maeva Caron, Di Xiao, Sigfredo Fuentes, Kate S. HowellAbstract:Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist Quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and Foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of Foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of Foam Quality, including Foamability (the ability of the Foam to form) drainability (the ability of the Foam to resist drainage) and bubble count and allometry. The Foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher Quality scores from trained panelists were positively correlated with Foam stability and negatively correlated with the velocity of Foam dissipation and the height of the collar. Significant correlations were observed between the wine Quality measurements of total protein, titratable acidity, pH and Foam expansion. The percentage of the wine in the Foam was found to promote the formation of smaller bubbles and to reduce Foamability, while drainability was negatively correlated to Foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their Foam and bubble characteristics, Quality scores and chemical parameters. The technique developed in this study objectively assessed Foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and Foam parameters obtained automatically, might assist in unraveling factors contributing to wine Quality and directions for further research.
S Vincentbonnieu - One of the best experts on this subject based on the ideXlab platform.
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experimental study of injection strategy for low tension gas flooding in low permeability high salinity carbonate reservoirs
Journal of Petroleum Science and Engineering, 2020Co-Authors: Alolika Das, Rouhi Farajzadeh, S Vincentbonnieu, Nhut Nguyen, Jeffrey G Southwick, S Khaburi, Al A Kindi, Quoc P. NguyenAbstract:Abstract Previous experimental studies have proven that Low-Tension-Gas (LTG) flooding can be a suitable enhanced oil recovery (EOR) method for low-permeability carbonate reservoirs with high salinity and hard formation brine. LTG flooding was observed to improve oil recovery by combining two effects: a reduction of the interfacial tension (IFT) between oil and water and mobility control through the formation of in-situ Foam with an injected gas. However, the high cost of chemicals and/or the limited supply of gas could make this process economically challenging. In this study, the primary goal was to reduce the amount of injected gas and surfactant to make the LTG process more economically feasible. A low-permeable ( 200,000 ppm and hardness 19,000 ppm) and temperature 69 °C was the target reservoir of this study. Effect of varying the concentration and pore volumes of the ultra-low IFT inducing surfactant slug injected on oil recovery was studied. Nitrogen gas was co-injected during selected time periods throughout the entire surfactant injection in order to identify the significance of mobility control during the crucial phases of the LTG flooding. The coreflood results emphasized the significance of the injection of gas, even at lower Foam Quality, for the maintenance of mobility control. Ultimate oil recovery of over 60% (residual oil post waterflood) was achieved, even after reducing the surfactant concentration by 75% and inducing a different in-situ salinity profile as compared to earlier studies. An innovative method for measuring surfactant adsorption using Liquid Chromatography and Mass Spectrometry (LC-MS) was developed, which could provide individual surfactant transport data for each of the three classes of surfactants used.
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probing the effect of oil type and saturation on Foam flow in porous media core flooding and nuclear magnetic resonance nmr imaging
Energy & Fuels, 2018Co-Authors: M Amirmoshiri, R Farajzadeh, Maura Puerto, Yongchao Zeng, Zeliang Chen, Philip M Singer, H Grier, R Kamarul Z Bahrim, S Vincentbonnieu, Sibani Lisa BiswalAbstract:The success of Foam displacement in porous media largely depends on its stability, which is adversely impacted by the presence of oil. In this study, we present the results of an experimental investigation into the effect of oil type and saturation on Foam rheology in Berea sandstone using the nuclear magnetic resonance imaging technique. The results of Foam Quality scan in the presence of remaining hexadecane was compared with those from the oil-free case. We showed that the calculated apparent viscosity values in the presence of remaining hexadecane were higher than those in the absence of oil except at very high Foam qualities. This was attributed to the dominance of relative permeability reduction and generation of oil-in-water emulsions over the Foam-weakening effect of oil. A closer analysis of the fluid distribution further allowed us to quantify the relative significance of the identified oil displacement mechanisms, namely, the increased capillary number, and the micellar solubilization. Addition...
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Foam flow in a model porous medium ii the effect of trapped gas
Soft Matter, 2018Co-Authors: S A Jones, S Vincentbonnieu, N GetrouwAbstract:Gas trapping is an important mechanism in both Water or Surfactant Alternating Gas (WAG/SAG) and Foam injection processes in porous media. Foams for enhanced oil recovery (EOR) can increase sweep efficiency as they decrease the gas relative permeability, and this is mainly due to gas trapping. However, gas trapping mechanisms are poorly understood. Some studies have been performed during corefloods, but little work has been carried out to describe the bubble trapping behaviour at the pore scale. We have carried out Foam flow tests in a micromodel etched with an irregular hexagonal pattern. Image analysis of the Foam flow allowed the bubble centres to be tracked and local velocities to be obtained. It was found that the flow in the micromodel is dominated by intermittency and localized zones of trapped gas. The quantity of trapped gas was measured both by considering the fraction of bubbles that were trapped (via velocity thresholding) and by measuring the area fraction containing immobile gas (via image analysis). A decrease in the quantity of trapped gas was observed for both increasing total velocity and increasing Foam Quality. Calculations of the gas relative permeability were made with the Brooks Corey equation, using the measured trapped gas saturations. The results showed a decrease in gas relative permeabilities, and gas mobility, for increasing fractions of trapped gas. It is suggested that the shear thinning behaviour of Foam could be coupled to the saturation of trapped gas.
Richard Collmann - One of the best experts on this subject based on the ideXlab platform.
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Development of a robotic and computer vision method to assess Foam Quality in sparkling wines
Food Control, 2017Co-Authors: Bruna C. Condé, Richard Collmann, Maeva Caron, Di Xiao, Sigfredo Fuentes, Kate S. HowellAbstract:Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist Quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and Foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of Foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of Foam Quality, including Foamability (the ability of the Foam to form) drainability (the ability of the Foam to resist drainage) and bubble count and allometry. The Foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher Quality scores from trained panelists were positively correlated with Foam stability and negatively correlated with the velocity of Foam dissipation and the height of the collar. Significant correlations were observed between the wine Quality measurements of total protein, titratable acidity, pH and Foam expansion. The percentage of the wine in the Foam was found to promote the formation of smaller bubbles and to reduce Foamability, while drainability was negatively correlated to Foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their Foam and bubble characteristics, Quality scores and chemical parameters. The technique developed in this study objectively assessed Foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and Foam parameters obtained automatically, might assist in unraveling factors contributing to wine Quality and directions for further research.
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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 Research International, 2016Co-Authors: Claudia Gonzalez Viejo, Richard Collmann, Sigfredo Fuentes, Bruna Conde, Guangjun Li, Damir Dennis TorricoAbstract:Abstract There are currently no standardized objective measures to assess beer Quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of Foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on Foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to Quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to Quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.
Bruna Conde - One of the best experts on this subject based on the ideXlab platform.
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soluble protein and amino acid content affects the Foam Quality of sparkling wine
Journal of Agricultural and Food Chemistry, 2017Co-Authors: Bruna Conde, Sigfredo Fuentes, Eloise Bouchard, Julie A Culbert, Kerry L Wilkinson, Kate HowellAbstract:Proteins and amino acids are known to influence the Foam characteristics of sparkling wines. However, it is unclear to what extent they promote Foam formation and/or stability. This study aimed to investigate the effect of protein content and amino acid composition, measured via the bicinchoninic acid assay and high-performance liquid chromatography, respectively, on the Foaming properties of 28 sparkling white wines, made by different production methods. Foam volume and stability were determined using a robotic pourer and computer vision algorithms. Modifications were applied to the protein determination method involving the use of yeast invertase as a standard in order to improve quantification accuracy. The protein content was found to be significantly correlated to parameters representative of Foam stability, as were the amino acids arginine, asparagine, histidine, and tyrosine. Additionally, the production method was found to influence the Foam collar height, which favored Foaming in Methode Traditionnelle wines over other those made by production methods. Understanding the contributions of key wine constituents to the visual and mouthfeel parameters of sparkling wine will enable more efficient production of high-Quality wines.
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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 Research International, 2016Co-Authors: Claudia Gonzalez Viejo, Richard Collmann, Sigfredo Fuentes, Bruna Conde, Guangjun Li, Damir Dennis TorricoAbstract:Abstract There are currently no standardized objective measures to assess beer Quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of Foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automated post hoc computer vision analysis algorithms to assess different parameters based on Foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to Quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to Quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.