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Article

Exploring Visualization of Beverage Consistency Through 2D and 3D Imaging Methods

by
Jane Mertz Garcia
1,*,
Edgar Chambers IV
2,*,
Madison Ukele
1,
Abby Brey Althauser
1 and
David Rehfeld
1
1
Communication Sciences & Disorders, Kansas State University, Manhattan, KS 66506, USA
2
Center for Sensory Analysis and Consumer Behavior, Kansas State University, Manhattan, KS 66502, USA
*
Authors to whom correspondence should be addressed.
Beverages 2025, 11(5), 141; https://doi.org/10.3390/beverages11050141
Submission received: 24 July 2025 / Revised: 11 September 2025 / Accepted: 19 September 2025 / Published: 1 October 2025
(This article belongs to the Section Quality, Nutrition, and Chemistry of Beverages)
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Abstract

Modification to a thin beverage consistency is frequently recommended when swallowing is impaired, but proper thickening during preparation is essential. Contemporary technologies provide innovative ways of addressing quality control due to ongoing challenges in the accuracy of beverage preparation. This study explored two-dimensional (2D) and three-dimensional (3D) methods for visualizing beverages representing different levels of consistency (thin, mildly thick, or moderately thick). A total of 48 adults with limited knowledge about swallowing and no experience with thickened beverages participated. They learned about levels of modification and then viewed 2D images (photos) and 3D virtual models of beverage content. Results showed that their ability to recognize beverage consistency and their decision confidence was generally similar across dimensions even though study participants conveyed a strong preference for viewing 3D models. Qualitative findings underscored the importance of beverage attributes, especially color. Participants differed in their perceptions in using a constant (2D) or multiple angles (3D) when evaluating visualizations. Results help inform about the potential role of visual content in developing instructional resources about thickened beverages prescribed for patients with special medical needs.

1. Introduction

Thin beverages like water, coffee, tea, and juice can be difficult to drink safely when swallowing is impaired (dysphagia). Naturally thin liquids flow quickly and health conditions that affect muscular control or the timing of protective sensory/motor responses can heighten the risk of fluid aspiration. Thickening of thin liquids typically has been applied as part of dysphagia management to compensate for swallowing difficulty and promote swallowing safety [1,2]. The beverage modification, through the addition of a thickening agent, represents a flow pattern that more closely matches a person’s control for safe oral consumption [2,3,4].
Thickened beverages require staff precision with instant starch or gum-based thickening agents to accurately measure/mix to a prescribed level of modification like mildly thick consistency [5]. Even small alterations in the amount of instant thickening agent contribute to changes in the thickness of a drink.
Staff inaccuracies often reflect changes in the amount of a thickening agent and a lack of adherence to product preparation guidelines [6,7]. Insufficient training in how to prepare beverage modifications and high staff turnover contribute to mistakes [5,6,7,8]. Resulting problems in quality control result in several concerns, including patients being asked to consume a level of texture modification that does not represent what was medically prescribed (often too thick), which can further contribute to post swallow residue and health complications [1,3]. The thickening agent also affects flavor and taste characteristics of the base beverage [9]. Patients’ lack of acceptance and noncompliance with beverage consumption may be further heightened due to taste and texture changes that result from excessively thickened beverages [10,11].
Challenges with staff turnover and variable training practices necessitate the development of innovative methods to learn about modifications and ensure quality control with preparations as current methods may not suffice [12,13]. For example, even experienced healthcare staff prepare samples with frequent clumping [6].
Visual methods of training may help in recognizing appropriately and inappropriately thickened beverages [12]. The visual appearance of a beverage, including color, can influence perceptions and preferences [14,15]. In limited reports, preparation accuracy appears aided by providing staff methods for visualizing modifications [16,17].
In addition to two-dimensional (2D) static images, the use of three-dimensional (3D) visualization has emerged as a promising method for instruction [18,19]. 3D virtual models can be rotated to allow analysis from different angles and can be viewed as a whole or detailed part through magnification. The ability to manipulate a 3D model is often a preferred learning format when compared to 2D visualizations [20,21]. Innovative methods such as 3D imaging have not been considered for visualizing modified samples or instructing staff about levels of consistency that include thickened beverages.
The aim of this work is to explore the use of visual content when making judgements about a level of beverage consistency (thin, mildly thick, or moderately thick) illustrated with static 2D images (photos) or 3D virtual models. We wanted to know if participants (1) can use 2D/3D visual content to correctly identify a level of beverage consistency, (2) express confidence in a decision about beverage consistency, and (3) convey clear preferences for 2D/3D visual content and why. Results will help inform about the role of visual content in developing instructional resources about thickened beverages prescribed to address special medical needs.

2. Materials and Methods

2.1. Participants

A total of 48 adult volunteers (83% women; 17% men) met inclusionary criteria: between 18 and 65 years of age, self-reported normal or near normal vision with correction, reading skills for a limited amount of content displayed on a computer monitor, ability to operate a computer mouse, and minimal knowledge about swallowing and the use of beverage modifications. We eliminated three other individuals based on exclusionary criteria. They self-reported previous work experience in preparing thickened beverages or substantial knowledge about swallowing. Participants were largely recruited from university undergraduate courses in human sciences, general areas of study, and by word-of-mouth. All participants provided informed consent. This study was approved by the Kansas State University Institutional Review Board (Proposal #IRB-11698).

2.2. Visualization of Beverage Samples

Beverages represented diverse visual content, including blue-colored water, Dr. Pepper (Keurig Dr. Pepper Group, Fisco, TX, USA), Orange Juice (Tropicana Brands Group, Chicago, IL, USA), and Fruit Punch/Red Gatorade (Gatorade, Chicago, IL, USA). Levels of consistency included Thin (Level 0), Mildly Thick (Level 2), and Moderately Thick (Level 3) based on the liquid texture classification described by the International Dysphagia Diet Standardisation Initiative (IDDSI) [22]. We selected mildly thick and moderately thick consistency given their prominence in patient care [5]. Beverage modifications were prepared with a starch-based thickening agent (Thick It, Kent Precision Foods Group, Inc., Muscatine, IA, USA) using product label directions for an 8-ounce beverage. Once prepared, 6-ounces of the sample was poured into a standard 8-ounce Styrofoam cup. Measurements taken with the IDDSI flow test [23] before and after 3D imaging verified that a beverage sample always represented the correct level of consistency.
3D content was made with the Polycam Application (2022 Release, Polycam Inc., San Francisco, CA, USA, 4.0.10) loaded to an Apple iPhone 12 Pro smartphone. Polycam utilized photogrammetry technology as it took multiple 2D photographs in rapid succession (roughly 140 photos for each sample) and converted them into a 3D model over a process of several minutes. The finished product was then uploaded to Sketchfab.com (Epic Games, New York, NY, USA), an online platform for hosting and sharing 3D virtual models. A 2D screenshot (Figure 1) of the published 3D model was then made to provide the same visual characteristics of beverages across 2D/3D dimensions.

2.3. Instructional Module

The instructional module began with a brief overview of the study followed by parts for 2D and 3D visualizations. Module parts included instructions and IDDSI definitions for each level of beverage consistency [24]. Participants also had a 2D or 3D reference example of a non-study beverage made with apple juice (Mott’s, Plano, TX, USA) to illustrate each level of thickness. The only difference was that the directions for the 3D part included brief instructions showing participants how to operate a 3D virtual model.
Study content was displayed through Adobe Captivate software (2019 Release, Version 11.8.0.586, Adobe Inc., San Jose, CA, USA). It allowed for integration of 3D models within instructional content and software features to enhance module navigation such as buttons to quickly move forward or backward through slides.

2.4. Experimental Design and Procedures

Every participant evaluated a total of 12 beverage samples equally divided between 2D and 3D visual content. Participants viewed all four beverages, two for each dimension (e.g., 2D photos of orange juice & Dr. Pepper and 3D models of Fruit Punch Gatorade & blue water). The sequence of module content was counterbalanced (whether the participant viewed 2D or 3D visualizations first), as well as the specific beverages illustrating 2D or 3D content. The order of consistency levels for each beverage was randomized.
Participants completed the self-paced module in one research session. After reading instructions and learning about consistency levels and their descriptions, participants viewed visual content of one beverage sample at a time. Participants first decided about the level of consistency (thin, mildly thick, or moderately thick) and then provided decision confidence using a 7-point interval rating scale (1—“Very Low” to 7—“Very High” confidence). After completing both parts of the module, participants selected their preferred method of viewing levels of thickness (2D photos or 3D models) and wrote their supportive reasons.

2.5. Data Analysis

Quantitative analysis was performed with IBM SPSS System for Windows (2024, Version 30, New York, NY, USA), including descriptive procedures for percentages, mean values, and standard deviations. Inferential statistics reflected “dimension” as the within-subjects factor. The nonparametric McNemar’s test determined whether right/wrong judgements about consistency levels varied across dimensions. A paired t-test compared decision confidence ratings for 2D and 3D visual content. Across quantitative measures, an alpha level of p < 0.05 was applied.
Responses to open-ended questions were inductively analyzed using qualitative thematic analysis [25]. Two of the authors (M.U. & A.B.A.) transcribed participants’ responses and completed multiple rounds of coding by words, phrase, and/or concepts to determine prominent themes. Each round included individual review followed by consensus discussion. A third author (J.G.) contributed to consensus decisions with each round and final content themes. We did not quantitatively assess the reliability of independent coding efforts because our process focused on reflexive engagement in the coding process. The use of multiple coders, decision-making through consensus, and repeated rounds of coding support the trustworthiness, dependability, credibility, and transferability of the findings [26].

3. Results

3.1. Accuracy with Visual Content

The 48 participants identified the correct level of beverage consistency of 2D photos with 55.9% accuracy and 53.1% accuracy when viewing 3D virtual models. Descriptive statistics showed that participants’ percentage accuracy in determining thin, mildly thick, and moderately thick consistency was generally similar across dimensions (Table 1). When averaged across 2D/3D dimensions, participants were most successful in recognizing thin consistency (60.9%). Mildly thick samples reflected the least accuracy for each dimension (40% range).
McNemar’s test examined the proportion of accuracy judgements (right/wrong) about consistency levels across types of visual content. McNemar’s test confirmed that were was not a statistically significant difference in participants’ accuracy when using 2D and 3D content (p = 0.492). Participants made similar decisions about levels of consistency for both types of visual content.

3.2. Rating of Decision Confidence

Participants rated their decision confidence for a level of consistency on a sample-by-sample basis. The mean rating of confidence for 48 adult participants was generally similar across levels of consistency, ranging from 4.1 to 4.8 (Table 2). The overall mean rating for 2D photos was 4.6 (SD ± 1.3) and 4.4 (SD ± 1.3) for 3D models. A paired t-test revealed a lack of statistical significance across dimensions (t = 1.479, p > 0.05), indicating a similar level of decision confidence about a level of consistency regardless of the type of visual content.

3.3. Preferences and Opinions About Visual Content

Participants indicated their preferred way of viewing visual content. A total of 36 of 48 participants (74.5%) preferred 3D models for deciding about levels of beverage thickness. Only 25.5% (12 participants) conveyed a preference for 2D visual content. Participants’ reasons for selecting one dimension over the other were qualitatively analyzed with prominent themes highlighted in Table 3.
Analysis reflected similar themes for both types of visual content. That is, participants wrote about the importance of beverage attributes such as its color and transparency (lighting) when making a decision. Participants also held the opinion that their preferred visual content made it easier to decide a level of consistency.
One main difference in themes emerged in participants’ preferences for 2D/3D visual content. Participants who preferred a 2D static image liked the constant angle it provided in their decision-making. One participant wrote that, “I prefer learning from the 2D models because I felt it was more straight forward whereas in the 3D models the more I moved them around the more I second guessed.” Participants who preferred 3D models described the opposite point of view. A different participant wrote that, “I enjoyed the 3D model more because I could look at the entire thing instead of one angle. This made me feel like I had a better grasp on what the liquid really looked like, as I wasn’t limited to one given screenshot or picture”.
Given the exploratory nature of this study, beverages were selected to represent a range of colors. Because participants’ perceptions about color appeared to be an important consideration, we looked at the percentage of samples correctly coded for each beverage. Participants were 76% accurate in their overall decisions about blue-colored water, followed by Dr. Pepper (59%), orange juice (43%) and Fruit Punch (red) Gatorade (40%).

4. Discussion

Reliable swallowing of beverages often requires a change in texture consistency to maintain safe hydration. Mistakes in preparing the correct consistency means that patients with special medical needs may be given an unsafe beverage modification and/or drink less due to negative perceptions about taste and consistency [6,7,11]. Quality control is of the utmost importance, including consideration of innovative methods of educating and instructing to achieve appropriately thickened beverages [12,13]. This study explored whether 2D and 3D visual imaging methods can be helpful in recognizing levels of thickness.

4.1. Accuracy and Rating of Decision Confidence for Levels of Consistency

One purpose was to determine if participants could use 2D/3D visual content to correctly identify a level of beverage consistency. Our findings are mixed. The consistency of a thin beverage was recognized more accurately in comparison to mildly thick and moderately thick samples. Thin beverages were in their typical state (no thickening agent), which would also represent the most familiar consistency to study participants. The recognition of mildly thick consistency was the least accurate (42.7% to 47.9% accuracy).
We also had participants rate their confidence in a decision as confidence has been shown in a few studies to align with accuracy [27,28]. Participants’ confidence ratings and consistency decisions reflected a similar pattern. Overall, participants were moderately successful in judging the level of consistency, and they were moderately confident (4.1 to 4.8 range on a 7-point scale) in their decision response. The 48 participants appeared most confident in deciding thin consistency; ratings suggested less confidence in decisions about mildly thick consistency.
Findings of accuracy and decision confidence for mildly thick consistency are of clinical importance since it represents the most recommended level of beverage modification [5]. One consideration is that a mildly thick beverage requires relatively less thickening agent to achieve its target level of consistency. Recognizing the correct level of thickness may be more difficult for less experienced participants or preparers without more instruction, practice, or training. Participants’ inexperience might be further magnified by exclusive reliance on visual cues for accurate decisions and the sensitivity of vision as a modality for detecting textural changes [29]. Lastly, the beverage samples were imaged in a natural context (drinking cup). The type and amount of visual content may have limited participants’ ability to distinguish small changes in thickness [14].
The color of unthickened beverages or when mixed with a thickening agent appeared important to judging a level of consistency. Participants were most accurate with blue-colored water (76%) and least accurate with Fruit Punch/red Gatorade (40%). Beverage appearance (especially color) influences perceptions about clarity, intensity, and perceived quality [15,30,31,32]. The addition of a thickening agent can also change perceptions about beverage appearance and degree of liking [33]. The participants in this study perceived changes in consistency related to variations in color and beverage transparency or opaqueness based on their written comments. Careful consideration must be given to beverage selection in developing instructional resources to provide optimal visualization of consistency for correct application in patient care.

4.2. Preferences and Opinions About 2D/3D Visualizations

Most participants selected 3D models over 2D static images when asked to select a viewing preference. Their reasons aligned with stem-related findings such as the ability to manipulate and analyze objects from different angles [21,30]. Changing a point of view contributed to participants’ recognition of beverage attributes (e.g., “I preferred the 3D visual as I could move it and get all around a better view of the color and consistency”).
Contemporary or innovative technologies can contribute to learning motivation [13,34,35,36,37]. In this study, participants’ interest with models may have contributed to a 3D preference, but it did not translate to better performance. Inconsistent outcomes when comparing 2D and 3D methods have also been reported in the literature [21]. We did not ask participants about their familiarity or exposure to 3D technology, which may be a consideration as some learners sometimes report that 3D methods are complicated or frustrating to use [21]. Although innovative techniques may enhance motivation and interest, there may be other factors affecting performance [37]. For example, supportive outcomes for 3D methods often relate to medical students learning anatomy in which acquisition of knowledge is continually evaluated as part of an educational program [20,21]. The value of 3D methods for judging a degree of consistency for thickened and unthickened beverages may not yield the same level of engagement.
Overall, participants expressed a clear opinion regarding a preferred method of viewing (2D or 3D presentation) regardless of whether their judgments about consistency were accurate. This disconnect reinforces the importance of pairing any visual method with feedback about accuracy or self-assessment components that promote learning [13,37]. Altogether, these findings suggest that instruction on beverage consistency should balance clarity of presentation with interactive elements to support accurate identification of different consistencies.

4.3. Limitations and Future Considerations

The results must be examined considering possible limitations. One consideration is our sample size. To our knowledge, this is the first study to integrate 2D/3D methods for imaging beverage modifications, which made it difficult to estimate a sample size given the exploratory nature of the study. Although we recruited a reasonably large group for a within-subjects design, there is limit to our statistical power. It must be noted that our number of participants (n = 48) is higher than the minimum number suggested (n = 40) in prior simulation research conducted to determine the minimum number of consumers needed for determining differences in consumer studies [38].
Another consideration is the background experience of study participants. We intentionally recruited individuals who did not have exposure to practices with thickened beverages to minimize potential bias from their knowledge about levels of modifications or existing preparation patterns. As a result, our participants largely represented a convenience sample recruited from undergraduate courses at a single university. We recognize limited generalizability to patient care environments, and that the participants in this study may not accurately represent the population of care providers who routinely prepare thickened beverages as part of their everyday work. For example, beverage attributes like bubbles or differences in hue due to a texture change may take on different levels of importance. Future study with large numbers of healthcare professionals and care providers will validate applicability and application of findings to real-world context. However, in many cases, those providers also have limited knowledge or training with thickened beverages [5,7]. This study compared findings from 2D and 3D visual content. Based on qualitative information, drinking cup imaging of beverages may be more helpful with objective decisions about sample correctness like the appearance of “clumping” due to an inappropriately mixed sample. Although 3D technology could image a beverage in drinking cup, it could not be used to image the act of stirring or pouring. Incorporating other visual methods to show movement or shape cues more prominently may be more important to estimating consistency [14,39]. Of future consideration is imaging different visual content (beverage colors) with other thickening agents for comparative purposes.
As technology continues to advance, additional opportunities will become available for online delivery of quick and easy to access training modules [13]. The type of technology must be carefully selected for learning and engagement goals [40]. The visual methods in this study were exploratory and not a substitute for training, nor recommended for use by themselves. Although recognition of the correct thickness level typically exceeded 50% using the visualizations, mistakes still occurred. Because those mistakes can have clinical repercussions, it is essential that other forms of instruction must accompany visual content to improve proper preparation of thickened beverages for patients with dysphagia [12,13].

5. Conclusions

Contemporary technologies offer new ways of addressing quality control in the preparation of thickened beverages that may be prescribed when swallowing is impaired. This study was exploratory in terms of participants’ recognition of beverage consistency using 2D and 3D visual content. Although 3D was overwhelmingly selected as the preferred format for beverage consistencies, it yielded similar outcomes when compared to 2D visual content. Visualization techniques require additional study and consideration. Because participants were equally confident in correct/incorrect decisions, instructional efforts must provide systematic feedback to improve learning accuracy with any visual method.

Author Contributions

Conceptualization, J.M.G., A.B.A., M.U. and E.C.IV, Methodology, J.M.G., A.B.A., M.U., E.C.IV and D.R., Data curation, J.M.G., E.C.IV, A.B.A., M.U. and D.R. Writing—original draft preparation, J.M.G., Writing—review and editing, J.M.G., D.R. and E.C.IV. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study proposal (IRB-11698) was approved 05/31/2023 by the Committee on Research with Human Subjects at Kansas State University.

Informed Consent Statement

Each participant reviewed and received notice of informed consent.

Data Availability Statement

The original contributions in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

Hannah Meyer contributed to the development of 3D models.

Conflicts of Interest

The authors declare no conflicts of interest.

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Beverages 11 00141 g001
Figure 1. Two-dimensional images of blue-colored water samples representing thin, mildly thick, and moderately thick consistency from left to right.
Figure 1. Two-dimensional images of blue-colored water samples representing thin, mildly thick, and moderately thick consistency from left to right.
Beverages 11 00141 g001
Table 1. Percentage of correctly identified levels of consistency when judging 2D and 3D visual content.
Table 1. Percentage of correctly identified levels of consistency when judging 2D and 3D visual content.
Level of Beverage Consistency2D Static Image3D Virtual Model
Thin63.5%58.3%
Mildly Thick47.9%42.7%
Moderately Thick56.3%58.3%
Table 2. Mean rating of decision confidence (1 = Very Low to 7 = Very High) for each level of consistency and type of visual content.
Table 2. Mean rating of decision confidence (1 = Very Low to 7 = Very High) for each level of consistency and type of visual content.
Level of Beverage Consistency2D Static Image
(X ± SD)		3D Virtual Model
(X ± SD)
Thin4.8 (1.5)4.8 (1.5)
Mildly Thick4.4 (1.2)4.1 (1.1)
Moderately Thick4.6 (1.3)4.3 (1.3)
Table 3. Common themes and comments of participants who preferred 2D or 3D visual content.
Table 3. Common themes and comments of participants who preferred 2D or 3D visual content.
DimensionThemeDescriptionRepresentative Comment
2D Static Images
(preferred by 12 participants)		Beverage AttributesColor and lighting (transparency) for knowing consistency“I liked the 2D models because it was easier to see color differences between the levels of thickness.”
ConfidenceEasier to decide“2D because it was easier to see the liquid and I felt more confident with what I thought it was.”
Constant AngleSame view (no movement) made it easy to see and evaluate“2D—the 3D image seemed to change as it moved.”
3D Virtual Models
(preferred by 36 participants)		Beverage AttributesTexture (e.g., bubbles), color, and lighting (transparency) for consistency“3D, because you are able to see color change from the top and side as well as if the liquid is see through.”
ConfidenceEasier to decide“3D models just because it seemed like I could inspect them easier.”
Multiple AnglesManipulating the cup (e.g., zoom in/out, rotate) for different perspectives“The 3D visuals because you could see the drink from different perspectives and angles.”
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MDPI and ACS Style

Garcia, J.M.; Chambers, E., IV; Ukele, M.; Althauser, A.B.; Rehfeld, D. Exploring Visualization of Beverage Consistency Through 2D and 3D Imaging Methods. Beverages 2025, 11, 141. https://doi.org/10.3390/beverages11050141

AMA Style

Garcia JM, Chambers E IV, Ukele M, Althauser AB, Rehfeld D. Exploring Visualization of Beverage Consistency Through 2D and 3D Imaging Methods. Beverages. 2025; 11(5):141. https://doi.org/10.3390/beverages11050141

Chicago/Turabian Style

Garcia, Jane Mertz, Edgar Chambers, IV, Madison Ukele, Abby Brey Althauser, and David Rehfeld. 2025. "Exploring Visualization of Beverage Consistency Through 2D and 3D Imaging Methods" Beverages 11, no. 5: 141. https://doi.org/10.3390/beverages11050141

APA Style

Garcia, J. M., Chambers, E., IV, Ukele, M., Althauser, A. B., & Rehfeld, D. (2025). Exploring Visualization of Beverage Consistency Through 2D and 3D Imaging Methods. Beverages, 11(5), 141. https://doi.org/10.3390/beverages11050141

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