AI food formulation uses predictive models to screen ingredients and rank likely formulas before bench work, helping R&D teams drop poor candidates early. For high-protein RTD beverage development, it narrows the trial list, but taste, heat stability and scale-up still need laboratory and pilot validation. Predictive ingredient screening ranks candidate proteins, minerals and stabilisers from measured data, so teams can reduce formulation trials rather than remove them. The shortlist is a hypothesis; the bench and the pilot plant determine whether it becomes a commercially viable formula.
Can AI Reduce Food Formulation Trials? Using Predictive Ingredient Screening to Optimise High-Protein RTD Beverages Before UAE Pilot Production
Dr. Shreya Iyer, PhD (Food Science) — Director of New Product Development, Food Research Lab
Specialization: Food formulation, beverage development, and culinary innovation
Quick Answer
AI food formulation uses predictive models to screen ingredients and rank likely formulas before bench work, helping R&D teams drop poor candidates early. For high-protein RTD beverage development, it narrows the trial list, but taste, heat stability and scale-up still need laboratory and pilot validation. Predictive ingredient screening ranks candidate proteins, minerals and stabilisers from measured data, so teams can reduce formulation trials rather than remove them. The shortlist is a hypothesis; the bench and the pilot plant determine whether it becomes a commercially viable formula.
1. Why High-Protein RTD Beverages Are a Combinatorial Formulation Problem
Consider a UAE brand developing a vanilla high-protein RTD. The brief may specify a protein level, calcium fortification, viscosity, ambient shelf life, sensory targets and a cost ceiling. On paper, these look separate. In the beverage matrix, they interact.
That is the core problem of functional beverage product development:
Protein source × concentration × pH × minerals × stabiliser × hydration × shear × heat × flavour → beverage performance |
Changing one variable alters several others. Even the protein blend alone behaves non-linearly: in a 2026 Journal of Dairy Science study, heat coagulation time fell as the whey proportion rose, while controlled modification of the whey improved heat stability under the study conditions. [1]
1a. Can AI really reduce food formulation trials?
The commercial question is narrower than “can AI replace the laboratory?” It is whether a model can rank candidates well enough that fewer physical trials are needed to reach a viable formula. The value lies in deciding which experiments are worth running first, which is how AI food formulation can reduce formulation trials. Whether it does so depends on the data behind the model and on what is measured afterwards; Sections 2, 3 and 8 take those in turn. [2]
1b. What predictive screening can and cannot replace?
Screening reduces the candidates entering physical development. It cannot replace measurements that depend on the actual formulation, process and storage: whether a beverage stays homogeneous after heating, holds particle size in storage, delivers the intended mouthfeel or behaves at pilot scale. Section 5 explains why. [2]
2. How Predictive Ingredient Screening Works
A predictive workflow begins with paired data: what went into the formulation and what happened to it. Inputs are ingredient functionality, composition and process variables; outputs are measured outcomes such as solubility, viscosity, particle size, sedimentation or heat stability. [3]
Define target profile ➜ assemble relevant data ➜ screen candidates ➜ rank risk/performance ➜ select experiments ➜ validate physically
2a. Ingredient functionality data and machine learning models
Model usefulness depends on the quality and relevance of its ingredient functionality data. Useful variables for protein systems include:
- Protein solubility and hydration behaviour
- Particle characteristics and viscosity
- Mineral composition and pH response
- Thermal response and aggregation tendency
- Concentration and blend ratio
Context matters as much as the value: a solubility figure without its pH, temperature, concentration or method transfers poorly to another system. That is why machine learning in food R&D must cope with variable, processed ingredients whose supplier, processing history and lot shift behaviour. Feasibility is shown for narrow tasks: a 2026 study predicted plant-protein solubility and emulsifying activity index from structural characteristics such as surface hydrophobicity, zeta potential and undenatured protein content [3]. For AI-assisted high-protein RTD formulation services, the practical implication is to train around the formulation domain under investigation, not to treat a model as a universal predictor.
2b. Solubility, emulsification and gelation prediction
Protein functionality is not a single property. A protein can dissolve well yet make a poor beverage through its interaction with minerals, stabilisers or heat, and an ingredient that emulsifies well may behave differently at high loading. Protein ingredient functionality prediction therefore treats solubility, emulsification, gelation, viscosity and aggregation as separate targets, each needing a measurable endpoint; “good functionality” is not one. In silico screening can then help shift the question from “Which ingredient is best?” to “Which combinations suit this product target?” For AI food formulation, the value is in narrowing the candidate space before physical formulation trials. [3]
3. Applying AI Screening to High-Protein RTD Beverages
3a. Protein selection — solubility, heat stability and mouthfeel
Whey, casein-rich, milk protein concentrate and plant proteins differ in functionality across pH and process, and acidic and neutral-pH systems impose different demands on solubility and thermal stability. Protein type and concentration also shape sensory outcome (Section 4) [4]. A screen built on protein ingredient functionality prediction can rank candidates on solubility and heat stability of proteins, eliminating those poorly aligned with the product profile before physical trials begin.
3b. Hydration kinetics, mixing order and shear
Hydration is not simply adding powder to water. Wetting, dispersion, hydration time, mixing order and shear shape the particle population entering downstream processing, and poor dispersion produces undissolved material, localised aggregation and undesirable texture. A protein that performs under one hydration sequence may not under another, so a screen should include process variables wherever data exist.
3c. Heat-induced denaturation, aggregation and sedimentation risk
Thermal processing is where many high-protein RTDs fail. Heating exposes reactive groups in whey proteins and promotes protein–protein interactions that change particle size, viscosity and heat coagulation.
A 2026 Journal of Dairy Science study heated acidified whey protein isolate (pH 3.5) at 90 °C for 10 minutes in a 10,000-rpm high-shear mixer, then tested it in systems with 8% and 10% total protein and three casein-to-whey ratios. At 10% protein and 50:50 casein-to-whey, heat coagulation time rose from 0.8 to 2 minutes with the modified protein [1].
The lesson is not to copy these conditions, but that protein pre-treatment, ratio and thermal history can materially change heat stability. A screen can flag combinations needing closer thermal testing; the laboratory shows whether they stay within specification. [5]
3d. pH, minerals and ionic environment — why the same protein behaves differently
Protein functionality shifts with the ionic environment, and calcium fortification makes this concrete. A 2026 Food Research International study of 8% protein MPC dispersions compared calcium chloride, calcium gluconate and calcium casein phosphopeptide, measuring pH, zeta potential, calcium-ion activity, heat coagulation time, sedimentation and particle size. Calcium chloride and gluconate reduced heat and ethanol stability, whereas the phosphopeptide caused a smaller decline [6]. Any protein ingredient functionality prediction must therefore treat “calcium” as several variables: source, concentration, protein system and matrix. For UAE products positioned around fortification, nutritional and physical-stability targets must be screened together.
3e. Stabiliser and sweetener system shortlisting
Stabilisers affect viscosity, suspension, mouthfeel and protein-particle interactions, and a system that suspends well before heating may behave differently after it. Sweeteners and flavours also change perceived thickness. For beverage formulation, AI can rank stabiliser systems against historical data, but interactions are formulation-specific, so the final choice is confirmed experimentally.
3f. Combining AI with design of experiments (DoE)
AI and design of experiments (DoE) work sequentially:
AI screening ➜ reduce candidate space ➜ DoE ➜ bench experiments ➜ measured response ➜ model refinement
AI identifies a manageable region of the formulation space; DoE structures the physical experiments to measure key variables and interactions efficiently [2]. For functional beverage product development teams, this keeps AI food formulation a front-end design tool rather than a replacement for formulation science.
4. Protein Loading vs Mouthfeel, Chalkiness, Astringency and Flavour
Raising protein improves nutrition but changes the sensory profile. The 2021 study compared 6.3% and 10.5% protein RTDs and found higher protein increased astringency and sensory viscosity and reduced vanillin flavour, while higher serum-protein isolate content increased sulfur/eggy flavour; chalkiness and graininess appeared late in storage in one blend [4]. A formulation therefore cannot be optimised on protein content alone. The target is the required protein level inside an acceptable rheological, sensory, thermal-stability and storage-stability window, which makes high-protein RTD beverage development a multi-response problem.
5. Limits of AI: Data Quality, Sensory and Real-World Validation
AI food formulation changes where uncertainty is managed; it does not remove it.
Data quality and coverage
Machine learning in food R&D is only as relevant as its data. A dataset dominated by one protein, supplier, process or pH range may not generalise to a UAE RTD with a different lot, mineral system or thermal process; A dataset dominated by one protein, supplier, process or pH range may not generalise to a UAE RTD with a different lot, mineral system or thermal process. Model reliability therefore depends on dataset quality, input representativeness, validation strategy and experimental confirmation [3].
Sensory complexity
Sensory outcomes resist reduction to one number: a beverage can have acceptable viscosity and excessive astringency, or good stability and poor flavour release. A 2025 systematic review found texture prediction has drawn less research than taste, and odour lacks standardised metrics, with robust datasets still needed [7]. Sensory prediction models should support panels, not replace them.
Thermal processing and storage
Stability is process-dependent: heating profile, residence time, shear history and cooling all matter, and storage adds aggregation, sedimentation and flavour change that ingredient tables do not capture.
“AI can predict risk. Laboratory testing generates evidence. |
6. FRL’s “AI Narrows, Lab Proves” Hybrid Formulation Workflow
FRL’s approach rests on one principle:
“AI narrows the experimental search space. The laboratory proves what survives. |
The aim is to remove low-value combinations before they consume bench time and ingredients. Every beverage formulation screened through our AI-assisted high-protein RTD formulation services is validated physically before a formula is locked. Screening ranks candidates and flags risk; it does not confirm a final formula, sensory quality or pilot behaviour, so the laboratory decides what moves forward.
The workflow has four stages:
1 | Brief • Protein level, source or blend, and pH • Mineral fortification and texture target • Sensory target and thermal process • Shelf life and cost • UAE market requirements and intended GCC expansion |
2 | Screen Screening ranks candidate proteins, mineral systems, stabilisers and combinations from available functionality data. Low-probability candidates are deprioritised. |
3 | Prove Shortlisted formulas enter physical testing, using the sequence in Section 7a. Where practical, DoE structures the runs. |
4 | Confirm The best-performing formulation progresses toward pilot-scale production. Bench results feed the next screening cycle. |
Table 1. AI vs Lab Validation
Formulation question | What AI can help with | What laboratory validation must establish |
Protein selection | Rank candidates from functionality data | Dispersion, hydration, viscosity, sensory response |
Mineral compatibility | Flag sensitive protein–mineral pairs | pH, ionic environment, thermal response |
Heat stability | Prioritise candidates with lower predicted risk | Aggregation, sedimentation, coagulation under defined processing |
Sensory performance | Find historical formulation–sensory relationships | Trained and consumer evaluation |
Storage/pilot readiness | Flag combinations needing closer assessment | Stability over time, behaviour on representative equipment |
Table 2. High-Protein RTD Formulation Interaction Map
Protein source | Minerals/pH | Stabiliser | Hydration | Heat process | Sensory outcome |
Whey-rich | pH and ions influence protein interactions | Affects viscosity, suspension | Controlled wetting and dispersion | Denaturation and aggregation affect stability | Astringency, viscosity, flavour |
Casein/MPC | Calcium balance and pH influence micelle stability | Modifies viscosity, physical stability | Hydration history affects dispersion | Depends on minerals and process | Higher protein adds body, viscosity |
Plant protein | pH influences solubility, dispersion | Often needed for suspension, mouthfeel | Hydration and shear affect texture | Heating alters aggregation | Flavour, mouthfeel vary with source and processing |
Protein blend | Ratio and matrix set interactions | Combined stabilisation may be needed | Mixing sequence shapes particle population | Proteins respond differently to heat | Can balance or amplify attributes |
Figure 1. FRL’s “AI Narrows, Lab Proves” Hybrid Workflow
Do not validate only the model's top-ranked formulation: include a control or a mid-ranked candidate, then compare predicted order with measured response. Two habits protect a shortlist. Treat the powder lot as a variable: record each incoming lot's dispersion pH and repeat a quick heat challenge before trusting an earlier ranking, since the lowest-pH powders were the ones that failed in the sedimentation study above. And fix the addition order: hydrate the protein fully before adding minerals, because a ranking made under one sequence may not survive another.
Plant-Protein RTD: What Bench Data Reveal Before Predictive Screening
Context: FRL developed a plant-based complete-nutrition RTD beverage using plant protein, dietary fibre, healthy fats, micronutrient premixes, stabilisers, natural flavours and sweeteners, evaluated across four flavour variants.
Approach: The bench programme covered ingredient evaluation, flavour and texture optimisation and manufacturing feasibility. It found variable plant-protein dispersion, higher viscosity with dietary fibre and a need for more intensive homogenisation for certain ingredients. This project did not use predictive screening; these are the kinds of measured variables a screen would need.
✓ All four variants scored above 8.0 on the 9-point hedonic scale (V1: 8.7)
✓ Uniform protein dispersion, smooth texture and good physical stability
✓ Dispersion, viscosity and homogenisation data recorded as formulation variables
✓ Laboratory results remain the decision gate for any AI-ranked formula
Source: Food Research Lab, Inside the Lab, Plant-Based Complete Nutrition Beverage (22 July 2026).
Small-Scale Heat-Stability Screening
Context: Heat stability is where many milk protein beverages fail, and pilot runs on unstable products are costly.
Approach: A 2025 Journal of Dairy Science study (Hargrove, Barbano & Drake) heated samples in sealed stainless-steel tubes, then measured sedimentation by centrifugation and particle size by laser light scattering, testing eight commercial MPC85 powders [5].
✓ Two powders, both with the lowest pH, showed poor heat stability
✓ The authors concluded the method is a practical screen that could avoid costly pilot runs on unstable products
✓ It is a laboratory method, not an AI model
✓ A predictive screen chooses candidates; a test like this shows whether they survive heat
Source: Journal of Dairy Science, 108(10), 10574–10585 [5].
FORMULATION INTELLIGENCE
Reduce formulation iterations with AI-assisted high-protein RTD formulation services
7. From Shortlist to UAE Pilot Production
Once the shortlist passes laboratory screening, the question changes from “Which formula should we test?” to “Can this formula be manufactured consistently?”
7a. Bench validation, stability checks and scale-up readiness
Bench validation confirms what the screen cannot establish for a given beverage formulation. For high-protein RTD beverage development, the sequence is:
- Confirm the shortlist at bench scale, including dispersion, viscosity and pH
- Apply a thermal challenge and check sedimentation and particle size
- Hold samples in storage and re-measure, alongside sensory evaluation
- Repeat the best formula to show it is reproducible
A 2025 Journal of Dairy Science study showed how small-scale heat-stability testing can screen milk protein beverages before pilot production. Samples were heated in sealed stainless-steel tubes, then sedimentation and particle size were measured; two of eight commercial MPC85 powders, both with the lowest pH, showed poor heat stability, so the authors described the method as a practical screen that could avoid costly pilot runs. [5]
7b. UAE pilot-scale constraints — ingredients, processing, cost and distribution
A UAE pilot introduces constraints that may not appear in a predictive dataset. Supplier consistency must be weighed alongside formulation performance, and the pilot must reproduce mixing, homogenisation and thermal conditions closely enough to show scale-up behaviour, so a bench formula may need process adjustment. Ingredient cost and availability also decide which shortlisted proteins remain viable: a top-ranked formula that misses the cost ceiling is not a candidate. Storage testing should represent the intended UAE supply chain, not a generic laboratory condition.
Regulatory review belongs before pilot approval. MoIAT maintains the UAE framework of laws, technical regulations and standards [8]. For regulated beverage products, applicable requirements can include accredited laboratory testing, food-safety management documentation, food-contact compliance, labelling, transport and storage conditions, and conformity documentation [9]. Ingredient, additive and nutrition-claim requirements should be verified against the applicable UAE/GSO requirements for the specific product category before pilot production. For a UAE launch with GCC expansion, review against UAE requirements first, then map the product to each intended GCC market.
8. When Predictive Screening Saves Trials — and When It Does Not
Predictive screening is most useful when: • Relevant historical data exist, and ingredient functionality is measured • The target response is clearly defined • Many combinations are possible, and the goal is to reduce formulation trials • Laboratory results can feed model refinement |
Rely on the laboratory first when: • Ingredients are novel or poorly represented in the data • Supplier or lot variability is substantial • Fortification changes the ionic environment, or the process differs from the training data • Thermal or storage behaviour is uncharacterised • Sensory acceptance is the main decision criterion |
The practical workflow is: Predict → Shortlist → Formulate → Measure → Optimise → Pilot → Verify
AI should sit inside the formulation workflow, not above it. For UAE functional beverage product development, that is how machine learning in food R&D reduces expensive investigation while laboratory evidence remains the decision gate. [2]
Conclusion
AI food formulation can narrow the formulation search space by ranking ingredients, identifying potential compatibility risks and prioritising experiments. However, laboratory and pilot testing remain essential to confirm hydration, stability, sensory performance, processing behaviour and scale-up readiness. Through its AI-assisted high-protein RTD formulation services, Food Research Lab connects predictive ingredient screening, bench formulation, physical validation and pilot-scale production to help develop stable, scalable and commercially relevant high-protein RTD beverages.
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Frequently Asked Questions
Yes, in many cases. Predictive screening can rule out unlikely ingredient combinations before they reach the bench, so R&D teams test a shorter, better-ranked list. It narrows the options rather than replacing experiments, because the final formula still must be confirmed physically.
Models learn from measured ingredient data, such as protein solubility, emulsifying and gelation behavior, to predict how a candidate will perform in a target beverage. Developers use those predictions to shortlist proteins and stabilizers for heat stability and mouthfeel.
AI models are only as good as the data behind them, and predicting human taste and texture perception remains difficult. Performance under heat, shear and storage is also not fully modeled, so expert judgment and real-world testing remain essential.
Yes. Shortlisted formulas should be validated at bench scale for stability, sensory quality and processing behavior before pilot runs. This confirms the AI predictions and avoids costly scale-up failures.
References
- Roy, S., & Amamcharla, J. (2026). Increasing the thermal stability of high-protein beverages through the modification of whey proteins through thermal processing. Journal of Dairy Science, 109, 4915–4926. https://doi.org/10.3168/jds.2025-27874
- Navarrete-González, R., López-Malo, A., Palou, E., & Ramírez-Corona, N. (2026). A comparative study of theoretical model-based optimization and experimental design approaches for functional beverage formulation. Sustainable Food Technology, 4(1), 947–960. https://doi.org/10.1039/d5fb00480b
- Mandal, R., Malvar, S., Chandra, R., & Ismail, B. P. (2026). A machine learning approach to predict functional performance from measurable protein structural characteristics: A screening tool for protein ingredient quality. Proteins, 94(8), 1458–1484. https://doi.org/10.1002/prot.70130
- Vogel, K., Carter, B., & Cheng, N. (2021). Ready-to-drink protein beverages: Effects of milk protein concentration and type on flavor. Journal of Dairy Science, 104, 10640–10653. https://doi.org/10.3168/jds.2021-20522
- Hargrove, D., Barbano, D., & Drake, M. (2025). A sedimentation test to measure heat stability of milk protein beverages. Journal of Dairy Science, 108, 10574–10585. https://doi.org/10.3168/jds.2025-26974
- Yuan, B., Liu, Y., Wan, N., Liu, D., Zhao, Y., Tian, C., Huppertz, T., & Zhou, P. (2026). Heat stability of high-protein calcium-fortified milk protein concentrate dispersions: Impact of different calcium salts. Food Research International, 227, 118299. https://doi.org/10.1016/j.foodres.2025.118299
- Gunning, M., & Tagkopoulos, I. (2025). A systematic review of data and models for predicting food flavor and texture. Current Research in Food Science, 11, 101127. https://doi.org/10.1016/j.crfs.2025.101127
- Ministry of Industry and Advanced Technology. (n.d.). Laws and legislation. United Arab Emirates. MoIAT – Laws and Legislation
- Ministry of Industry and Advanced Technology. (n.d.). List of technical requirements for regulated products—UAE Conformity Assessment System (ECAS). United Arab Emirates. MoIAT – Regulated Product Requirements
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