A design science framework for alternative proteins: coupling ingredient functionality, processing physics, and predictive modeling

1 Introduction

The transition toward alternative protein systems has accelerated markedly over the past decade, driven by sustainability imperatives, supply chain volatility, and shifting consumer expectations regarding environmental footprint, health, and animal welfare. Alternative proteins including plant-based, insect-derived, microbial, and cultivated sources have been proposed as sustainable solutions to meet rising global protein demand while reducing environmental burdens such as land use, water consumption, and greenhouse gas emissions (Lurie-Luke, 2024; Malila et al., 2024). However, widespread adoption remains constrained by challenges in nutritional quality, sensory fidelity, affordability, scalability, and regulatory clarity (Malila et al., 2024).

Despite significant advances in processing technologies, alternative protein development often remains empirical. Ingredients, process conditions, structural characterization, and predictive modeling are frequently treated as independent research domains rather than interdependent aspects of a unified engineering problem. This fragmentation limits mechanistic understanding of how formulation and process parameters co-determine final product structure, texture, and sensory experience (McClements and Grossmann, 2024). For example, although moisture content, shear history, and protein source are known to influence anisotropic structuring in meat analogues, systematic frameworks that integrate these factors into predictive design strategies are limited (Varayil and Mitra, 2026; Wilhelm et al., 2025; Zhang and Zhu, 2025; Zhang X. et al., 2024).

Plant and microbial proteins exhibit diverse denaturation, aggregation, and interfacial behaviors that influence hydration kinetics, gelation pathways, and phase separation during structuring processes (Brito-Oliveira et al., 2026; da Silva et al., 2024; Flory et al., 2023). High-moisture extrusion a dominant industrial structuring method combines thermal denaturation with shear-induced alignment to produce fibrous architectures, but many questions remain regarding the mechanistic pathways that govern anisotropy and network stabilization (Messina et al., 2025; Sägesser et al., 2025; See et al., 2025; Sui et al., 2024). Concurrently, fermentation technologies are being deployed to improve ingredient functionality, nutritional quality, and flavor profiles in alternative proteins (Coronel-León et al., 2026). Hybrid product design strategies, combining distinct protein sources such as plants, mycelia, and microbes, further complicate mechanistic understanding but present opportunities for improved performance and scalability (Kaplan and McClements, 2025).

Alternative protein matrices are increasingly viewed as multi-phase, multi-scale soft materials, with microstructural attributes such as anisotropy indices, porosity, and network connectivity linked to mechanical behavior, juiciness, and consumer perception (McClements and Grossmann, 2024). Advances in microscopy and image analysis have improved structural resolution, yet integration with predictive process models remains limited (Sui et al., 2024; Schreuders et al., 2022). Computational approaches including machine learning and physics-informed modeling are emerging as tools to optimize process parameters and predict textural outcomes (Li et al., 2024; Zhang et al., 2023). Digital twin concepts, although widely discussed in broader manufacturing contexts, are just beginning to be explored for food structuring and process optimization (Falayi et al., 2024; Peladarinos et al., 2023).

Ingredient variability adds another layer of complexity, as differences in crop cultivar, extraction methods, and protein purification strategies propagate through processing and affect final product performance (Akharume et al., 2021; Prajapati et al., 2023). Sustainability considerations further complicate optimization, as thermal inputs, energy intensity, and moisture management influence both structural development and environmental footprint (Malila et al., 2024; Detzel et al., 2022; Mercês et al., 2024).

Addressing these interconnected challenges requires a design-oriented perspective that explicitly connects ingredient functionality, processing physics, structure formation, and performance outcomes. To formalize this integrative perspective, the Ingredient–Process–Structure–Performance (IPSP) framework (Figure 1) organizes alternative protein development as a closed-loop, model-guided engineering system operating under variability and sustainability constraints. In this framework, ingredients function as tunable molecular building blocks; processing platforms act as controlled energy-delivery systems; structural architectures emerge through coupled physicochemical transformations; and performance outcomes encompass mechanical, sensory, nutritional, and stability attributes. Predictive modeling serves as the connective layer linking these domains, enabling rational experimentation, improved transferability, and scalable optimization.

Unlike traditional structure–function frameworks, IPSP explicitly incorporates predictive modeling as an active design layer and treats variability and sustainability as system-level constraints. Thus, IPSP is intended not only as a descriptive framework but as a model-guided design architecture for alternative protein development.

2 Literature search strategy

This review was developed through a structured search of the peer-reviewed literature using Web of Science, Scopus, and Google Scholar. Searches were conducted using combinations of keywords including alternative proteins, plant proteins, extrusion, ingredient functionality, protein structure, food processing, processing physics, machine learning, digital twins, predictive modeling, and food design. Publications published between approximately 2010 and 2026 were considered, with emphasis placed on recent advances while retaining seminal studies that established the foundations of the field. Articles were selected based on their relevance to ingredient functionality, processing mechanisms, hierarchical structure formation, predictive modeling, and sustainable design of alternative protein systems. Additional references were identified through citation tracking of key publications.

Preference was given to peer-reviewed original research articles, systematic reviews, and authoritative review papers reporting mechanistic insights or quantitative characterization relevant to alternative protein engineering. Publications lacking direct relevance to ingredient–processing–structure relationships or predictive design concepts were excluded. Because the objective of this article is to synthesize knowledge across multiple disciplines while proposing an integrative design framework, the manuscript is presented as a structured narrative review rather than a systematic review.

2.1 Positioning the IPSP framework within existing literature

The IPSP framework synthesizes concepts that have traditionally been investigated independently within food engineering and materials science. Previous research has established important relationships between ingredient functionality and structural development, processing conditions and microstructural evolution, and structure and product performance. However, these relationships have generally been examined within individual processing technologies or specific protein systems, limiting cross-platform integration and predictive design (McClements and Grossmann, 2024; Dekkers et al., 2018; Krintiras et al., 2014).

Evidence from high-moisture extrusion demonstrates that protein composition, hydration behavior, and thermal stability influence unfolding and aggregation, whereas shear, residence time, and cooling govern the development of anisotropic fibrous structures responsible for meat-like texture (Dekkers et al., 2018; Krintiras et al., 2014; Samard et al., 2021; Samard and Ryu, 2018). Likewise, fermentation-based approaches show that microbial metabolism, enzymatic hydrolysis, and acidification modify ingredient functionality prior to gelation, network formation, and sensory development (Coronel-León et al., 2026; Boukid et al., 2023). Studies of emulsification and interfacial engineering further illustrate how protein surface properties influence lipid domain organization, oxidative stability, and flavor release (Taghian et al., 2023; Fatima et al., 2025; Kakraba et al., 2026).

Building upon these established findings, the IPSP framework organizes existing knowledge into a unified systems-design architecture that explicitly links ingredient functionality, processing physics, hierarchical structure formation, predictive modeling, and performance outcomes. In contrast to conventional structure–function or process–structure–property approaches, IPSP additionally incorporates predictive modeling, digital twin technologies, uncertainty arising from ingredient variability, and sustainability constraints as interconnected design elements. Rather than introducing new physicochemical mechanisms, the framework provides an integrative perspective that facilitates comparison across processing platforms, identifies knowledge gaps, and supports the transition from empirical optimization toward predictive, model-guided engineering of alternative protein systems.

3 Ingredient functionality as the molecular design starting point

Within the IPSP framework, ingredient functionality represents the molecular starting point that defines the structural potential of alternative protein systems. Proteins derived from plants, fungi, algae, and microbial sources differ substantially in amino acid composition, tertiary structure, surface hydrophobicity, sulfhydryl reactivity, and association with non-protein components such as polysaccharides and lipids. These intrinsic properties determine solubility, hydration behavior, emulsification capacity, gelation potential, and responsiveness to thermal and mechanical energy inputs (McClements and Grossmann, 2024; Kyriakopoulou et al., 2021).

3.1 Protein structure and molecular architecture

Most plant proteins used in meat and dairy analogues such as soy globulins and pea legumin/vicilin fractions are predominantly globular in their native state. Compared to fibrous myofibrillar proteins, these globular proteins require denaturation and controlled aggregation to form extended networks capable of mimicking anisotropic muscle structure (Sui et al., 2024; Sood et al., 2026). Their compact architecture, stabilized by intramolecular disulfide bonds and hydrophobic interactions, influences unfolding temperature, aggregation pathways, and network formation under extrusion or shear processing (Taghian et al., 2023; Nisov, 2024).

Variations in amino acid composition also influence functional behavior. Proteins rich in hydrophobic residues exhibit stronger interfacial adsorption and emulsifying performance, whereas sulfhydryl group availability affects crosslinking potential during thermal treatment (Kyriakopoulou et al., 2021). In microbial and fungal proteins, cell wall components and polysaccharide–protein interactions introduce additional complexity, influencing water-binding capacity and mechanical behavior (Zahari et al., 2022). These compositional differences underscore that ingredient functionality is not uniform across alternative protein sources but highly source-dependent.

3.2 Solubility, hydration, and water–protein interactions

Water plays a central role in determining structural transitions in alternative protein systems. Solubility profiles govern dispersion stability and phase behavior, while hydration kinetics influence plasticization and mobility during processing. Proteins with limited solubility may exhibit premature aggregation, whereas excessive solubility can impede network formation under shear (McClements and Grossmann, 2024).

Hydration capacity also affects rheological properties, particularly in high-moisture extrusion systems where water content modulates viscosity and molecular mobility (Sui et al., 2024; Samaranayake et al., 2005). Protein–water interactions contribute to the development of continuous or dispersed phases, influencing anisotropy and fibrous structuring. Differences in isoelectric point and charge distribution further impact electrostatic repulsion and aggregation pathways, affecting gel strength and network connectivity (Baig et al., 2025; Riaz et al., 2026).

Understanding hydration and solubility as controllable functional parameters is essential for predictive process design. Without explicit characterization of water-binding behavior and dispersion stability, processing optimization becomes largely empirical.

3.3 Interfacial activity and emulsification

Many alternative protein products incorporate structured lipid phases to simulate juiciness and mouthfeel. Protein interfacial activity therefore plays a critical role in stabilizing oil–water interfaces and structuring fat domains. Surface hydrophobicity, molecular flexibility, and charge distribution influence adsorption kinetics and film formation at interfaces (McClements and Grossmann, 2024).

In plant-based meat analogues, emulsification capacity contributes to lipid domain size distribution, which in turn affects lubrication, flavor release, and perceived tenderness. Poor interfacial stabilization can lead to phase separation during processing or storage, compromising structural integrity (Boukid, 2021; De Angelis et al., 2024). Thus, interfacial functionality links directly to performance attributes such as juiciness and flavor retention.

3.4 Thermal responsiveness and gelation pathways

Thermal denaturation is central to structuring processes such as extrusion and shear cell processing. The temperature at which unfolding occurs, along with aggregation kinetics and crosslinking behavior, determines network architecture (Gulzar et al., 2025; Zhou et al., 2026). Controlled denaturation promotes the formation of interconnected protein networks, whereas uncontrolled aggregation can result in brittle textures or crumbly structures.

Gelation behavior is influenced by protein concentration, ionic strength, pH, and presence of co-solutes. Globular plant proteins typically form particulate gels unless shear-induced alignment is introduced to promote anisotropy (Aghagholizadeh and Rigi, 2025; Jiang et al., 2024). Fermentation can further modify gelation behavior by altering pH and promoting enzymatic hydrolysis, which affects molecular weight distribution and network continuity (Boukid et al., 2023; Fatima et al., 2025; Dhiman et al., 2025).

Understanding gelation kinetics and crosslinking mechanisms is therefore essential for designing processing conditions that yield targeted structural outcomes.

3.5 Ingredient variability and functional consistency

Ingredient variability remains a major challenge in alternative protein design. Differences in cultivar selection, growing conditions, extraction methods, and purification strategies introduce compositional heterogeneity that influences solubility, aggregation behavior, and rheological response (Kyriakopoulou et al., 2021). Even small variations in protein fraction composition can alter denaturation temperature and water-binding capacity, propagating variability through processing stages.

Such variability complicates scale-up and reproducibility. Without systematic characterization of functional parameters such as water absorption index, emulsifying activity, and gelation strength processing optimization often requires iterative reformulation (McClements and Grossmann, 2024). Integrating quantitative functionality metrics into predictive design frameworks is therefore essential to reduce empirical adjustment and improve transferability across batches and facilities. Table 1 summarizes the key ingredient functionality attributes synthesized from the literature discussed in Section 2.

TABLE 1. Key ingredient functionality attributes, measurable indicators, structural implications, and relevance within the IPSP framework.

3.6 Linking ingredient functionality to the IPSP framework

Within the IPSP framework (Figure 1), ingredient functionality serves as the initial state vector of the system and defines the boundary conditions governing subsequent processing-induced transformations. Molecular architecture, hydration behavior, interfacial activity, and thermal responsiveness collectively determine how a protein system responds to controlled energy input during processing. These functional attributes influence the feasibility of achieving anisotropic alignment, stable gel networks, and structured lipid domains.

A mechanistic understanding of ingredient functionality therefore establishes the foundation for rational processing selection and predictive modeling integration. Rather than treating proteins as interchangeable commodities, a design-oriented perspective recognizes them as tunable molecular systems whose intrinsic properties govern structural emergence and performance potential.

The functionality attributes summarized in Table 1 collectively define the initial design space of alternative protein systems. These measurable physicochemical parameters establish the operational domain within which processing physics, structure formation, and performance outcomes emerge. Consequently, ingredient functionality provides the starting conditions for the IPSP design loop and serves as a critical input for model-guided optimization and digital twin development. These ingredient functionality attributes establish the initial design space of the IPSP framework.

4 Processing physics and mechanistic structuring platforms

Within the IPSP framework (Figure 1), processing physics functions as the controlled energy- and mass-transfer layer that transforms ingredient functionality into structured material architectures. While alternative protein technologies are often categorized according to equipment platforms such as extrusion, shear cell processing, or fermentation, their structural outcomes are fundamentally governed by underlying physicochemical mechanisms. Consequently, a mechanistic classification provides greater predictive value than a technology-based taxonomy.

Processing platforms may be viewed as integrated physicochemical systems that regulate shear fields, thermal gradients, hydration dynamics, phase separation, and biochemical transformations. These drivers determine molecular unfolding, aggregation, alignment, and domain formation across multiple length scales. Within the IPSP framework, they constitute the primary process variables through which structural outcomes can be engineered. Understanding these mechanisms is essential for linking ingredient properties to structural and performance outcomes.

4.1 Shear and extensional flow–driven alignment

High-moisture extrusion remains the dominant structuring technology for fibrous meat analogues. During extrusion, proteins are subjected to simultaneous thermal denaturation and shear-induced deformation, leading to anisotropic alignment and phase separation (Guyony et al., 2023; Schmid et al., 2022). Specific mechanical energy input, moisture content, and die geometry influence viscosity gradients and extensional flow fields that promote domain orientation. These parameters function as controllable design variables that govern the degree of anisotropic structure formation.

Recent work emphasizes that fibrous structure formation is not solely a consequence of shear intensity, but of the coupling between shear, phase separation kinetics, and cooling profiles (Angonese et al., 2024; Dobson et al., 2023). Under appropriate thermomechanical conditions, protein-rich and water-rich phases separate and elongate along the flow direction, forming layered architectures that mimic muscle fiber alignment.

However, excessive shear or uncontrolled aggregation can result in brittle textures and loss of anisotropy. Mechanistic modeling of viscosity evolution and flow distribution has therefore become increasingly important for optimizing structuring conditions (Udugama et al., 2023; Eppinger et al., 2021). Within the IPSP framework, these models provide a quantitative link between process variables and the emergence of fibrous architecture.

Mechanistically, anisotropic structure formation results from the coordinated action of shear-induced molecular orientation, thermally induced protein unfolding, and phase separation under controlled cooling conditions (Dekkers et al., 2018; Krintiras et al., 2014; Wu et al., 2026). Shear and extensional flow promote the alignment of unfolded protein aggregates along the principal flow direction while simultaneously elongating protein-rich domains into continuous fibrous structures (Kakraba et al., 2026; Dobson et al., 2023; Gasparre et al., 2022). Concurrently, thermal denaturation exposes hydrophobic and reactive functional groups that facilitate intermolecular interactions, whereas controlled cooling stabilizes the aligned domains into continuous anisotropic networks (Malila et al., 2024; Taghian et al., 2023; Samard and Ryu, 2019a; Samard and Ryu, 2019b). Consequently, specific mechanical energy, moisture content, residence time, die geometry, and cooling profile function as interdependent design variables that collectively regulate anisotropic structure development, rather than acting as isolated processing parameters (Krintiras et al., 2014; Krintiras et al., 2015; Verboven et al., 2020).

Despite broad agreement that thermomechanical processing governs anisotropic structure formation, the relative contributions of individual mechanisms remain debated. Several studies attribute fiber formation primarily to shear- and extensional-flow–induced alignment of unfolded protein aggregates, whereas others emphasize thermodynamically driven phase separation followed by cooling-induced stabilization of protein-rich domains (Dekkers et al., 2018; Samard and Ryu, 2019b; Krintiras et al., 2015). These differing interpretations likely reflect variations in protein source, moisture content, extrusion configuration, and analytical methodologies used to characterize hierarchical structures. Consequently, establishing standardized structural descriptors and harmonized characterization protocols remains essential for comparing results across studies and developing predictive process–structure relationships.

4.2 Thermal denaturation and aggregation kinetics

Thermal transitions govern protein unfolding, aggregation, and network formation in alternative protein systems. During heating, native protein conformations lose structural stability, exposing hydrophobic regions, sulfhydryl groups, and other reactive sites that facilitate intermolecular interactions and crosslink formation. The extent of denaturation depends on protein source, molecular architecture, moisture content, heating rate, temperature profile, and residence time (McClements and Grossmann, 2024; Kyriakopoulou et al., 2021). Because these variables determine the availability of reactive molecular sites, thermal history represents a critical determinant of subsequent structural development.

From a process-engineering perspective, thermal denaturation can be viewed as a kinetic transformation governed by temperature-dependent reaction rates. Similar to microbial thermal inactivation and enzyme deactivation processes, protein unfolding and aggregation have been described using first-order, fractional conversion, and Arrhenius-type kinetic models that relate structural change to temperature exposure and processing time (Singh and Heldman, 2001; Van Boekel, 2008). Such approaches provide a quantitative framework for predicting the progression of denaturation and aggregation during thermomechanical processing operations.

In alternative protein manufacturing, thermal transitions rarely occur in isolation. Under extrusion and shear-based structuring conditions, denaturation proceeds simultaneously with shear-induced deformation, water redistribution, and phase separation. The interaction among these mechanisms determines whether proteins form continuous anisotropic networks, particulate gels, or heterogeneous aggregates (Wilhelm et al., 2025; Cornet et al., 2022). Consequently, thermal history functions not merely as a processing condition but as a controllable design variable governing structural emergence across multiple length scales.

Temperature gradients interact strongly with flow fields in high-moisture systems. Elevated temperatures increase molecular mobility and collision frequency, thereby promoting intermolecular aggregation and network development. However, excessive thermal exposure may accelerate uncontrolled aggregation, reduce molecular flexibility, impair molecular alignment, and diminish functional properties required for fibrous structure formation. Structural development therefore depends on balancing protein unfolding and aggregation within a defined processing window rather than maximizing thermal severity (Zhang et al., 2023; Dinali et al., 2024; Sun et al., 2023).

Residence time distributions further contribute to structural variability. Unlike batch thermal treatments, extrusion and continuous structuring systems expose different material elements to distinct thermal histories due to spatial variations in temperature, shear rate, and flow behavior. As a result, proteins within the same processing system may experience substantially different degrees of denaturation and aggregation, influencing network connectivity and textural uniformity (Verboven et al., 2020; Fogler, 1999). Incorporating residence time distribution concepts into predictive frameworks may therefore improve the ability to relate processing conditions to final structural outcomes.

Cooling profiles are equally important because structural locking occurs during solidification. As temperature decreases, molecular mobility is reduced and transient protein assemblies become stabilized into permanent network architectures. Rapid or non-uniform cooling may generate localized stress concentrations, heterogeneous phase distribution, or structural collapse, whereas controlled cooling promotes anisotropic organization and network continuity (Gasparre et al., 2022; Varayil et al., 2024). The coupling of heating and cooling dynamics therefore plays a central role in determining final product morphology.

Within the IPSP framework, temperature profile, heating rate, residence time, and cooling rate constitute the primary thermal control variables linking ingredient functionality to structural emergence. Mechanistic descriptions of denaturation and aggregation kinetics provide the foundation for predictive process modeling, scale-up, and digital twin development by enabling quantitative relationships between thermal history and network formation.

From a structural perspective, thermal history governs the balance between protein unfolding and aggregation, thereby determining the availability of reactive molecular sites for subsequent network formation. Moderate thermal exposure promotes controlled denaturation, increases molecular mobility, and facilitates intermolecular interactions required for continuous network development, whereas excessive heating accelerates irreversible aggregation and reduces molecular flexibility needed for shear-induced alignment (McClements and Grossmann, 2024; Samard and Ryu, 2019b). Because heating, residence time, and cooling act simultaneously during continuous processing, thermal variables should be regarded as coupled design parameters that regulate network connectivity, structural uniformity, and ultimately the mechanical properties of alternative protein systems (Krintiras et al., 2015; Verboven et al., 2020).

4.3 Phase separation and network formation

Alternative protein matrices frequently behave as multiphase systems in which protein-rich, water-rich, and, in some formulations, lipid-rich domains coexist. The spatial organization of these phases plays a central role in determining the structural architecture and mechanical performance of alternative protein products. Phase separation dynamics are influenced by protein concentration, moisture content, pH, ionic strength, temperature, and shear history, all of which affect intermolecular interactions and thermodynamic compatibility between system components (McClements and Grossmann, 2024; Dekkers et al., 2018; Jin et al., 2022; Stounbjerg et al., 2019).

During thermomechanical processing, proteins undergo unfolding, aggregation, and molecular alignment while simultaneously interacting with water and other biopolymers. Under appropriate conditions, these interactions promote the formation of distinct yet interconnected phases that subsequently elongate and orient under flow. In high-moisture extrusion systems, phase separation between protein-rich and water-rich domains is widely recognized as a key mechanism underlying fibrous structure development. The elongation and alignment of these domains along the flow direction generates anisotropic architectures that resemble the hierarchical organization of muscle tissue (Beniwal et al., 2021; Olivares et al., 2018; Quevedo et al., 2020).

The kinetics of phase separation are governed by the balance between thermodynamic driving forces and process-induced deformation. Parameters such as moisture content, temperature profile, shear intensity, and cooling rate influence domain size, morphology, and stability. Controlled phase separation promotes anisotropy, structural heterogeneity, and network continuity, whereas uncontrolled separation may generate weak interfacial regions, discontinuous structures, and textural instability (McClements and Grossmann, 2024; Drusch et al., 2021; Kumar and Raman, 2025). Consequently, phase behavior represents a critical design variable linking processing conditions to final product structure.

Network formation occurs concurrently with phase separation through protein aggregation, crosslinking, and domain stabilization. The interplay between aggregation kinetics and water redistribution determines porosity, connectivity, and mechanical integrity. Water-rich regions influence molecular mobility and plasticization, while protein-rich domains provide the structural framework responsible for strength and elasticity. The resulting network architecture governs fracture behavior, deformation mechanisms, and moisture retention properties (Frieden, 2007; Mezzenga and Fischer, 2013; Mukherjee et al., 2024).

Increasingly, quantitative microstructural descriptors are being used to characterize phase morphology and network organization. Metrics such as anisotropy index, pore size distribution, void fraction, tortuosity, network connectivity, and lipid domain distribution provide objective measures of structural architecture and facilitate comparisons across processing conditions and ingredient systems (Varayil and Mitra, 2026; Varayil et al., 2024). These descriptors also serve as important targets for predictive modeling and machine-learning approaches aimed at linking process variables with structural outcomes.

From an IPSP perspective, phase separation and network formation constitute the mechanistic bridge between processing physics and emergent material architecture. The ability to control domain formation, interfacial interactions, and network connectivity ultimately determines whether processing-induced transformations translate into desirable textural, sensory, and functional properties. Consequently, understanding phase behavior is essential for rational process design, scale-up, and digital twin development.

4.4 Fermentation-driven biochemical transformation

Fermentation introduces enzymatic and metabolic transformations that modify protein functionality prior to or during structuring. Unlike thermomechanical processes that primarily alter material organization through energy input, fermentation operates through biologically mediated reactions that reshape the physicochemical properties of proteins and associated matrix components. Through microbial metabolism, proteins may undergo hydrolysis, deamidation, acidification-induced conformational changes, and interactions with newly generated metabolites, thereby altering their functional behavior and structuring potential (Augustin et al., 2024; Dimidi et al., 2019; Sawant et al., 2025).

Proteolytic activity represents one of the principal mechanisms through which fermentation influences alternative protein systems. Enzymatic hydrolysis modifies molecular weight distribution, exposes reactive sites, and alters solubility, water-binding capacity, and gelation behavior. Controlled proteolysis may improve protein functionality and digestibility, whereas excessive hydrolysis can weaken network formation and reduce mechanical strength (Boukid et al., 2023; Dimidi et al., 2019). Consequently, fermentation conditions must be carefully optimized to balance functional enhancement with structural integrity.

Acidification constitutes a second major transformation pathway. Organic acids produced during microbial growth alter pH and electrostatic interactions, thereby influencing protein charge distribution, aggregation behavior, and phase separation dynamics. As proteins approach their isoelectric points, intermolecular interactions become increasingly favorable, promoting network formation and gel development. These pH-driven transitions play important roles in fermented dairy alternatives, mycoprotein products, and hybrid protein systems (Ashraf et al., 2026; Guo et al., 2025).

Beyond protein modification, microbial metabolism generates a diverse range of compounds that contribute to flavor, color, aroma, and nutritional quality. Organic acids, volatile compounds, pigments, vitamins, and bioactive metabolites influence consumer acceptance while simultaneously affecting physicochemical functionality. Fermentation can therefore serve as both a structuring and sensory optimization strategy, distinguishing it from purely physical processing approaches (Boukid et al., 2023; Senanayake et al., 2023; Yousefi et al., 2025).

Recent advances in precision fermentation have expanded the role of microbial systems beyond traditional ingredient modification. Engineered microorganisms can produce specific proteins, enzymes, fats, pigments, and flavor compounds that are subsequently incorporated into alternative protein formulations. Such approaches provide opportunities to tailor functional properties at the molecular level while reducing dependence on conventional agricultural protein sources (Malila et al., 2024; Ajayeoba and Ijabadeniyi, 2025; Singh and Kumar, 2025).

From an IPSP perspective, fermentation can be viewed as a state-modification process that alters the physicochemical boundary conditions under which subsequent structuring occurs. By changing molecular composition, charge distribution, aggregation propensity, and hydration behavior, fermentation influences the response of protein systems to shear, temperature, and phase-separation mechanisms. Hybrid processing platforms that combine fermentation with extrusion or shear-based structuring are increasingly recognized as promising strategies for improving texture, nutritional quality, and sensory performance (Boukid et al., 2023).

The effects of fermentation are inherently time-dependent and can be described using kinetic models of microbial growth, substrate utilization, metabolite production, and proteolysis. Integration of these kinetic descriptions with rheological and structural models represents an important opportunity for predictive process design and digital twin development. Within the IPSP framework, fermentation therefore functions not merely as a processing step but as a dynamic mechanism for engineering ingredient functionality prior to structural emergence.

4.5 Interfacial assembly and encapsulation mechanisms

In structured lipid systems, interfacial phenomena govern fat domain stability, flavor retention, oxidative resistance, and oral lubrication. The interfaces formed between proteins, lipids, water, and other biopolymers strongly influence the spatial organization of multiphase alternative protein matrices. Protein adsorption at oil–water interfaces stabilizes dispersed phases by reducing interfacial tension and creating viscoelastic interfacial films that inhibit droplet coalescence and phase separation (Han et al., 2025; Hong et al., 2023; Qiao et al., 2017).

The effectiveness of interfacial stabilization depends on protein molecular flexibility, surface hydrophobicity, charge distribution, and adsorption kinetics. These properties influence the formation of interfacial layers that determine droplet size distribution, emulsion stability, and lipid domain morphology. Because dispersed lipid phases contribute directly to lubrication behavior, interfacial assembly plays a central role in the development of mouthfeel, juiciness, and creaminess in alternative protein products (Cao and Mezzenga, 2020; Sarkar et al., 2019; Sarkar et al., 2025; Tan et al., 2025). The resulting lipid domain architecture including droplet size, spatial distribution, and interfacial composition constitutes an important structural determinant of lubrication behavior, flavor release, and oxidative stability.

Encapsulation technologies provide an additional level of structural control by regulating the localization and release of functional compounds. Proteins, polysaccharides, and composite biopolymer systems can be used to encapsulate lipids, flavors, pigments, micronutrients, and bioactive compounds. Encapsulation protects sensitive ingredients from oxidation and degradation while enabling controlled release during processing, storage, or oral breakdown (Augustin and Sanguansri, 2015; Martirosyan, 2025; Pinto et al., 2023). Consequently, encapsulation functions not only as a preservation strategy but also as a mechanism for engineering temporal sensory responses.

Interfacial phenomena are inherently dynamic and evolve throughout processing and storage. Thermal treatment, shear exposure, pH shifts, and enzymatic activity may alter interfacial composition and stability, thereby affecting droplet aggregation, coalescence, and release behavior. The interaction between interfacial assembly and other processing mechanisms including extrusion, fermentation, and phase separation remains incompletely understood despite its importance for product quality and stability (Long et al., 2025; Pawde and Dave, 2025; Yiasmin et al., 2026).

From an engineering perspective, interfacial assembly can be interpreted as a mechanism for controlling mass transfer and molecular partitioning within complex food matrices. Parameters such as droplet size distribution, interfacial composition, zeta potential, diffusion coefficients, and encapsulation efficiency provide quantitative descriptors that link processing conditions to sensory and stability outcomes. Within the IPSP framework, these parameters function as controllable design variables and provide critical inputs for predictive modeling and process optimization. Consequently, interfacial characteristics are increasingly incorporated into population balance models, diffusion models, and multiphase transport simulations used to predict emulsion stability, lipid domain evolution, and controlled release kinetics (McClements et al., 2009; Tolstoguzov, 2002; Wang L. et al., 2024).

From a mechanistic perspective, interfacial assembly regulates the spatial distribution and stability of dispersed lipid phases, thereby influencing lubrication behavior, flavor release, and oxidative stability. Protein adsorption kinetics, droplet size distribution, and interfacial composition collectively determine the evolution of lipid domains during processing and storage. Consequently, emulsifier selection, homogenization conditions, and encapsulation efficiency function as coupled design variables that control both structural organization and sensory performance, providing quantitative inputs for predictive modeling and process optimization (McClements and Grossmann, 2024; Tan et al., 2025; Pawde and Dave, 2025).

Within the IPSP framework, interfacial assembly and encapsulation mechanisms represent critical links between structure formation and performance outcomes. By governing lipid domain organization, flavor release kinetics, oxidative stability, and lubrication behavior, interfacial phenomena directly influence sensory perception and consumer acceptance. Integration of interfacial modeling with extrusion, fermentation, and digital twin architectures remains relatively underexplored. However, such integration offers a promising pathway for predictive control of lipid domain organization, flavor release, and sensory performance in next-generation alternative protein systems (Figure 2).

4.6 Mechanistic classification of processing platforms

To operationalize the processing landscape described above, Table 2 classifies alternative protein structuring technologies according to their dominant physicochemical drivers, structural outcomes, critical control variables, common failure modes, and representative modeling approaches. Rather than grouping technologies solely according to equipment configuration, this framework emphasizes the underlying mechanisms responsible for structure formation and performance development.

TABLE 2. Mechanistic classification of alternative protein structuring platforms.

The purpose of Table 2 is not merely descriptive but analytical. By organizing technologies according to dominant processing mechanisms, it becomes possible to identify common physicochemical principles across seemingly distinct platforms (Dekkers et al., 2018; Kyriakopoulou et al., 2021). For example, both extrusion and shear cell processing rely on the coupling of thermal transitions, flow-induced alignment, and phase separation, whereas fermentation primarily operates through biochemical modification and pH-mediated restructuring. Similarly, encapsulation and interfacial assembly share common mass-transfer and interfacial stabilization mechanisms despite being implemented through different technological routes.

This mechanistic perspective facilitates transferability across ingredient systems and supports integration with predictive modeling frameworks. When processing drivers are explicitly defined in terms of shear rate, temperature profile, hydration state, residence time distribution, interfacial properties, and biochemical transformation kinetics, they can be incorporated into constitutive equations, transport models, reaction-kinetic frameworks, and data-driven optimization approaches (Verboven et al., 2020; Koulouris et al., 2021; Suciu et al., 2021). Such parameterization provides a foundation for quantitatively linking process conditions to structural outcomes and functional performance.

Importantly, the mechanistic classification presented in Table 2 aligns directly with the processing physics layer of the IPSP framework. By identifying the dominant drivers responsible for protein unfolding, aggregation, alignment, phase separation, interfacial organization, and network stabilization, the framework establishes a common language through which diverse processing technologies can be compared, modeled, and optimized. This approach supports the development of transferable design principles that extend beyond individual equipment platforms and ingredient sources (McClements and Grossmann, 2024).

The inclusion of representative modeling approaches further highlights the transition from empirical process development toward model-guided design. Rheological constitutive models, computational fluid dynamics (CFD), reaction-kinetic models, diffusion models, population balance approaches, and physics-informed machine learning increasingly enable quantitative prediction of structure formation and process performance (Verboven et al., 2020). As digitalization strategies continue to evolve, these modeling frameworks are expected to play a central role in digital twin development, real-time process monitoring, and closed-loop optimization of alternative protein manufacturing systems.

4.7 Processing physics within the IPSP framework

Within the IPSP architecture (Figure 1), processing physics functions as the mechanistic layer that transforms ingredient functionality into emergent structural architecture. Through the controlled application of shear, thermal energy, hydration dynamics, phase separation, biochemical transformation, and interfacial assembly, processing conditions regulate protein unfolding, aggregation, alignment, and network formation across multiple length scales. The resulting structural features—including anisotropy, porosity, network connectivity, and lipid domain organization—provide the direct link between processing history and product performance. Consequently, the mechanistic drivers summarized in Table 2 establish the critical bridge connecting ingredient functionality to the multi-scale structure formation discussed in Section 4.

This mechanistic interpretation enables processing variables to be incorporated into predictive modeling frameworks, thereby supporting rational process optimization and digital twin implementation within the IPSP architecture. In practical applications, this enables processing parameters to be selected and optimized based on their predicted effects on structural descriptors and performance outcomes rather than by empirical trial-and-error alone. Processing physics therefore represents the second mechanistic layer of the IPSP architecture, providing the operational link between ingredient functionality and experimentally measurable structural evolution.

5 Multi-scale structure formation and quantification

Within the IPSP framework (Figure 1), structure formation represents the intermediate layer where ingredient functionality and processing physics converge to generate hierarchical material architectures. Alternative protein systems behave as complex, multiphase soft materials in which structural organization spans molecular, mesoscopic, and macroscopic length scales. The emergent mechanical and sensory properties of these systems are determined not only by composition but also by spatial organization, domain morphology, and network connectivity (McClements and Grossmann, 2024; Dekkers et al., 2018).

From an IPSP perspective, structural emergence can be quantified through a set of reproducible descriptors that link processing history to product performance. These descriptors include anisotropy, porosity, network connectivity, lipid domain organization, crosslink density, and moisture distribution. Together, they provide a quantitative bridge between ingredient functionality, processing conditions, and sensory-relevant outcomes. Consequently, structure formation represents the critical mechanistic layer through which molecular-scale transformations are translated into measurable product properties.

5.1 Molecular to mesoscopic transitions

Structure formation begins with molecular events triggered during processing. Thermal denaturation and shear-induced deformation disrupt native protein conformations, exposing hydrophobic regions, sulfhydryl groups, and other reactive sites that promote intermolecular association and aggregation. These molecular events propagate into mesoscale assemblies through protein–protein interactions, phase separation, and crosslink formation (Sui et al., 2024; Nasrollahzadeh, 2023). The kinetics of unfolding, aggregation, and network development determine whether proteins form continuous networks, particulate gels, or aligned fibrous domains.

Phase separation plays a central role in this transition. Under high-moisture extrusion conditions, protein-rich and water-rich phases separate and elongate along flow fields, generating anisotropic morphologies that resemble muscle-like architectures. The extent of domain elongation depends on viscosity ratios between phases, cooling rate, moisture content, and flow-field stability (Cornet et al., 2022; Rolandelli and Eeckhout, 2026). Inadequate control of phase behavior may result in heterogeneous pore structures, discontinuous domains, and weak interfacial regions that compromise mechanical integrity.

Viewed mechanistically, molecular unfolding, aggregation, and phase separation constitute the first stage of structural emergence. These processes establish the initial spatial organization upon which larger-scale architectures subsequently develop.

5.2 Hierarchical architecture, anisotropy, and porosity

Fibrous meat analogues seek to replicate the hierarchical organization of muscle tissue, characterized by aligned protein bundles, layered structures, and directional mechanical properties. Consequently, anisotropy is among the most important structural descriptors in alternative protein systems. Quantitative measures such as anisotropy indices, orientation distribution functions, and texture tensor analysis have been increasingly applied to characterize fiber alignment and structural orientation in plant-based materials (Dekkers et al., 2018; Angonese et al., 2023).

Beyond anisotropy, porosity provides an additional measure of structural organization. Parameters including pore size distribution, void fraction, tortuosity, and pore connectivity influence moisture mobility, fracture behavior, and mass transfer within the matrix. Highly porous structures may enhance water retention and flavor diffusion but can also reduce mechanical strength if network continuity is compromised. Conversely, dense architectures may improve strength while limiting moisture release and volatile transport (Joardder et al., 2023; Navarro, 2025).

Microstructural imaging techniques including confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), and X-ray micro-computed tomography (micro-CT) have substantially improved the ability to quantify anisotropy, porosity, domain morphology, and network organization. These descriptors provide objective metrics for comparing ingredient systems, processing conditions, and structural outcomes across studies (Varayil and Mitra, 2026; Varayil et al., 2024; Pulikkottil Rajan et al., 2024).

From a materials-science perspective, alternative protein matrices can be viewed as viscoelastic composites in which protein networks form the continuous phase and lipid or water domains function as dispersed inclusions. The resulting mechanical response is governed by phase continuity, interfacial adhesion, and crosslink density. Consequently, anisotropy and porosity emerge as key structural variables linking process-induced organization to functional performance (Lorenz and Köster, 2022; Robertson-Anderson, 2024).

5.3 Network connectivity and mechanical behavior

Network connectivity determines the extent to which structural elements form continuous load-bearing architectures. Protein aggregation, crosslink formation, and domain stabilization collectively contribute to network development, influencing elasticity, resilience, and fracture behavior. Variations in connectivity directly affect the transmission and dissipation of mechanical stress throughout the matrix.

Mechanical performance typically evaluated using texture profile analysis, compression testing, tensile testing, or dynamic rheological measurements reflects the underlying network architecture. Fracture stress, Young’s modulus, and deformation behavior correlate strongly with protein concentration, crosslink density, and anisotropic alignment (Damircheli and Moradi, 2025; Huda et al., 2025). Increased alignment generally enhances directional strength and fibrousness, although excessive orientation may reduce isotropic resilience (Chen et al., 2023; Liu et al., 2020; Oppen et al., 2024).

Water distribution further modulates mechanical behavior. Uniformly distributed water contributes to plasticization and elasticity, whereas localized water-rich regions may act as structural defects that reduce cohesiveness and mechanical stability. Moisture migration during cooling and storage can therefore alter microstructure and influence long-term performance (Kim et al., 2024; Torrejon et al., 2022).

Importantly, mechanical measurements provide experimentally accessible outputs that can be linked directly to structural descriptors and incorporated into predictive modeling frameworks. As a result, network connectivity, crosslink density, anisotropy, and moisture distribution serve not only as structural metrics but also as critical inputs for model-guided process optimization and digital twin development (Bock et al., 2019; Richert and Huber, 2020).

5.4 Structural determinants of sensory performance

Although structure formation is frequently evaluated through mechanical measurements, sensory perception depends on additional structural attributes that govern lubrication behavior, flavor release, and oral processing dynamics. Consequently, sensory quality emerges from the interaction between matrix architecture and mass-transfer processes occurring during mastication (Foster et al., 2011; Shewan et al., 2020).

Lipid domain size and spatial distribution play central roles in oral lubrication and perceived juiciness. Small, well-dispersed lipid domains can enhance lubrication and improve mouthfeel, whereas coalesced fat phases may generate greasiness, instability, or phase separation during storage (Mu and Chen, 2023; Tian et al., 2026). Interfacial organization further influences lubrication by controlling interactions between proteins, lipids, saliva, and oral surfaces (Prak et al., 2013; Scholten, 2017).

Flavor release is governed by diffusion and partitioning processes occurring within protein–lipid matrices. Structural characteristics such as crosslink density, porosity, moisture distribution, and interfacial composition influence volatile transport pathways and therefore determine both flavor intensity and temporal release behavior. Dense, highly crosslinked networks may restrict volatile migration and delay flavor perception, whereas porous architectures facilitate more rapid flavor release (Guichard et al., 2023; Seuvre et al., 2017; Verma et al., 2026).

Emerging research increasingly seeks to establish quantitative relationships between microstructural descriptors and sensory outcomes. Correlations between anisotropy and perceived fibrousness, porosity and juiciness, or lipid domain distribution and mouthfeel have been reported, although standardized methodologies remain limited (Dekkers et al., 2018). Establishing robust structure–sensory relationships remains a major challenge for predictive alternative protein design (Aken, 2022; Fu et al., 2023).

5.5 Quantification of structural architecture

Quantitative characterization is essential for translating structural observations into predictive design variables. Imaging approaches such as CLSM, SEM, and micro-CT provide measurements of anisotropy, pore size distribution, domain morphology, and network organization, while spectroscopic and thermal techniques including FTIR, DSC, and Raman spectroscopy can provide insight into molecular organization, aggregation state, and crosslink formation (Helmick et al., 2023; Rasheed et al., 2020; Wang et al., 2023).

Recent advances in image analysis, graph-based connectivity mapping, and machine-learning-assisted segmentation have improved the extraction of quantitative structural descriptors from complex food matrices. Metrics such as anisotropy index, network connectivity, pore size distribution, and lipid domain morphology are increasingly employed as quantitative indicators of structural organization and process performance (Sanfeliu et al., 2002; Shah et al., 2025).

Despite these advances, no consensus has emerged regarding standardized protocols for quantifying anisotropy, porosity, or network connectivity. Different imaging modalities, image-analysis algorithms, sampling strategies, and segmentation methods frequently generate structural descriptors that are not directly comparable across studies. This methodological variability limits cross-study comparisons, constrains meta-analyses, and reduces the transferability of predictive models. Establishing harmonized structural characterization protocols and standardized descriptor definitions therefore represents a critical prerequisite for robust model validation and the implementation of digital twin technologies (Wang et al., 2023; Sanfeliu et al., 2002; Shah et al., 2025).

Standardized structural descriptors are particularly valuable because they provide a common quantitative language linking ingredient functionality, processing conditions, hierarchical architecture, mechanical behavior, and sensory performance. Consequently, quantitative structural characterization forms the foundation for predictive modeling, process optimization, and digital twin implementation (Richstein and Schröder, 2024; Thelen et al., 2022).

5.6 Structural variability and reproducibility

Structural heterogeneity remains a major barrier to reproducible alternative protein manufacturing. Small variations in ingredient composition, hydration behavior, or processing history can propagate into substantial differences in anisotropy, porosity, network connectivity, and lipid domain organization. Such variability complicates process transferability and scale-up (de Moraes et al., 2023; Hefferon et al., 2023).

The establishment of standardized structural descriptors is therefore critical for improving reproducibility and enabling cross-study comparisons. Quantitative benchmarks for anisotropy, porosity, connectivity, moisture distribution, and lipid domain morphology would facilitate model validation and strengthen the predictive capabilities of emerging digital twin systems (Li et al., 2024; Vargas et al., 2026).

5.7 Structural layer within the IPSP framework

Within the IPSP architecture (Figure 1), structure formation represents the critical intermediary layer that translates ingredient functionality and controlled processing inputs into measurable performance outcomes. Processing-induced phenomena including unfolding, aggregation, alignment, phase separation, and network stabilization manifest as hierarchical architectures characterized by anisotropy, porosity, network connectivity, lipid domain organization, and moisture distribution.

These structural descriptors provide the mechanistic bridge between processing history and functional performance. Moreover, because they can be quantified experimentally and incorporated into predictive models, they form the foundation for the modeling frameworks discussed in Section 5. Consequently, multi-scale structural characterization is not merely a descriptive exercise but a central component of model-guided design, process optimization, and digital twin development for next-generation alternative protein systems (Ahmad et al., 2025; Zhu and Xu, 2022).

The relationships among structural descriptors, oral processing behavior, sensory perception, and performance outcomes are summarized in Figure 3.

5.8 Instrumental–sensory coupling and oral processing mechanics

While structural descriptors such as anisotropy index, porosity, and network connectivity provide quantitative insight into material architecture, translation into sensory perception requires consideration of oral processing dynamics and human response variability. Instrumental measurements, compression modulus, fracture stress, or viscoelastic parameters, capture bulk mechanical behavior under controlled deformation modes. In contrast, sensory perception reflects dynamic fracture, saliva incorporation, lubrication, bolus formation, and flavor release during mastication. Establishing robust links between structural metrics and perceived attributes therefore necessitates coupling materials characterization with sensory methodologies and oral mechanics (Ditschun et al., 2025; Liu et al., 2025; Zo et al., 2026).

Descriptive sensory analysis conducted with trained panels provides structured vocabulary and reproducible quantification of attributes such as fibrousness, firmness, cohesiveness, and juiciness. When aligned with structural descriptors, trained-panel data enable correlation between measurable architectural features and perceived intensity. For example, anisotropic alignment and directional fracture propagation are frequently associated with enhanced fibrous perception, whereas pore distribution and moisture retention contribute to perceived tenderness and succulence. However, consumer testing introduces additional dimensions, hedonic preference, familiarity, and expectation effects, that may not correlate linearly with instrumental mechanical properties. Integrating structural quantification with both trained-panel descriptors and consumer acceptance metrics strengthens predictive mapping between process-induced architecture and market performance (Basile et al., 2023; Prasad et al., 2025; Ramadhan et al., 2026).

Importantly, relationships between instrumental measurements and sensory perception remain inconsistent across alternative protein systems. Similar values of hardness, cohesiveness, or fracture stress do not necessarily translate into equivalent sensory experiences because oral processing is influenced by saliva incorporation, bolus formation, individual chewing behavior, lubrication, and product familiarity. Consequently, establishing transferable structure–sensory relationships requires integrating instrumental characterization with dynamic sensory methodologies and consumer evaluation rather than relying solely on bulk mechanical measurements (Basile et al., 2023; Prasad et al., 2025; Ramadhan et al., 2026; Pineau et al., 2015; Schlich, 2017; Dadmohammadi and Datta, 2022; Dai et al., 2026).

Temporal aspects of perception further complicate structure–sensory relationships. Texture and flavor evolve during oral breakdown as structural elements fracture and saliva infiltrates the matrix. Temporal dominance of sensations (TDS) and dynamic sensory profiling techniques have demonstrated that attributes such as firmness, juiciness, and flavor intensity change across mastication stages (Pineau et al., 2015; Schlich, 2017). Structural parameters including crosslink density, lipid domain distribution, and porosity govern fracture pathways and volatile diffusion, thereby shaping temporal sensory trajectories. Dense networks may delay breakdown and prolong firmness perception, whereas porous architectures accelerate moisture release and flavor diffusion (Dadmohammadi and Datta, 2022; Dai et al., 2026).

Oral tribology has emerged as a complementary instrumental approach for quantifying lubrication behavior under conditions approximating oral shear. Friction coefficients measured between soft, elastomeric surfaces in the presence of food matrices correlate with perceived creaminess, smoothness, and mouth-coating sensations (Pradal and Stokes, 2016; Sethupathy et al., 2021). In structured alternative protein systems, lipid droplet size distribution, protein adsorption at oil–water interfaces, and moisture availability influence boundary lubrication regimes. Incorporating tribological measurements alongside rheological and fracture analyses enhances mechanistic understanding of mouthfeel, particularly for hybrid systems containing structured fat phases (Sarkar and Krop, 2019; Stokes et al., 2013).

Satiety and oral breakdown kinetics represent additional dimensions of structure–performance coupling. The rate of structural disintegration and bolus formation influences oral processing behavior, oro-sensory exposure, and subsequent digestive responses. Although the satiety effects of alternative proteins remain an emerging research area, structural properties such as particle size distribution after mastication and matrix density may modulate gastric emptying and nutrient bioaccessibility. Integrating oral processing mechanics into predictive frameworks expands the IPSP architecture beyond static texture metrics toward dynamic physiological relevance (Pawde and Dave, 2025; He et al., 2022).

Within the IPSP framework, sensory evaluation functions not merely as a terminal validation step but as an integral feedback layer linking structural characterization with predictive modeling. Structural descriptors summarized in Table 3 provide quantitative predictor variables for trained sensory attributes, whereas tribological measurements and dynamic sensory profiling offer calibration targets for model development and validation. Integrating instrumental, sensory, and oral processing data within predictive frameworks enhances the transferability of laboratory observations to consumer perception and supports the development of robust digital twin systems for alternative protein engineering.

TABLE 3. Structural descriptors linking structure formation, performance outcomes, and predictive modeling within the IPSP framework.

5.9 Structural variability and reproducibility

Structural heterogeneity remains a major challenge for the industrial production and scale-up of alternative protein systems. Variability may originate from differences in ingredient functionality, including protein composition, molecular weight distribution, hydration behavior, and interfacial activity, as well as from fluctuations in processing conditions such as moisture content, temperature profiles, shear intensity, and residence time. Because structure formation is highly sensitive to these variables, relatively small changes in formulation or process history can propagate into substantial differences in anisotropy, porosity, domain morphology, and network connectivity (Muniz et al., 2024; Porto de Souza Vandenberghe et al., 2025).

Such variability directly impacts product quality, resulting in inconsistent texture, juiciness, flavor release, and mechanical performance. Furthermore, structural heterogeneity complicates cross-study comparisons and limits the transferability of processing strategies across ingredients, equipment platforms, and manufacturing scales. The challenge is particularly evident in extrusion-based systems, where small variations in thermal and shear histories can generate markedly different hierarchical architectures (Varayil et al., 2024; Sun et al., 2022).

Standardization of structural descriptors including anisotropy indices, pore size distributions, lipid domain metrics, and network connectivity parameters is therefore essential for improving reproducibility and enabling quantitative comparison across studies. The adoption of reproducible structural benchmarks would facilitate model validation, strengthen process transferability, and accelerate the development of predictive design tools (Le et al., 2012; Zhao et al., 2025).

Within the IPSP framework, these descriptors function as measurable structural state variables linking ingredient functionality and processing physics to performance outcomes. Embedding quantitative structural metrics within the IPSP feedback loop enables structure-informed ingredient selection, process optimization, and continuous model refinement.

5.10 Structural layer within the IPSP framework

Within the IPSP architecture (Figure 1), structure formation represents the critical intermediary layer that translates ingredient functionality and controlled processing inputs into measurable performance outcomes. While Table 2 identifies the dominant physicochemical mechanisms responsible for structural evolution, the present section demonstrates how these mechanisms manifest as hierarchical architectures characterized by anisotropy, porosity, network connectivity, lipid domain organization, and moisture distribution.

These structural descriptors provide the mechanistic bridge connecting molecular-scale transformations to macroscopic functionality and sensory perception. Importantly, because they can be quantified experimentally and monitored throughout processing, they serve as ideal variables for predictive modeling and process control (Pawde and Dave, 2025).

Consequently, multi-scale structural characterization is not merely a descriptive exercise but a foundational component of model-guided product design. The integration of quantitative structural metrics into predictive frameworks establishes the basis for the digital twin approaches discussed in Section 5, where ingredient–process–structure–performance relationships are translated into actionable design and optimization strategies.

6 Predictive modeling and digital twin integration

Within the IPSP framework (Figure 1), predictive modeling functions as the integrative core that connects ingredient functionality, processing physics, structure formation, and performance outcomes. Traditional development of alternative protein products has largely relied on empirical optimization through iterative experimentation. However, the increasing complexity of modern alternative protein systems including multiphase behavior, non-Newtonian rheology, coupled thermal–mechanical transformations, and hierarchical structure formation necessitates predictive and model-guided design approaches (McClements and Grossmann, 2024).

From an IPSP perspective, modeling serves as the mechanism through which ingredient–process–structure–performance relationships can be quantified, simulated, and optimized. Modeling approaches can be broadly categorized into three complementary domains: (i) first-principles physics-based models, (ii) constitutive and kinetic models describing structural evolution, and (iii) data-driven and hybrid modeling approaches that leverage machine learning and digitalization technologies.

6.1 First-principles modeling: transport and thermomechanics

First-principles models are based on the conservation of mass, momentum, and energy and provide the mechanistic foundation for understanding transport phenomena during alternative protein processing. In extrusion-based structuring systems, interactions among heat transfer, viscous dissipation, moisture transport, and non-Newtonian flow behavior govern protein unfolding, aggregation, and phase evolution (Cornet et al., 2022; Wang Y. et al., 2024).

Computational fluid dynamics (CFD) has been widely employed to predict velocity fields, shear-rate distributions, temperature gradients, and residence-time distributions within extrusion systems (Fatahıan et al., 2018; Norton et al., 2013). Such simulations provide valuable insight into local processing conditions that are difficult to measure experimentally and can identify regions associated with anisotropy development, phase separation, and structural heterogeneity (Zhang et al., 2023; Pawde and Dave, 2025).

Despite their mechanistic rigor, physics-based models remain computationally demanding and often require simplifying assumptions regarding multiphase flow behavior, interfacial interactions, and aggregation kinetics. Consequently, translating process-scale variables into experimentally measurable structural descriptors remains challenging. Nevertheless, first-principles models remain indispensable for equipment design, process scale-up, and mechanistic interpretation (Pawde and Dave, 2025; Fries, 2021).

6.2 Rheological and constitutive modeling

Alternative protein systems exhibit highly dynamic rheological behavior characterized by shear-thinning viscosity, viscoelasticity, yield stress phenomena, and time-dependent structural evolution. Because rheological properties strongly influence flow behavior and structural alignment, constitutive modeling represents a critical component of predictive process design (Chen et al., 2026; Jiang et al., 2025).

Generalized Newtonian fluid models, viscoelastic constitutive equations, and empirical rheological relationships have been applied to describe extrusion-relevant flow behavior (Allmendinger et al., 2014; Sun et al., 2024). These models facilitate prediction of pressure drop, flow stability, and shear alignment within processing equipment and can support optimization of moisture content, screw configuration, and die geometry.

A major challenge remains the discrepancy between laboratory rheological measurements and industrial processing conditions. Most rheological characterization is conducted under controlled laboratory environments that fail to replicate the complex temperature gradients, shear rates, and residence-time distributions encountered during commercial manufacturing. Bridging laboratory rheometry with in-line process rheology therefore represents an important research priority (Ahmad et al., 2025; Sha and Xiong, 2020).

6.3 Kinetic modeling of structural transitions

The transformation of ingredients into structured protein matrices involves multiple time-dependent processes, including protein denaturation, aggregation, crosslink formation, phase separation, and network stabilization. Kinetic models seek to describe these transitions as functions of temperature, moisture content, shear exposure, and residence time (Mezzenga and Fischer, 2013; Yiasmin et al., 2026).

In fermentation-based alternative protein systems, kinetic modeling has been used to describe proteolysis, acidification, molecular weight evolution, and gelation processes (Yousefi et al., 2025; Knychala et al., 2024). Such approaches provide mechanistic insight into strain selection, fermentation duration, and process optimization.

Although kinetic models can capture individual transformation pathways, integration with transport and rheological models remains limited. Developing unified multi-scale frameworks capable of linking molecular transformations to emergent structural descriptors remains a major challenge for predictive food engineering.

6.4 Multi-scale structure–property modeling

A central objective of the IPSP framework is the prediction of structural descriptors that connect processing conditions to product performance. Recent efforts have focused on establishing quantitative relationships between processing history and emergent structural characteristics, including anisotropy, porosity, network connectivity, lipid domain organization, and moisture distribution (Pawde and Dave, 2025; Oppen et al., 2024; Joardder et al., 2017).

Structure–property models seek to predict mechanical and sensory performance directly from structural measurements. For example, anisotropy has been associated with perceived fibrousness and directional fracture behavior, whereas porosity influences tenderness, water retention, and flavor release kinetics. Similarly, network connectivity and crosslink density have been linked to elasticity, chewiness, and structural resilience.

Advances in image analysis, graph-based network characterization, and machine-learning-assisted segmentation have enabled increasingly quantitative descriptions of food microstructure (Loesch et al., 2024; Todhunter et al., 2024). However, standardized methodologies for extracting structural descriptors remain limited, restricting model transferability across studies and processing platforms.

6.5 Data-driven and machine learning approaches

Machine learning (ML) methods have emerged as powerful tools for predicting quality attributes and optimizing processing conditions in alternative protein systems using complex, multivariate datasets. Regression algorithms, random forests, support vector machines, and artificial neural networks have been applied to predict texture, moisture retention, and sensory characteristics from ingredient composition and processing variables (dos Santos et al., 2025; Olagunju et al., 2025).

Compared with purely mechanistic approaches, data-driven machine learning models can efficiently capture nonlinear interactions among variables and identify hidden patterns within complex, multivariate datasets without requiring explicit constitutive equations. In extrusion-based systems, ML models have been used to predict texture profile analysis parameters and optimize process conditions for target product attributes.

Despite their growing application, purely data-driven machine learning approaches remain constrained by the limited availability, diversity, and standardization of experimental datasets. Most published models are developed using data generated from individual protein sources, specific formulations, or laboratory-scale processing conditions, limiting their transferability across ingredient systems, processing platforms, and industrial scales. Furthermore, variability in structural characterization methods, performance metrics, and experimental protocols complicates direct comparison among studies and hinders the development of generalized predictive models. These limitations indicate that improvements in predictive accuracy alone are insufficient; standardized datasets, harmonized structural descriptors, and independent model validation are equally essential for robust and transferable machine-learning applications in alternative protein engineering (Todhunter et al., 2024; dos Santos et al., 2025; Olagunju et al., 2025; Ellis et al., 2020; Himanen et al., 2019; Xue et al., 2015).

To overcome these limitations, hybrid approaches that combine physical constraints with machine learning algorithms have attracted increasing attention. Physics-informed machine learning integrates mechanistic understanding into data-driven architectures, thereby improving robustness, interpretability, and generalizability while reducing dependence on large experimental datasets (Verboven et al., 2020). Such hybrid approaches are expected to play an increasingly important role in predictive process optimization and future digital twin implementation for alternative protein manufacturing (Verboven et al., 2020).

6.6 Toward digital twin systems for alternative proteins

Digital twins represent virtual replicas of physical systems capable of real-time simulation, monitoring, prediction, and optimization. Within food manufacturing, digital twins have been proposed as tools for quality assurance, process optimization, sustainability assessment, and supply-chain integration (Helmy et al., 2024; Qian et al., 2022).

For alternative protein production, a digital twin would integrate four major information streams:

• Ingredient functionality metrics
• Processing conditions and transport phenomena
• Structural descriptors
• Performance targets and sustainability constraints (Koulouris et al., 2021; Helmy et al., 2024; Keohavong, 2026)

Within the IPSP framework, these information streams form a closed-loop architecture in which experimental measurements continuously update predictive models, while model outputs inform process optimization and product reformulation.

Although digital twin implementation remains in its infancy, advances in sensor technologies, process analytics, machine learning, and computational modeling are rapidly expanding its feasibility. Significant challenges remain, including real-time measurement of structural attributes, uncertainty quantification, model validation across ingredient variability, and integration of sensory performance metrics (Ge et al., 2025; Ling and Heldman, 2025).

6.7 Modeling as the integrative core of IPSP

Within the IPSP architecture (Figure 1), predictive modeling is not merely an auxiliary analytical tool but the central mechanism through which ingredient functionality, processing physics, structure formation, and performance outcomes are connected. While Table 2 identifies the dominant modeling approaches associated with different structuring platforms, the present section emphasizes their integration across scales and disciplines.

By coupling first-principles transport models, rheological constitutive equations, structural kinetic frameworks, quantitative structure–property relationships, and machine learning algorithms, alternative protein development can transition from empirical trial-and-error experimentation toward predictive co-design. The incorporation of experimentally measurable structural descriptors such as anisotropy, porosity, network connectivity, lipid domain organization, and moisture distribution provides the mechanistic bridge necessary for robust model development (Paluri et al., 2017; Sengar et al., 2023).

Ultimately, the integration of predictive modeling across ingredient, process, structure, and performance domains represents the critical enabling step toward digital twin–driven alternative protein manufacturing. Such systems have the potential to improve reproducibility, accelerate product innovation, enhance sustainability, and support scalable production of next-generation protein foods.

Despite substantial progress, important inconsistencies remain regarding the relationships among ingredient functionality, process-induced structural evolution, and predictive modeling across diverse protein sources and processing platforms. Differences in experimental methodologies, structural descriptors, and processing conditions continue to limit direct comparison among studies and hinder model generalization. Furthermore, standardized datasets, harmonized structural characterization protocols, and experimentally validated digital twin implementations remain scarce. These unresolved challenges underscore the need for integrated, evidence-based design strategies and provide the basis for the research priorities discussed in the following section.

7 Variability, scale-up, and sustainability constraints

Within the IPSP framework (Figure 1), variability, scale-up, and sustainability represent system-level constraints that influence every stage of alternative protein development. While ingredient functionality, processing physics, structure formation, and predictive modeling define the internal mechanisms governing product design, successful industrial implementation depends on the ability to manage compositional variability, maintain structural reproducibility during scale-up, and achieve economically and environmentally sustainable manufacturing. These factors are not external considerations evaluated after product development but fundamental boundary conditions that shape the behavior of the entire IPSP system (Porto de Souza Vandenberghe et al., 2025; Akdemir, , 2026; Malashin et al., 2024).

A central premise of the IPSP framework is that variability propagates through interconnected ingredient–process–structure–performance relationships. Consequently, successful scale-up and sustainability optimization require monitoring variables that capture structural evolution rather than relying solely on process conditions or endpoint product measurements. The structural descriptors summarized in Table 3 provide such measurable state variables and form the quantitative link between processing history, performance outcomes, and manufacturing robustness.

Despite growing recognition of these interconnected relationships, considerable variability remains in how ingredient functionality, structural descriptors, and processing outcomes are quantified across studies. Differences in experimental protocols, analytical techniques, and reporting practices continue to hinder cross-study comparisons and limit the development of transferable predictive models. Greater standardization of characterization methodologies will therefore be essential for translating laboratory-scale findings into robust industrial design strategies.

7.1 Structural descriptors as scale-up and sustainability metrics

Traditional process validation often relies on operational parameters such as temperature, moisture content, residence time, or specific mechanical energy. However, equivalent process settings do not necessarily produce equivalent structural outcomes. Alternative protein systems are particularly sensitive to small variations in ingredient composition, hydration behavior, and thermomechanical history, which can propagate into substantial differences in texture and sensory performance (Grossmann and Weiss, 2021; Kew, 2023; Khezerlou et al., 2026).

The structural descriptors summarized in Table 3 provide a mechanistic basis for evaluating process consistency and scale-up success. Unlike endpoint measurements alone, these descriptors quantify the emergent architecture generated by processing and therefore serve as transferable indicators across ingredients, equipment platforms, and production scales.

These descriptors provide a common quantitative language connecting ingredient functionality, process history, structural architecture, mechanical behavior, and sensory perception. Their adoption as standardized metrics would substantially improve reproducibility, facilitate cross-study comparisons, and support digital twin calibration.

7.2 Ingredient variability and functional drift

Plant-, fungal-, algal-, and microbial-derived proteins exhibit substantial variability arising from genetics, cultivation conditions, harvesting practices, extraction methods, and purification processes. Differences in protein composition, molecular weight distribution, residual lipid content, mineral profile, and particle morphology can significantly alter hydration behavior, interfacial activity, thermal responsiveness, and rheological performance (Amiri et al., 2026; Cao et al., 2025; Karim et al., 2024).

From an engineering perspective, ingredient variability represents uncertainty in the initial state vector of the IPSP system. Functional attributes established at the ingredient level determine how proteins respond to thermal, mechanical, and biochemical processing inputs. Consequently, variability introduced at the ingredient stage propagates through processing physics and ultimately manifests as changes in anisotropy, porosity, network connectivity, lipid organization, and moisture distribution (Pawde and Dave, 2025; Joardder et al., 2017).

Without quantitative characterization of ingredient functionality, predictive models risk overfitting to narrow compositional windows and may fail when raw material characteristics change. Standardized measurement of ingredient functionality should therefore be incorporated into adaptive manufacturing strategies and uncertainty-aware modeling frameworks. Moreover, standardized reporting of ingredient functionality would facilitate independent validation of predictive models and improve reproducibility across laboratories, processing platforms, and protein sources.

7.3 Process scale-up and thermomechanical sensitivity

Scaling alternative protein production from laboratory or pilot systems to industrial manufacturing introduces substantial challenges because thermomechanical conditions rarely scale linearly. Changes in screw geometry, barrel diameter, throughput rate, cooling capacity, and residence-time distribution alter the shear fields and thermal histories responsible for protein unfolding, phase separation, and alignment (Ahmad et al., 2025; Porto de Souza Vandenberghe et al., 2025; Röder et al., 2022).

High-moisture extrusion illustrates the sensitivity of structure formation to scale-dependent process conditions. Fiber development depends on coupled interactions among shear, hydration, temperature, and cooling kinetics. Consequently, maintaining identical process settings across scales does not guarantee equivalent anisotropy, porosity, or network architecture (Deng et al., 2023; Dou et al., 2022; Hu et al., 2025).

Computational fluid dynamics and transport modeling provide valuable tools for predicting these scale-dependent changes (Schlangen et al., 2025; Szpicer et al., 2025; Zink et al., 2024). However, successful scale-up should ultimately be evaluated using structural descriptors such as anisotropy index, pore size distribution, and network connectivity rather than relying solely on process variables or texture measurements.

7.4 Sustainability trade-offs and resource efficiency

The sustainability advantages commonly attributed to alternative proteins depend not only on ingredient origin but also on processing efficiency. Although plant-based protein systems generally demonstrate lower greenhouse gas emissions, land use requirements, and freshwater consumption than conventional animal-derived proteins, these advantages may be partially offset by energy-intensive processing operations, extensive purification requirements, or inefficient resource utilization (Aubin et al., 2025; Javourez et al., 2024; Vågsholm et al., 2020).

Sustainability trade-offs are often nonlinear. Increased moisture content may facilitate fibrous structure development and improve sensory quality while simultaneously increasing thermal energy requirements. Similarly, highly purified protein ingredients may improve processability and structural consistency but require additional water, energy, and processing steps.

Life cycle assessment studies consistently demonstrate favorable environmental performance of plant-based proteins relative to conventional livestock systems, particularly with respect to greenhouse gas emissions and land use (Grant and Hicks, 2018; Shanmugam et al., 2023; Varela-Ortega et al., 2022). Nevertheless, maximizing sustainability benefits requires optimization of energy consumption, water utilization, process integration, and heat recovery throughout the manufacturing chain.

Embedding environmental metrics directly within predictive modeling frameworks enables multi-objective optimization in which structural quality, sensory performance, process efficiency, and environmental impact can be evaluated simultaneously.

7.5 Economic feasibility and manufacturing robustness

Commercial viability depends on the ability to achieve consistent product quality at competitive production costs. Ingredient variability, yield losses, structural instability, and process inefficiencies all contribute to manufacturing risk (Khanashyam et al., 2025; Kumar et al., 2023; Quintieri et al., 2023).

Advanced monitoring systems, machine-learning architectures, and digital twin technologies offer opportunities to improve process consistency and reduce waste. However, implementation requires investment in sensors, computational infrastructure, and model maintenance. The economic value of these technologies depends on their ability to improve product consistency, reduce energy consumption, increase yield, and shorten development cycles.

Manufacturing robustness therefore emerges as a key performance objective within the IPSP framework. Robust systems should maintain target structural and sensory properties despite fluctuations in ingredient composition and operating conditions. Achieving this capability requires integration of compositional analytics, structural monitoring, predictive modeling, and adaptive process control.

7.6 Variability and sustainability within the IPSP loop

Within the IPSP architecture, variability and sustainability constitute overarching constraints that influence all internal interactions. Ingredient variability alters molecular functionality, scale-dependent effects modify processing physics, structural heterogeneity influences performance outcomes, and environmental considerations constrain optimization objectives (Szpicer et al., 2025; Etzbach et al., 2024).

By embedding variability quantification, structural monitoring, and sustainability metrics within a unified feedback architecture, IPSP enables a transition from reactive reformulation toward anticipatory optimization. Such integration provides the foundation for uncertainty-aware digital twins and adaptive manufacturing systems capable of maintaining product quality under changing raw material and operational conditions.

Ultimately, successful industrialization of alternative proteins will depend not only on advances in ingredients and processing technologies but also on standardized characterization, transferable predictive models, and coordinated integration of computational and experimental approaches. Rather than replacing existing structure–function or process–structure paradigms, the IPSP framework provides an integrative systems perspective that connects these established concepts within a unified design architecture. Its primary contribution lies in organizing current knowledge, identifying methodological gaps, and supporting the future development of predictive, scalable, and sustainable alternative protein manufacturing (Hefferon et al., 2023; Wood and Tavan, 2022).

8 Research roadmap and future directions

The preceding sections position alternative protein development as a coupled, multi-scale engineering challenge in which ingredient functionality, processing physics, structural architecture, predictive modeling, and sustainability constraints interact dynamically. Although substantial progress has been achieved within individual research domains, integration across scales remains incomplete. Advancing from empirical optimization toward predictive, transferable, and scalable design will require coordinated progress in structural standardization, cross-scale modeling, uncertainty quantification, adaptive process control, and sustainability-aware optimization.

A foundational limitation is the absence of universally accepted structural descriptors. While anisotropy indices, pore size distributions, lipid domain metrics, and network connectivity analyses are increasingly reported, significant variability remains in their definitions, quantification methodologies, and reporting standards (Krintiras et al., 2014; Zink et al., 2024; Garina et al., 2026). This lack of harmonization limits reproducibility, complicates cross-study comparisons, and restricts model validation across ingredient systems and processing platforms. Establishing consensus methodologies for fiber alignment quantification, phase morphology characterization, and network connectivity mapping would provide stable structural benchmarks that support predictive modeling, digital twin calibration, and process transferability (Muniz et al., 2024; Kumar et al., 2026; Mu’azzam et al., 2024).

A second challenge concerns the translation of laboratory-scale rheological characterization into industrially relevant structuring environments. Rheological measurements are frequently performed under steady-state or oscillatory conditions that do not adequately represent the complex shear histories, transient thermal gradients, and residence time distributions encountered during high-moisture extrusion or shear-cell processing (Porto de Souza Vandenberghe et al., 2025; Röder et al., 2022; Alasi et al., 2024; Barrios-Rodríguez et al., 2026). Consequently, constitutive equations derived from bench-scale experiments often fail to capture structural evolution under manufacturing conditions. Future research should focus on integrating rheological characterization with computational flow-field simulations and validating predictions against quantitative microstructural descriptors rather than relying solely on mechanical performance measurements. Such integration would strengthen the mechanistic linkage between processing physics and hierarchical structure formation.

More fundamentally, many of the transformations governing structure formation including protein denaturation, aggregation, crosslinking, phase separation, and molecular alignment occur simultaneously under non-isothermal and spatially heterogeneous conditions. Existing modeling approaches frequently treat these phenomena independently despite their intrinsic coupling. Progress toward predictive structuring will therefore require integrated multi-scale frameworks capable of coupling molecular transition kinetics with heat transfer, moisture transport, and momentum transfer processes. Embedding aggregation kinetics, phase behavior models, and network formation mechanisms within computational fluid dynamics simulations represents a critical step toward scale-aware prediction of anisotropy development, porosity evolution, and network connectivity (Verboven et al., 2020; Yiasmin et al., 2026; Durand et al., 2002).

The rapid adoption of machine learning within food process engineering has substantially expanded predictive capabilities. Nevertheless, purely data-driven approaches often exhibit limited robustness when extrapolated beyond their training domains, particularly in systems characterized by substantial ingredient variability and process uncertainty (Ling and Heldman, 2025; Malashin et al., 2024). Hybrid modeling approaches that integrate physical constraints within machine-learning architectures offer a more promising pathway. Physics-informed neural networks, surrogate models constrained by transport equations, and mechanistically guided learning frameworks have the potential to improve predictive accuracy while maintaining interpretability. Such approaches are particularly attractive for alternative protein systems where ingredient variability is an intrinsic characteristic rather than an experimental artifact (Kaplan and McClements, 2025; Jiang et al., 2025; dos Santos et al., 2025).

A closely related research priority involves uncertainty quantification. Most existing predictive models generate deterministic outputs despite substantial uncertainty associated with ingredient composition, processing conditions, measurement systems, and model assumptions. Future IPSP implementations should incorporate probabilistic modeling approaches, Bayesian inference frameworks, and uncertainty propagation analyses capable of quantifying confidence intervals around structural and performance predictions. The integration of uncertainty-aware modeling is essential for reliable deployment of digital twins under industrial operating conditions and for establishing confidence in model-based decision making (Malila et al., 2024; Bryant and Noé, 2024; Ding and Dokholyan, 2005; Nguyen et al., 2022).

Digital twin implementation further depends on the availability of real-time structural monitoring. Current high-resolution characterization techniques including micro-computed tomography, confocal laser scanning microscopy, and advanced imaging methods remain largely offline analytical tools. Future progress will require the development of in-line spectroscopic measurements, moisture distribution sensors, process rheology diagnostics, and soft-sensor architectures capable of inferring structural evolution during processing (Marr et al., 2020; Vega, 2016). When coupled with predictive models, such sensing technologies could enable adaptive control of shear rate, thermal input, cooling profiles, and moisture distribution to maintain target anisotropy, porosity, and network integrity throughout manufacturing.

Sustainability considerations introduce an additional layer of complexity. Although life cycle assessments consistently demonstrate environmental advantages of plant-based protein systems relative to conventional livestock production, energy-intensive structuring operations and extensive ingredient fractionation may offset some upstream gains (Detzel et al., 2022; Upadhyay et al., 2026). Consequently, sustainability should not be treated as a post hoc assessment but rather as an integral design constraint. Embedding environmental indicators including energy consumption, water utilization, greenhouse gas emissions, and process efficiency within predictive modeling architectures would enable multi-objective optimization in which structural quality, sensory performance, economic feasibility, and environmental impact are evaluated simultaneously. Such integration transforms the IPSP framework from a performance-oriented model into a systems-level engineering architecture.

Beyond predictive modeling, future IPSP implementations may support inverse or generative design strategies. Rather than predicting outcomes from predefined formulations and processing conditions, inverse-design frameworks would begin with target performance attributes such as fibrousness, juiciness, nutritional quality, or environmental footprint and computationally identify ingredient combinations and processing pathways capable of achieving those objectives. The convergence of generative artificial intelligence, physics-informed optimization, and digital twin technologies presents an opportunity to accelerate product development while reducing experimental burden.

Ultimately, advancing toward adaptive and resilient manufacturing systems will require coordinated progress in compositional analytics, structural characterization, uncertainty quantification, cross-scale modeling, and digital process control. Ingredient variability and supply-chain fluctuations are persistent features of plant-, microbial-, and fungal-derived protein systems; therefore, future predictive architectures must accommodate uncertainty rather than assume homogeneity. The IPSP framework provides a scaffold for such adaptation by positioning modeling as the integrative core linking molecular functionality, processing physics, structural evolution, and performance outcomes under environmental and economic constraints.

By aligning structural standardization, uncertainty-aware modeling, real-time sensing, and sustainability-constrained optimization within a unified systems perspective, alternative protein engineering can evolve from empirically guided product development toward predictive, adaptive, and ultimately autonomous manufacturing. In this context, the IPSP framework functions not only as a conceptual model but also as a roadmap for the development of digitally enabled, resilient, and scalable alternative protein production systems. The major research priorities and future opportunities emerging from the IPSP framework are summarized in Table 4.

TABLE 4. Priority research directions for IPSP-enabled alternative protein engineering.

Looking forward, advances in artificial intelligence, multimodal sensing, and autonomous process control are expected to extend the IPSP framework beyond predictive design toward AI-guided closed-loop manufacturing. By continuously integrating real-time compositional, structural, and process data with digital twin models, future manufacturing systems may dynamically optimize processing conditions, compensate for raw material variability, and simultaneously improve product quality, process efficiency, and sustainability. Such adaptive manufacturing strategies represent a promising pathway toward resilient, scalable, and sustainable next-generation alternative protein production.

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Footnotes

Edited by: Alev Yüksel Aydar, Manisa Celal Bayar University, Türkiye

Reviewed by: Ma. Fabiola León-Galván, Universidad de Guanajuato, Mexico

Jonghyeok Shin, Sejong University, Republic of Korea