Advancements in Intelligent Thawing Systems for Protein‐Based Foods: An Industry 5.0 Perspective

ABSTRACT

Freezing is essential for maintaining the stability of the global supply chain for protein‐based foods such as meat and aquatic products. However, traditional thawing technologies suffer from low efficiency and severe quality deterioration. Aligned with the principles of Industry 5.0, next‐generation food thawing increasingly prioritizes intelligence, human‐centricity, sustainability, and resilience. This paper systematically examines the entire technological chain from freezing pretreatment to intelligent integration. Rather than providing a technology‐by‐technology summary, this review establishes a progressive analytical framework that connects freezing pretreatment, thawing strategies, intelligent systems, and Industry 5.0 enabling technologies. It first discusses freezing pretreatment strategies that establish a quality foundation for subsequent thawing, then reviews individual thawing technologies and synergistic thawing strategies through critical evaluation of their mechanisms, technological advantages, limitations, evidence quality, and industrial applicability. It further analyzes the architecture of intelligent thawing units, including multidimensional sensing networks, intelligent control, and digital twins, and highlights their role as the digital infrastructure for adaptive and data‐driven thawing systems. Then it further discusses the roles of Industry 5.0 enabling technologies in building human‐centric, sustainable, and resilient thawing systems. These technologies include augmented reality, collaborative robots, low‐code platforms, blockchain, edge–cloud collaboration, and closed‐loop feedback optimization. Finally, core challenges and future directions are outlined. This review proposes a whole‐chain optimization framework from freezing pretreatment to intelligent thawing systems and provides critical insights into the transition toward Industry 5.0‐oriented food processing.

1. Introduction

Protein‐based foods such as meat and aquatic products are indispensable to human diets, providing high‐quality protein, amino acids, unsaturated fatty acids, and trace elements (Zhang et al. ^2023a). However, due to population growth, increasing demands are being placed on the integrity of these supply chains. Freezing is the most popular long‐term preservation technique and effectively extends shelf life by suppressing microbial growth and enzyme activity (Çalışkan Koç et al. 2025; Im et al. ²⁰²⁴). However, ice crystals formed and distributed at freezing negatively affect the water‐holding capacity, texture and microstructure of thawed products (Im et al. 2024). Traditional air and water thawing techniques are slow and often result in severe drip loss, protein denaturation, and lipid oxidation, leading to economic losses and food waste (Tian et al. 2025; Zhang et al. ^2023a).

Figure 1 illustrates the trajectory of industrial revolutions. From Industry 1.0 driven by steam engines in the late 18th century, to Industry 2.0 powered by electricity in the early 20th century, to Industry 3.0 enabled by computers and automation in the late 20th century, and to Industry 4.0 supported by the Internet of Things (IoT) and cyber‐physical (CP) systems in the early 21st century, each revolution has reshaped manufacturing (Zafar et al. 2024). Nowadays, Industry 5.0 is gaining global recognition, advocating three core value dimensions built upon Industry 4.0: human‐centricity (technology should empower rather than replace humans), sustainability (resource efficiency and environmental friendliness), and resilience (systems capable of adapting to fluctuations, disruptions, and uncertainties; Rahman, Khatun, et al. ²⁰²⁴; Zafar et al. ²⁰²⁴). Next‐generation thawing technologies should therefore contribute to these three objectives simultaneously. In this review, Industry 5.0 is adopted as the analytical perspective for evaluating technological development, with emphasis on how different thawing technologies contribute to human‐centricity, sustainability, and resilience rather than being discussed as an independent concept.

FIGURE 1.

The technology evolution chart of the five industrial revolutions, reference from Rahman, Khatun, et al. (2024).

In recent years, intelligent thawing technologies have advanced significantly. The following keywords were used for retrieval in the Web of Science (WOS) Core Collection on June 8, 2026, and the annual number of publications, H‐index, and the sum of total citations are presented in Figure 2.

FIGURE 2.

Publications, H‐index, and total times cited pertaining to the topic of intelligent food thawing technologies, as published in the Web of Science (WOS) from 2000 to 2026 (data retrieved from WOS on June 8, 2026).

TS = ((“Industry 5.0” OR “Industrial 5.0” OR “artificial intelligence” OR “machine learning” OR “deep learning” OR “internet of things” OR “real‐time monitoring” OR “digital twin” OR “human‐center*” OR “Cobot*” OR “blockchain” OR “cyber‐physical system” OR “augmented reality” OR “extended reality” OR “low‐code” OR “edge computing” OR “cloud‐edge”) AND (“thaw*” OR “defrost*” OR “temper*” OR “recovery*” OR “freez*”) AND (“food” OR “meat” OR “fish” OR “aquatic” OR “beef” OR “mutton” OR “seafood” OR “protein‐based food”)).

As shown in Figure 2, the number of relevant publications increased steadily from 2008 to 2026, with a notable acceleration after 2020 and a particularly marked rise from 2024 to 2025, indicating the rapid development of this field in recent years. Meanwhile, both the H‐index and the sum of total citations have shown a consistent upward trend since 2020, suggesting a simultaneous increase in academic attention and research quality in this area. Although numerous studies have reported advances in freezing, thawing, and intelligent food processing technologies, existing reviews mainly focus on individual technologies or specific processing stages. A critical review that systematically connects freezing pretreatment, thawing technologies, intelligent thawing systems, and Industry 5.0‐oriented enabling technologies within a unified analytical framework remains lacking. To address this gap, this review follows a progressive analytical framework rather than a technology‐by‐technology description. The discussion progresses from freezing pretreatment to individual thawing technologies, synergistic thawing strategies, intelligent thawing systems, and finally Industry 5.0 enabling technologies. This organization reflects the technological evolution from process optimization to system intelligence and ultimately to the realization of human‐centric, sustainable, and resilient thawing systems. Specifically, the review first examines how freezing pretreatments, including physical field‐assisted, medium‐assisted, chemical, and physical pretreatments, establish an appropriate ice crystal foundation for subsequent high‐quality thawing. It then critically compares individual thawing technologies with respect to their mechanisms, industrial applicability, technological maturity, and evidence quality. Subsequently, synergistic thawing strategies are discussed as approaches for overcoming the intrinsic limitations of individual technologies. Intelligent thawing systems, including multidimensional sensing, intelligent decision‐making, and digital twins, are then presented as the digital infrastructure supporting intelligent thawing. Building upon these technological foundations, Industry 5.0 enabling technologies are discussed from the perspectives of human‐centricity, sustainability, and resilience. Finally, current challenges, knowledge gaps, and future research priorities are critically summarized.

2. Advanced Freezing Technologies

Freezing, as a prerequisite to thawing, fundamentally determines the quality attributes of thawed protein‐based foods, such as water‐holding capacity, texture, and microstructure (Im et al. 2024). Traditional slow freezing technologies result in large ice crystals which can cause severe mechanical damage to muscle microstructures, whereas advanced freezing technologies create many small and uniform ice crystals inside and outside muscle cells, thereby minimizing structural damage (Im et al. 2024). Table 1 illustrates the mainapplication cases of advanced freezing technologies.

TABLE 1.

Summary of representative studies on advanced freezing technologies for protein‐based foods.

Technology category	Specific technology	Sample material	Key freezing parameters	Main findings	Advantages	Limitations	References
Physical field‐assisted freezing	Ultrasound‐assisted freezing (UF)	Large yellow croaker	180 W, 20 kHz/28 kHz/40 kHz single/dual/multifrequency	Multifrequency UF significantly increased freezing rate, reduced thawing loss, maintained stable protein secondary/tertiary structure	Fine and uniform ice crystals, low lipid oxidation, stable protein structure	/	Bian et al. (2022)
	UF	Chicken breast	0.25 W/cm2 specific power, 30 kHz	UF had smallest ice crystals during 180‐day storage, lowest thawing/cooking loss, quality comparable to IF‐90d/air freezing (AF)‐60d	Excellent quality retention during long‐term storage, minimal water migration	/	Zhang, Li, et al. (2022)
	UF	Large yellow croaker	200 W, 25 kHz, 30s on/45s off, −25°C coolant	UF significantly delayed WHC, color, texture deterioration during multiple freeze–thaw cycles, smaller/regular ice crystals	Good tolerance to multiple freeze–thaw cycles, low‐protein oxidation	/	Li, Wang, et al. (2023)
	High‐voltage electrostatic field‐assisted freezing (HVEFF)	Pork loin	10 kV/m/30 kV/m/50 kV/m	10 kV/m optimal: freezing time reduced by 40.04%, thawing loss decreased from 5.7% to 1.7%, highest umami signal	High freezing efficiency, low thawing loss, good flavor retention	/	Hu et al. (2022a)
	Low‐voltage electrostatic field‐assisted freezing (LVEFF)	Pork	2500 V, 0.2 mA, 50 Hz, storage at −18°C/−38°C for 5 months	LVEF inhibited water migration, reduced thawing loss by 15.97% (−18°C) and 3.38% (−38°C), miniaturized ice crystals	Small ice crystals, reduced protein denaturation, suitable for short‐term storage	Long‐term storage effect inferior to ultra‐low temperature	Wu, Yang, et al. (2024)
	Static magnetic field‐assisted freezing (SMFF)	Pork loin	2 mT/4 mT/6 mT/8 mT	2 mT optimal: freezing time reduced by 37.81%, thawing loss decreased from 5.7% to 2.4%, good water state retention	Energy‐saving, noncontact, good quality retention	Mechanism still debated	Hu et al. (2022a)
	SMFF	White shrimp	20 mT/40 mT/60 mT/80 mT	60 mT optimal: reduced total freezing time, lower thawing/cooking loss, decreased immobilized/free water mobility	Fine ice crystals, good water‐holding capacity, excellent texture retention	/	Sun, Zhang, et al. (2023)
	Alternating magnetic field‐assisted freezing (AMFF)	Golden pompano	3 mT, 70 Hz, freezing at −40°C	AMFF significantly alleviated quality deterioration during 6 freeze–thaw cycles, intact muscle fiber structure, low‐protein oxidation	Strong tolerance to multiple freeze–thaw cycles, thawing exudate as evaluation index	/	Sun, Zhang, et al. (2023)
	Weak oscillating magnetic field‐assisted freezing (OMF)	Pork loin	0.04–0.53 mT, freezing at −30°C	OMF had no significant effect on freezing characteristics (freezing time, drip loss, color, texture)	/	No significant improvement in freezing	Rodríguez et al. (2017)
	Radio frequency‐assisted freezing (RFF)	Rainbow trout	27.12 MHz, 2 cm/3 cm/4 cm electrode gap; RF pulse modes	2 cm electrode gap reduced ice crystal size by 25%, reduced thawing loss, texture similar to fresh sample	Fine ice crystals, good microstructure protection	No significant reduction in total freezing time	Hafezparast‐Moadab et al. (2018)
	Microwave‐assisted freezing (MF)	Lamb	2.45 GHz, 0%/40%/50%/60% microwave power	60% power reduced ice crystal size by 38%, reduced thawing loss and color change, increased hardness	Significant ice crystal size reduction, good quality retention	Excessive power may damage protein structure	Atani et al. (2022)
	Air blast freezing + High pressure freezing (HPF)	Beef	Air blast freezing at −35°C + 650 MPa, 10 min high pressure treatment at −35°C	Freezing before pressurization prevented color deterioration; thawed samples recovered fresh‐like color; reduced aerobic (2−log10 cycles) and lactic acid bacteria (2.4−log10 cycles)	Prevents pressure‐induced color deterioration, bactericidal effect, color recovery after 45 days frozen storage	High equipment requirements, precise temperature–pressure control needed	Fernández et al. (2007)
	Pressure‐shift freezing (PSF)	Largemouth bass	150 MPa, storage at −30°C for 28 days	PSF had significantly lower thawing/cooking loss than AF/IF, lowest TBARS (0.25), best α‐helix retention	Fine uniform ice crystals, low lipid oxidation, stable protein structure	High equipment requirements, no commercial application	Li, Kuang, et al. (2022)
	Isochoric freezing	Chicken breast	−4°C, 2.5% NaCl, 25 MPa	C1–C3 cycles maintained good protein solubility, WHC, Ca2+‐ATPase activity	Potential for ice‐free damage, suitable for temperature fluctuation control	Quality significantly decreased after C5	Rinwi et al. (2024)
	EFF + SMFF	Beef	220 V, 0.2 mA EF + 6 mT SMF, −18°C	EFF + SMFF reduced total freezing time by 52.5%, formed fine/uniform intra/extracellular ice crystals	Very high freezing efficiency, higher protein solubility, low thawing loss	/	Yang et al. (2025)
Medium‐assisted freezing	Immersion freezing (IF)	Conditioned beef steaks	−30°C, liquid medium (20% ethanol, 21% propylene glycol, 4% NaCl)	IF group had highest freezing rate, lowest thawing loss and TBARS, densest muscle fiber structure	Rapid freezing, good quality retention	/	Wang et al. (2020)
	IF	Beef	−35°C refrigerant, freezing rate 5.124 cm/h	IF freezing rate much higher than AF (0.194 cm/h), better color in first 75 days, lower TVB‐N/POV during 30–75 days	Rapid freezing, good color retention, stable quality	/	Ren et al. (2021)
	IF	Grass carp	−30°C, medium (65% water, 25% ethanol, 5% NaCl, 3% dextran, 1% ice‐nucleating active protein)	IF thawing loss (5.49%) significantly lower than AF (13.88%) and LNF (7.08%), minimal water migration	Rapid freezing, good water‐holding capacity, uniform water distribution	/	Diao et al. (2021)
	Liquid nitrogen spray freezing (LNSF)	Large yellow croaker	−20°C/−60°C/−100°C spray, nitrogen flow 4 m/s	−100°C LNSF ice crystal area only 187 µm2 (AF 7844 µm2), thawing loss reduced from 11.6% to 7.0%	Extremely small ice crystals, excellent quality retention	High equipment requirements, high cost	Mao et al. (2025)
	Liquid nitrogen freezing (LNF)	Perca fluviatilis fillets	Liquid nitrogen immersion	LNF ice crystal equivalent diameter 12.30 µm, ice crystal area ratio 7.61%, texture closest to fresh sample	Fine uniform ice crystals, good texture retention	High economic cost	Zhang et al. (2024a)
	LNF	White shrimp	−35°C/−65°C/−95°C/−125°C	−95°C optimal: minimal thawing loss, best water‐holding capacity and hardness	Optimal quality at suitable temperature	−125°C caused microstructure damage	Yan et al. (2023)
	Ultrasound‐assisted IF (UIF)	Large yellow croaker	200 W, 25 kHz, 30 s on/45 s off ultrasound, −30°C, medium (95% ethanol)	UIF delayed WHC, color, texture deterioration, deceased thawing loss by 1.09%–4.54%, had smaller/regular ice crystals	Good tolerance to multiple freeze–thaw cycles	/	Li, Wang, et al. (2023)
	LVEF‐assisted IF	Frozen sheep liver	LVEF (2500 V, 0.2 mA) + Allium mongolicum regel (AMR) juice (0.5 g/mL) IF	LVEF‐AMR reduced thawing time by 35.71%, improved WHC, highest α‐helix, lowest carbonyl	Physical field (ice crystal control) + polyphenol (chemical protection) synergy	/	Li et al. (2026)
	Magnetic field‐assisted IF	Pork	0–10 mT static magnetic field, −20°C, medium (water)	6 mT optimal: thawing loss reduced by 44.96% vs. 0 mT, improvements in pH, cooking loss, freezing rate.	Significant quality improvement	Effect not linearly enhanced with field strength	Gan et al. (2024)
	Magnetic field‐assisted IF	Shrimp surimi	60 mT magnetic field, medium (0.2%–0.8% curdlan)	0.6% curdlan + magnetic field reduced thawing loss to 11.41%, cooking loss to 13.83%, dense gel network	Significant synergistic effect, excellent gel properties	Excess curdlan (> 0.6%) interfered with cross‐linking	Gan et al. (2024)
Chemical pretreatments	Carbohydrates–saccharides	Frozen egg yolk	5% w/w sucrose, L‐arabinose, xylitol, trehalose, D‐cellobiose, and xylooligosaccharides	Saccharides reduced freezable water content, inhibited large ice crystal formation; L‐arabinose most effective	Natural origin, high safety	Introduces sweetness and extra calories	Ma et al. (2023)
	Carbohydrates–micro‐/nanostarch	Fish myofibrillar protein gel	14 µm (MS1)/9 µm (MS2)/265 nm (NS1)/130 nm (NS2) starch	NS2 inhibited texture deterioration (active filling effect), MS1 reduced drip loss (free water → bound water)	Use of micro‐/nanostarch provides comprehensive protection	Introduces sweetness and extra calories	Li, Huang, et al. (2024)
	Carbohydrates–cellulose nanofibers	Chicken surimi‐like material	2%/4%/6% CNF	2% CNF gave salt‐soluble protein 9.6 mg/g, carbonylation 1.9 nmol/mg, WHC 86.8%	Natural origin, no sweetness introduced	Higher concentration (> 2%) less effective	de Oliveira Meira et al. (2025)
	Carbohydrates–inulin	Beef	2% inulin vacuum impregnation	Inulin + individual quick freezing (IQF) effectively improved freezing rate	Natural origin, high safety	/	Bayraktar et al. (2024)
	Carbohydrates–glycerol (Gly)	Pork patties	2%/4%/6% Gly	6% Gly best inhibited pH drop, lipid oxidation; 4% Gly best for water‐holding capacity	Lowers freezing point	/	Liang et al. (2024)
	Carbohydrates–saccharides	White shrimp	3% trehalose/alginate oligosaccharide immersing for 1 h	Saccharides molecules embed into ice crystal lattice, inhibiting ice growth via hydrogen bonding/hydrophobic interactions	Potentially used as ice‐growth inhibitors	/	Zhang, Cao, et al. (2019)
	Carbohydrates–alginate oligosaccharide	Cooked crayfish	1% alginate oligosaccharide + 40 W ultrasound‐assisted soaking for 1 h	It improved water‐holding capacity by 19.47%, reduced ice crystal size, maintained protein structure	Ultrasound facilitates oligosaccharide penetration	/	Han et al. (2023a)
	Carbohydrates–waxy starch cryogel	Pork	Waxy starch cryogel + 24 kHz high‐intensity ultrasound diffusion, 6 min	Ice crystal growth space reduced by 75%, freezing–thawing damage alleviated	Significant reduction in ice crystal growth space	/	Coria‐Hernández et al. (2022)
	Proteins–ice structuring protein (ISP)	Quick‐frozen pork patties	0.05%/0.10%/0.20%/0.30% ISP	0.20% ISP reduced thawing loss by 43.64%, decreased TBARS/carbonyl by 25%/32%	Inhibits water migration, protects microstructure	/	Wang, Li, et al. (2021)
	Proteins–antifreeze protein (AFP)	Antarctic krill meat	0.05%/0.1%/0.2% AFP soaking for 1 h	0.1% AFP optimal: reduced protein degradation, protected secondary/tertiary structure	Uniform water distribution, less protein degradation	/	Diao et al. (2022)
	Proteins–collagen glycopeptides (GPP)	White shrimp	0.5%/2%/3% GPP	2% GPP efficacy comparable to 0.5% polyphosphate, 3% GPP superior to polyphosphate	High efficiency at low concentration, natural origin	/	Huang et al. (2025)
	Proteins–pigskin gelatin hydrolysate (PGH)	Porcine meat	0%/1%/2%/4% PGH	4% PGH reduced thawing loss by 5.32%, ice crystal area reduced to 15.54%, more intact protein structure	Wide source, relatively low cost	/	Lu et al. (2022)
	Proteins–whey peptides (WP)	Pork balls	5%/10%/15% WP (MW < 1 kDa)	10% WP maintained antioxidant activity after 7 freeze–thaw cycles, reduced sensory damage, inhibited microbial growth	Dual antioxidant and antimicrobial effects	/	Zhang et al. (2023c)
	Proteins–WP hydrolysate (WPH)	Mackerel surimi	5%/10%/15% WPH	15% WPH delayed texture deterioration, reduced TVB‐N, increased antioxidant activity	Natural antioxidant	/	Li, L. Kong, et al. (2023)
	Proteins (L‐proline)	White shrimp	1%/3% proline immersion	Proline forms hydrogen bonds with myosin via “water substitution” and “glassy state” hypotheses, inhibiting ice crystal growth	Natural amino acid; Efficacious cryoprotectant for aquatic products	/	Zhang et al. (2024b)
	Polyphenols–tea polyphenols (TP)	Minced lamb	0.015/0.10%/0.30% TP	0.01% TP enhanced emulsifying stability, water‐holding capacity, and texture	Natural origin, strong antioxidant activity	Dose‐dependent, high concentration (0.30%) harmful	Yun, Yang, et al. (2025)
	Polyphenols–olive leaf water extract (OEx)	Chicken breast sausages	0.1%/0.5% OEx	OEx groups showed no change in thawing loss during 15–60 days storage, maintained WHC, breaking strength, springiness, viscosity	Natural origin, strong antioxidant properties	/	Rachman et al. (2021)
	Carbohydrates + proteins	Culter alburnus myofibrillar protein	Carrageenan oligosaccharides (CGO): Egg white protein (EWP) = 1:1	CGO/EWP more effective than single components in delaying protein oxidation, maintaining α‐helix, and improving gel water‐holding capacity	Multimechanism synergistic protection	/	Zhang, Xiong, et al. (2022)
	Carbohydrates + salt	Snakehead fish filets	4% trehalose + 3% NaCl	Reduced intermyofibrillar cracks and pores, lowered thawing/cooking loss, delayed decline in hardness/springiness/chewiness	Complementary ice inhibition + ionic strength regulation	/	Huang et al. (2023)
	Protein + polyphenols	Beef myofibrillar protein	Soy protein isolate (SPI) modified by ultrasound (20 kHz/40 kHz), then complexed with curcumin (CUR)	SPI‐CUR complex stabilized via hydrogen bonds/hydrophobic interactions, reduced thawing loss and carbonyl content	Plant protein + polyphenol synergy enhancing protection	/	Meng et al. (2025)
	Natural complex	Plant‐based yak meat burgers	10%–70% seaweed addition	40% seaweed optimal: formed polysaccharide protective network + polyphenol antioxidant, stable texture	Natural complex, physical + chemical dual protection	/	Wang et al. (2025a)
	LVEFF + commercial cryoprotectant	Frozen beef steak	LVEF (2500 V, 0.2 mA) + 4% sucrose + 4% sorbitol	Synergistic treatment reduced thawing loss by 41%, inhibited MP unfolding and aggregate formation	Physical field significantly enhances cryoprotectant effect	/	Xie et al. (2023)
	Natural deep eutectic solvents (NADES)	Frozen shrimp surimi	Betaine‐based NADES (glycerol/D‐glucose/D‐sorbitol)	NADES remained unfrozen at −80°C, inhibited ice nucleation and growth, reduced thawing time by 2–4 times	Excellent antifreeze properties, good biocompatibility	/	Li, Liang, et al. (2025)
Physical pretreatments	Infrared predehydration	Pork	40°C, 4–14 µm, 600 W, 90 s/120 s/150 s	120 s infrared predehydration significantly reduced thawing loss, decreased hardness, increased springiness	Dehydration reduces freezable water content	/	Hu et al. (2022b)
	Microwave predehydration	Pork loin	50 W/100 W/150 W, 90 s	50 W (1.92 W/g) microwave predehydration reduced thawing loss to 1.7%, decreased hardness, increased springiness	Dehydration reduces freezable water content	/	Hu et al. (2022b)
	Infrared predehydration + MFF	Beef	Infrared 40°C, 120 s + 5 mT magnetic field	It reduced freezing time by 27.13%, thawing loss decreased from 4.39% to 0.31%	Significant synergistic effect, excellent quality	/	Hu et al. (2022c)
	Physical supercooling	Pork tenderloin meat	Supercooled rapid freezing (−80°C)	The combination produced fine uniform ice crystals, minimal thawing loss, best quality	Optimal ice crystal morphology, good reabsorption upon thawing	/	Lochan Poudyal et al. (2023)

2.1. Physical Field‐Assisted Freezing

Physical field‐assisted freezing, which intervenes in the nucleation and growth of ice crystals through external fields, represents one of the most actively researched areas today. Ultrasound‐assisted freezing (UF) is a new physical field technology, and the main mechanism is derived from cavitation effect induced by ultrasound waves propagating through a liquid medium (Zhang et al. ^2023b; Jiang, Zhang, et al. ²⁰²³). At the protein level, moderate ultrasound disrupts noncovalent interactions and protein–water interactions, and can easily produce uniform three‐dimensional gel networks with higher performance. However, more than sufficient ultrasound can disrupt orderly protein–protein interaction during gel, causing protein structure to overstretch and the gel network to destabilize (Bian et al. 2022).

Electric field‐assisted freezing (EFF) is classified into high‐voltage electrostatic field (HVEF, > 2.5 kV) and low‐voltage electrostatic fields (LVEF, ≤ 2.5 kV; Jiang et al. ²⁰²⁵). EFF is driven by the polarization effect of the electrostatic‐field on water (Jiang, Zhang et al. ²⁰²³; Jiang et al. ²⁰²⁵). HVEF drastically reduces the size of ice crystals and improves the quality, but can lead to high energy consumption and safety risks as the output voltages are increased (Jiang et al. 2025). In contrast, although LVEF offer fewer effects, it is smaller in power consumption and is more suitable for industrial purposes (Jiang et al. 2025).

Magnetic field‐assisted freezing (MFF) is a new physical field technology classified as static magnetic field‐assisted freezing (SMFF) and oscillating/alternating magnetic field‐assisted freezing (OMFF/AMFF; Fang et al. ²⁰²⁶; Jiang et al. ²⁰²⁵; Rodríguez et al. ²⁰¹⁷). Its main mechanism is the diamagnetic effect of magnetic fields on water molecules (Jiang et al. 2025; Ruan et al. ²⁰²⁴). MFF has been found to significantly improve freezing quality, and offer benefits such as strong penetration, noncontact application, no pollution, and low energy consumption (Jiang et al. 2025; Sun, Zhang, et al. ²⁰²³). However, the industrial applications of MFF remain limited due to practical operational challenges and cost (Jiang et al. 2025).

Radio frequency (RF) is a nonionizing electromagnetic wave between 1 and 300 MHz (Jiang et al. 2025). The fundamental role of RF‐assisted freezing (RFF) is the RF‐induced rotation of water molecule dipoles. RFF has been shown to substantially improve thawed product quality and reduce thawing loss (Hafezparast‐Moadab et al. ²⁰¹⁸). RFF technology offers advantages such as low energy consumption, uniform heating, and strong penetration. However, most industrial applications of RFF to food manufacturing still struggle with real operating problems and cost constraints (Jiang et al. 2025).

Microwave‐assisted freezing (MF) enables food freezing using microwaves. As a nonionizing electromagnetic wave, microwaves enter a food and produce thermal energy by inducing dipole rotation of water molecules (Jiang, Zhang, et al. ²⁰²³). MF drastically reduces ice crystals and improves frozen product quality. Xanthakis et al. (2014) used MF in pork freezing and found that compared to conventional freezing, MF reduced average ice crystals size by 62%. However, MF has disadvantages including uneven heating and localized overheating (Jiang, Zhang, et al. ²⁰²³).

Multiphysical field synergistic is a frontier approach to individual physical field‐assisted freezing, which combines two or more physical fields operating through distinct mechanisms to obtain additive or complementary effects rather than individual fields. However, research on this technology remains limited, especially for complex food matrices such as meat (Yang et al. 2025).

2.2. Pressure Field Freezing Technologies

High‐pressure‐assisted freezing (HPF) is a physical field technology using high pressure to regulate water–ice phase transition (Jiang et al. 2025; Zhang et al. ^2023b). HPF technology can help to improve frozen product quality by controlling ice crystal formation. A study has shown that HPF reduces thawing loss by 35% compared to control pork (Jiang et al. 2025). However, HPF causes severe damage to sarcomere structures of frozen meat and severe discoloration (Jiang et al. 2025; Fernández et al. ²⁰⁰⁷). Currently, the HPF applications remain limited, with most research focused on vegetables (Jiang et al. 2025).

Pressure‐shift freezing (PSF) and isochoric freezing are two novel approaches distinct from conventional isobaric freezing. PSF uses enormous supercooling to drive the simultaneous and immediate formation of multiple fine and uniformly distributed ice crystal nuclei (Li, Kuang, et al. ²⁰²²). The primary advantage of PSF is that ice crystal formation is not limited by heat transfer, enabling uniform freezing of large‐sized samples. However, PSF is limited due to equipment size, pressurization rate, and temperature control and limited to laboratory studies (Li, Kuang, et al. ²⁰²²). The core idea of isochoric freezing is to preserve the food in a rigid constant volume container. As temperature decreases, the pressure within the container increases. The pressure elevation allows the system to remain partially unfrozen below 0°C, thereby substantially reducing mechanical damage caused by ice crystals. The isochoric freezing effectively preserves meat integrity without remarkable defects. Nevertheless, the strict requirements for such rigid containers make it difficult for continuous industrial production (Rinwi et al. 2024).

2.3. Medium‐Based Freezing

Immersion freezing (IF) typically uses a low‐temperature liquid medium that directly contacts the food surface to transfer heat. Because liquid medium has a substantially higher heat transfer coefficient than air, this allows fast and consistent temperature lowering during the immersion (Yang et al. 2025). Traditional medium is sodium chloride brine, calcium chloride solutions, and ethylene glycol solutions. In recent years, research has focused on developing new multicomponent composite medium to preserve freezing performance and food quality (Svendsen et al. 2022).

Cryogenic freezing, though distinct from IF, similarly relies on liquid medium as the cooling source. Cryogenic freezing uses liquid nitrogen (−195.8°C) or liquid carbon dioxide (−78.5°C) to reach ultra‐fast freezing (Svendsen et al. 2022). It is primarily based on direct contact between the cryogenic liquid and the food surface (Jiang et al. 2025; Mao et al. ²⁰²⁵). Several studies showed that cryogenic freezing can improve the quality of frozen meat and aquatic products (Mao et al. 2025; Zhang et al. ^2024a; Jiang et al. ²⁰²⁵). However, excessively low freezing temperatures may cause muscle damage in meat products (Yan et al. 2023).

Medium‐physical field synergistic freezing is an emerging combination freezing technique based on physical field coupled with IF to achieve synergistic improvements beyond those of individual approaches (Jiang et al. 2025). Ultrasound‐assisted IF (UIF) and magnetic field‐assisted IF (MFIF) are the most widely studied medium‐physical field synergistic technologies (Jiang et al. 2025; Gan et al. ²⁰²⁴). Medium‐physical field combined freezing leads to finer crystals, reduced protein denaturation, and lower oxidation. However, this technology requires higher equipment, which currently limits its large‐scale industrial implementation (Zhang, Li, et al. ²⁰²²).

2.4. Chemical Pretreatments

Incorporating chemical pretreatments prior to freezing effectively improves frozen products quality. Salts interact with myofibrillar proteins via their ions, changing the surface charge distribution and hydration condition of proteins (Jiang, Huang, et al. ²⁰²³). Due to their low cost and ease of use, salts are widely used as cryoprotectants in food industry (Zhang et al. ^2024b). However, the use of salts poses potential health risks (Huang et al. 2025).

Carbohydrates are widely used in protein‐based food freezing and primarily include monosaccharides, disaccharides, sugar alcohols/polyols, oligosaccharides, and polysaccharides (Ma et al. 2023; Zhang et al. ^2023b; Zhang, Cao, et al. ²⁰¹⁹; Li, Huang, et al. ²⁰²⁴; de Oliveira Meira et al. ²⁰²⁵). Such compounds contain abundant hydroxyl groups in their molecular structures, enabling them to bind to water molecules or proteins by hydrogen bonds (Liang et al. 2024; Zhang et al. ^2023b). Carbohydrates are renewable, safe, biocompatible, and can be tailored to diverse applications (Han et al. ^2023a; Li, Huang, et al. ²⁰²⁴). The main limitation of carbohydrates lies in their potential to introduce undesirable sweetness and additional calories (de Oliveira Meira et al. ²⁰²⁵; Liang et al. ²⁰²⁴).

Protein‐based cryoprotectants primarily include antifreeze proteins (AFPs), antifreeze peptides, protein hydrolysates, and amino acids with cryoprotective activity (Diao et al. 2022; Wang, Li, et al. ²⁰²¹; Zhang et al. ^2023b; Huang et al. ²⁰²⁵). These components play a role in ice crystal behavior through specific molecular mechanisms, thereby protect muscle tissue from freezing damage (Wang, Li, et al. ²⁰²¹). Protein‐based cryoprotectants offer the advantages of natural origin, high efficacy, and environmental friendliness, without introducing sweetness or additional calories (Huang et al. 2025; Zhang et al. ^2024b). However, the high extraction costs substantially limit their large‐scale industrial application (Huang et al. 2025).

Polyphenols have multiple phenolic hydroxyl groups, which confer their distinctive antioxidant activity. They primarily inhibit oxidative damage during freeze–thaw cycles through antioxidant mechanisms, thereby indirectly enhancing freezing tolerance. Consequently, polyphenols tend to be used as adjuncts to protein‐ and carbohydrate‐based cryoprotectants (Yun, Yang, et al. ²⁰²⁵). However, some polyphenols may adversely affect meat color or flavor (Rachman et al. 2021).

Synergistic cryoprotective strategies can be divided into additive–additive synergy and physical field–additive synergy. For example, Zhang, Xiong, et al. (2022) combined carrageenan oligosaccharides with egg white protein and found that the mixture inhibited the oxidation of the proteins and maintained the gel properties. Physical field–additive synergy combines physical field‐assisted freezing technologies with additive‐based protection (Lu et al. 2024). The advantage of synergistic cryoprotective strategies is that multimechanism complementarity may provide enhanced protection. However, the stability of composite systems and their interaction mechanisms remain underexplored, and challenges in process parameter optimization continue to hinder their industrial adoption (Xie et al. 2023).

2.5. Physical Pretreatments

Physical pretreatment applies physical processes to raw materials before freezing to indirectly modulate ice crystal behavior by optimizing the initial state of the product. This pretreatment can be divided into two main types: physical predehydration and physical supercooling. Physical predehydration reduces the freezable water content by moderately decreasing the moisture level of the material, thereby decreasing the amount of freezable water and mitigating mechanical damage to cellular structures caused by ice crystals (Hu et al. ^2022b). Physical supercooling is a technique in which food is cooled below its freezing point without initiating phase transition. Lochan Poudyal et al. (2023) demonstrated that regardless of freezing rate, supercooled freezing produces more uniform and finer ice crystals in pork tenderloin than conventional freezing.

3. Individual Thawing Strategies

Although thawing is the reverse process of freezing, the two processes operate under fundamentally different principles and mechanisms. This section reviews the key mechanisms of thawing and various advanced individual thawing technologies. Beyond describing their working principles and applications, this section also critically compares their technological characteristics, industrial applicability, evidence quality, and compatibility with the three core dimensions of Industry 5.0. These individual thawing technologies constitute the technological foundation upon which intelligent thawing systems and Industry 5.0‐oriented thawing systems are subsequently established.

3.1. Multiscale Mechanistic Framework of Thawing Process

3.1.1. Phase Transition and Microstructural Damage

Thawing is a complex, multiscale process involving the coupling of multiple factors. Ice crystals formed during freezing, particularly large ones generated by slow freezing, may cause mechanical damage to muscle cells (Zhang, Kim, et al. ²⁰²²). During thawing, ice crystals melt and release water, but damaged cellular structures cannot effectively reabsorb the water, resulting in drip loss. The size, shape, and spatial distribution of ice crystals directly determine the integrity and water‐holding capacity of thawed muscle tissue (Im et al. 2024). Furthermore, as the thermal conductivity of thawed material is about one‐third that of the frozen state, the outer melted layer acts as a natural insulator, hindering heat transfer and thus impeding the thawing process (Svendsen et al. 2022; Zhang et al. ^2023d).

3.1.2. Water Migration and Drip Loss

During freezing, extracellular water crystallizes first, increasing the osmotic pressure in the extracellular space. To balance this osmotic gradient, intracellular water continuously migrates outward across the cell membrane, causing severe cellular dehydration, with water ultimately residing as ice crystals in the extracellular space (Zhang, Kim, et al. ²⁰²²). During thawing, the extent of drip loss depends on whether a balance can be achieved between the rate of ice crystal melting and the reabsorption capacity of muscle fibers. Excessively slow thawing gives sufficient time for water to migrate from the extracellular space into tissue interstices, where it is lost as exudate under gravity. Conversely, excessively rapid thawing may release moisture faster than fiber reabsorption capacity, similarly resulting in increased drip loss (Zhang et al. ^2023d). Low‐field nuclear magnetic resonance (LF‐NMR) studies have demonstrated that the conversion of bound water (T₂₁) and multilayer water (T₂₂) to free water (T₂₃) is the direct source of drip loss during thawing (Li, Xia, et al. ²⁰¹⁸; Zhang et al. ^2023g). Therefore, an optimal thawing rate range must be identified to achieve the best match between ice crystal melting kinetics and fiber reabsorption capacity (Zhang et al. ^2023a).

3.1.3. Protein Denaturation, Oxidation, and Functional Loss

The damage to myofibrillar proteins during freezing and thawing manifests primarily as denaturation and oxidation, leading to loss of protein functionality. The native conformational state of proteins is stabilized by covalent forces (disulfide bonds) and noncovalent forces (electrostatic interactions, hydrogen bonds, hydrophobic interactions, and van der Waals force; Zhang et al. ^2023b). During thawing, dehydration of myofibrils inevitably disrupts these noncovalent bonds, exposing more hydrophobic and hydrophilic groups on the protein surface. Subsequently, enhanced hydrophobic interactions among these unprotected hydrophobic molecules destabilize the three‐dimensional protein structure, leading to protein denaturation and molecular aggregation (Zhang, Kim, et al. ²⁰²²). Simultaneously, damage to myofibrillar structure releases pro‐oxidants from cells, including mitochondria, lysosomal enzymes, heme pigments, and transition metal ions. These pro‐oxidants are activated at thawing temperatures and initiate protein oxidation chain reactions including carbonylation, sulfhydryl oxidation, and disulfide crosslinking (Zhang et al. ^2023b; Zhang, Kim, et al. ²⁰²²). These oxidative modifications alter protein secondary and tertiary structures, resulting in decreased protein solubility and loss of emulsifying and gelling properties (Zhang et al. ^2023b). The effect of thawing rate is bidirectional: excessively slow thawing prolongs protein oxidation reactions and leads to more severe denaturation. In contrast, rapid thawing better preserves protein secondary structure and mitigates oxidative damage; however, localized overheating associated with certain rapid methods such as microwave thawing (MT) can cause more extensive structural disruption (Zhang et al. ^2023a).

3.1.4. Lipid Oxidation and Flavor Deterioration

Ice crystals formed during freezing disrupt the cell membrane structure of myofibrils. The abundant unsaturated fatty acids in membranes are thus exposed to oxygen in the extracellular space. Meanwhile, pro‐oxidants including mitochondria, lysosomal enzymes, heme pigments, and transition metal ions are activated during thawing. Under the combined action of these factors, lipid oxidation chain reactions are initiated, generating volatile compounds such as aldehydes, ketones, and alcohols, which are responsible for undesirable odors (Zhang, Kim, et al. ²⁰²²). Thawing temperature and duration are key parameters controlling lipid oxidation. For example, MT can suppress some oxidation by rapidly passing through the temperature danger zone, but its localized overheating may paradoxically promote thermal oxidation (Tian et al. 2025).

3.1.5. Enzyme Activity Recovery and Microbial Proliferation

During thawing, endogenous enzymes that were dormant during freezing, such as calpains, cathepsins, and lipases, gradually regain activity as temperature rises, accelerating protein hydrolysis and lipid degradation (Zhang et al. ^2023a). Simultaneously, some microorganisms resume activity after frozen dormancy and begin to proliferate at thawing temperatures, particularly above 4°C. Longer thawing times and higher temperatures increase microbial risk. Therefore, rapidly passing through the 0–4°C temperature danger zone is critical for ensuring the safety of thawed products (Zhang et al. ^2023a).

3.1.6. Multifactor Interactions

The physical and biochemical changes during thawing do not occur in isolation but are coupled and amplified in a cascading manner. For example, aldehydes generated by lipid oxidation, such as malondialdehyde and 4‐hydroxynonenal, can react with protein amino groups, exacerbating protein crosslinking and loss of protein functionality (Zhang et al. ^2023b). Protein denaturation and crosslinking, in turn, weaken the structural integrity of myofibrils, thereby reducing their water reabsorption capacity and exacerbating drip loss (Zhang et al. ^2023b). Therefore, an ideal thawing technology should balance efficiency, uniformity, and quality retention (Çalışkan Koç et al. 2025; Im et al. ²⁰²⁴). Key applications of advanced thawing technologies are summarized in Table 2.

TABLE 2.

Summary of representative studies on advanced thawing technologies for protein‐based foods.

Technology category	Specific technology	Sample material	Key thawing parameters	Main findings	Advantages	Limitations	References
Physical field‐driven thawing technologies	Solid‐state microwave thawing (SMT)	Beef	915 MHz, 1 kW, endpoint −2°C (COMSOL optimized)	Optimized SMT avoided overheating, improved thawing efficiency, maintained WHC, microstructure integrity, low oxidation	Uniform thawing, good quality retention	/	Zhu et al. (2025)
	Solid‐state microwave dynamic thawing	Mashed pork chops	2.40–2.50 GHz, 0–200 W, frequency shifting + adaptive power control	Adaptive power control strategy significantly improved thawing uniformity, reduced hot spot temperature	Precise control, embeddable in smart algorithms	/	Yang and Chen (2022)
	Smart microwave oven thawing	Pork/fish/drumstick/chicken breast	Infrared radiation (IR) sensor + humidity sensor closed‐loop control	Thawed meat temperature controlled between −2°C and 25°C, mean temperature ranges from 1°C to 2.5°C	Intelligent control, better quality than traditional microwave thawing (MT)	/	He et al. (2022)
	Microwave thawing (MT)	Pork longissimus lumborum	800 W, 5 min	Fastest thawing (5 min), but highest thawing loss (4.71%) and cooking loss (23.07%), greatest muscle fiber damage	Fastest thawing (5 min)	Most severe quality deterioration, localized overheating	Wang et al. (2022)
	MT	Beef for sauce marinated beef	900 W, 1.6 min	Shortest thawing time (1.6 min), highest yield of sauce‐marinated beef (58.21%), minimum shear force (10.25 N)	High efficiency, high product yield	/	Wu et al. (2025)
	MT	Pork/beef/mutton	2450 MHz, intermittent (15 s on/10 s off), 40–50 s	Fastest thawing (40–50 s), but localized overheating caused protein denaturation and lipid oxidation	Fastest thawing (40–50 s)	Uneven heating, localized overheating	Gan et al. (2022)
	MT	Beef (fillet/minced)	100/200/300 W	100 W optimal, thawing time 11–26 min, better microbial safety than ultrasound	Rapid passage through danger zone	Surface overheating, color deterioration, high drip loss.	Köprüalan Aydın and Kaymak Ertekin (2025)
	MT	Pompano	500 W, 5.8 min	Shortest thawing time, but localized overheating caused protein denaturation and degradation	Shortest thawing time	Localized overheating, quality deterioration	Lan et al. (2021)
	MT	Chicken breast	Microwave oven thawing, 5 min	Suitable for short‐term frozen (1 month) meat, good physicochemical property retention	Good for short‐term storage	Quality declined with long‐term storage (5–7 months)	Augustyńska‐Prejsnar et al. (2018)
	MT	Common carp	600 W	Lowest sensory score, highest cooking loss	/	Poor sensory quality	Wang et al. (2015)
	MT	Chicken feet	200 W, 2450 MHz, intermittent (15 s on/5 s off)	Fastest thawing, but localized overheating caused partial “cooking,” worst physicochemical properties	Fast	Localized overheating, worst quality	Yuan et al. (2023)
	Radio frequency thawing (RFT)	Beef	A 12 kW, 27.12 MHz, 50‐ohm RF system	Cuboid best uniformity (STUI = 0.194), step shape worst (STUI = 0.282); uniformity decreased with increased thickness	Deep penetration, volumetric heating	Edge overheating, uniformity affected by shape/size	Li, Li, et al. (2018)
	RFT	Tilapia fillets (multilayer)	27.12 MHz, 600/800/1000 W, electrode gap 10/12/14 cm	800 W/12 cm optimal, more uniform temp distribution than water tempering, lower TBARS	Fast, uniform, good quality retention	Electromagnetic energy may negatively affect fat oxidation	Zhang et al. (2021)
	RFT	Chicken breast	27.12 MHz, 10 kW, electrode gap 65 mm	Thawing time 40 min (conventional 18 h), thawing loss 0.32% (conventional 4.84%)	Fast, low drip losses	/	Bedane et al. (2018)
	RFT	Minced chicken breast	27.12 MHz, 300/500/700 W, electrode gap 80/90/100 mm	500 W/80 mm optimal, crushed ice assisted (CIRFT) improved uniformity and rate	Suitable for home use, quality better than MT	Requires crushed ice assistance	Tian et al. (2023)
	RFT	Tilapia fillets	27.12 MHz, 300/600/900 W, electrode gap 10/12/14 cm	Color changes strongly correlated with pH, immobilized water content, drip loss	Improved color	Mechanism not fully understood	Jiang et al. (2021)
	RFT	Pork loin	27.12 MHz, 200/300/400 W, electrode gap 30 mm	400 W thawing rate is 5 times of water tempering, 94 times of air tempering; quality better than MT	Fast, uniform, good quality retention	/	Choi et al. (2017)
	RFT	Marinated yellowfin tuna	27.12 MHz, 3 kW, electrode gap 11 cm	RF significantly reduced tempering time; marinated samples maintained good quality after 5 cycles	Fast, good quality retention	TBARS and b* value increased	Fu et al. (2025)
	RFT (Simulation)	Hairtail fish	27.12 MHz, 6 kW, electrode gap 9–20 cm	Flat placement best uniformity (temp uniformity index 0.033), upright worst	Simulation guides thawing of irregular products	/	Jiang, Yang, et al. (2023)
	RFT	Frozen lean beef	27.12 MHz, 2 kW, conveyor movement	Stationary tempering can be fast and uniform; downward‐inclined conveyor aids temperature control	Conveyor movement affects uniformity	Improvement not statistically significant	Palazoğlu and Miran (2018)
	RFT	Tuna	13.56/27.12 MHz	More uniform temp distribution when top electrode size matched sample; thawing time reduced by 3 times compared with air thawing (AT)	Frequency and electrode matching can optimize uniformity	/	Llave et al. (2014)
	RFT	Chicken (cubes)	40.68 MHz, 50 Ω, 300 W, electrode gap 10 cm	The gap between the top electrode and the samples plays a major role in the heating efficiency	Digital twin model can optimize the process	/	Goñi et al. (2022)
	RFT	Salmon fillets	40.68 MHz, 400 W	Thawing time of water thawing (WT) and AT were 3.0 and 12.8 times longer than that of RFT	Fast, uniform, high‐quality retention	/	Han et al. (2022)
	RFT	Mutton	27.12 MHz, 6 kW	Thawing time 0.5 h (AT 8.1 h), lower thawing loss, TVB‐N, TVC	Fast, good quality retention	/	Sun, Jia, et al. (2023)
	RFT	Tilapia fillets	27.12 MHz, 12 kW, 600 W, electrode gap 12 cm	Significantly reduced tempering time, less water loss, uniform tempering distribution	Fast, uniform, excellent quality	/	Jiang et al. (2022c)
	RFT	Pork	27.12 MHz, 3 kW, electrode gap 11 cm	Significantly reduced tempering time; acceptable quality	Fast, potential for industrial application	Edge overheating issue to be solved	Zhu et al. (2019)
	RFT (Batch/Continuous)	Pacific sauries	27.12 MHz, 200/400/800/1600 W, electrode gap 7/8/9 cm	Continuous mode reduced overheating; high‐temperature zone area decreased from 0.28% to 0.23%	Continuous mode better uniformity	/	Yao et al. (2021)
	Low‐voltage programmed ohmic thawing	Pork	35 V, 8000 Hz, current (500, 200, 100 mA)	Dual current loop reduced thawing time by 14.54%–15.95%, but caused hot spots and worse quality	Safe voltage, fast thawing	Dual current loop caused hot spots	Zhang et al. (2024e)
	Ohmic thawing (OT)	Minced beef	10/13/16 V/cm, electrode surface form (smooth foil/needle‐type/pyramid‐type)	16 V/cm + needle electrode lowest unit exergy cost ($72.40); cylindrical samples uneconomical	Needle electrode good contact, high efficiency	Cylindrical samples uneconomical	Çokgezme et al. (2021)
	OT	Tuna	20 kHz, 200 V	EC increased with frequency and temperature; parallel circuit and membrane removal increased EC	High frequency increases heating rate	/	Liu et al. (2017)
	OT	Duck breast	400 Hz, 10/15/20 V/cm	15 V/cm optimal: thawing time reduced by 28%–86%, thawing loss reduced by 2.55%	Efficient, good quality	/	Cao et al. (2025)
	OT	Frozen fish (seabass)	20/30/40 V/cm, 50 Hz	30 V/cm optimal, operating cost $58.5/100 kg (water thawing $108/100 kg)	Low cost, low thawing time, good quality retention	/	Chysirichote et al. (2024)
	OT	Minced beef	10/13/16 V/cm, needle‐type electrodes	13 V/cm recommended as alternative; thawing time 87% shorter than refrigeration thawing, 64% shorter than water thawing	Fast, acceptable quality	/	Cevik and Icier (2021)
	Pulsed electric field thawing (PEFT)	Pork	Constant current (CC) mode (40/20/12 mA), 1000 Hz	CC mode had better temperature uniformity, and reduced water migration and oxidation	Uniform temperature, good quality retention	/	Yao et al. (2023)
	PEFT	Pekin duck meat	5.5, 11, 16.5, 22 kV, 1–3 kV/cm, 50 Hz, pulse width 2000 µs	3 kV/cm optimal: thawing time reduced by 20%–50%, thawing loss reduced by 28%, protein loss reduced by 19%	Fast, reduced nutrient loss	Intensity must be < 4 kV/cm to avoid negative effects	Lung et al. (2022)
	CC‐PEFT	Pork longissimus muscle	CC mode (40/20/12 mA based on impedance)	CC mode had higher thawing rate, lower water migration and thawing loss, inhibited protein oxidation	Uniform current, good quality	/	Yang, Yao, et al. (2024)
	PEFT	Zhijiang duck meat	0/1/2/3/4 kV/cm, 0–103 Hz	2 kV/cm optimal: lowest shear force, best color, improved WHC	Improved texture and WHC	Excessive intensity/time reduced quality	Wu, Xu, et al. (2024)
	High‐voltage electrostatic field thawing (HVEFT)	Beef	10 kV, needle electrode numbers (8/16/48)	With increasing needle count, thawing loss first decreased then increased, TBA first increased then decreased	Electrode optimization can improve quality	High needle count increased oxidation	Amiri et al. (2019)
	HVEFT	Rabbit meat	−15/−20/−25 kV	−20 kV optimal: thawing time reduced by 60%, good texture	Fast, good quality, bactericidal	/	Jia et al. (2017)
	HVEFT	Tuna	4.5–14 kV, electrode gap 3/4.5/6 cm	TBA and ΔE values significantly increased; faster oxidation at higher intensities	/	Increased lipid oxidation, color changes	Jia et al. (2017)
	HVEFT	Lightly salted, frozen pork tenderloin	−10/−15 kV	−10 kV had significantly less drip loss than air thawing	Promotes salt diffusion, reduces drip loss	/	Jia et al. (2019)
	HVEFT	Bass/pork/chicken	10/30/50 kV/m	30 kV/m optimal: thawing time reduced by 23.06%–46.93%, drip loss reduced by 36.25%–48.29%	Fast, good WHC, color stability	/	Hu et al. (2025)
	HVEFT	Chicken breast	1.5/2.25/3 kV/cm (4.5–18 kV, electrode gap 3—6 cm)	2.25 kV/cm optimal: highest protein solubility and WHC, least denaturation	Optimized parameters maintain quality of sensitive products	/	Rahbari et al. (2018)
	HVEFT	Beef	12/16/20/24/28 kV	Thawing rate increased with voltage; 28 kV had lowest total loss rate (54.2%)	Improved WHC, good color, less myofibril damage	/	Tian and Ding (2023)
	HVEFT	Beef	Multineedle to plate electrode system	HVEF significantly shortened thawing time, average drip loss reduced by 1.75%	Fast, reduced drip loss	/	Zhang, Ding, et al. (2019)
	Low‐voltage EFT	Chicken breast cubes	2500 V, electrode gap 5/15/25/35 cm	15 cm optimal: lowest thawing loss, drip loss, cooking loss, highest WHC	Energy saving, safe, good quality	/	Essa et al. (2025)
	HVEFT	Beef striploins	0/2.5/5/10 kV	EFT increased purge loss, did not improve thawing rate; 10 kV had lower Warner–Bratzler shearing force	Improving tenderness and shelf life	Increased purge loss, no economic benefit	Corrette et al. (2025)
	Static magnetic field thawing (SMFT)	Beef tenderloin	10/20/30/40/50 Gs	Thawing time reduced by 21.5%–40%; at appropriate strength, thawing loss, TBARS, cooking loss reduced	Fast, good quality	/	Jiang et al. (2022b)
	SMFT	Beef steaks	0/1/2/3 mT	2 mT optimal: thawing time reduced by 20.0%, good color stability, low lipid oxidation	Fast, good quality	/	Wang, Lin, et al. (2024)
	Alternating magnetic field thawing (AMFT)	Rainbow trout fillets	100 mT, 1/40/80 Hz	Thawing time significantly reduced; 40/80 Hz caused protein unfolding and aggregation	Fast	High frequency may alter protein structure	Mohsenpour et al. (2023)
	AMFT	Porcine myofibrillar proteins	1–5 mT	4 mT optimal: inhibited protein oxidation and denaturation, formed compact uniform gel network	Protects protein structure and gel properties	/	Zhu et al. (2024)
	Magnetic field thawing (MFT)	Portunus trituberculatus meat	2 mT, 50 Hz	Thawing loss reduced by 18.9%–20.0%, TBARS reduced by 40%, good muscle fiber integrity	Fast, low lipid oxidation, good flavor retention	/	Shi, Sun, et al. (2025)
	AMFT	Pork	0/1/2/3/4/5 mT	4 mT optimal: lowest thawing and drip loss, most stable MP structure	Protecting protein structure, good quality	/	Zhu et al. (2023)
	Infrared radiation thawing (IRT)	Pork/beef/mutton	60°C, IR power 12 W, 20–25 min	Relatively fast thawing (20–25 min), quality better than AT	Superior to traditional methods	WHC inferior to ultrasonic thawing	Gan et al. (2022)
	Ultrasound thawing (UT)	Pork	40 kHz, 100 W	Thawing time significantly reduced; Improving WHC and textural properties	Fast, better textural properties, low cleaning costs	Limited to laboratory‐scale experiments	You et al. (2026)
	UT	Goose meat	mono‐frequency (28 kHz; 50 kHz)	50 Hz optimal: Reduced the thawing time by 45.37%–57.58%, significantly decreased thawing loss	Fast, low thawing loss	/	Zhang et al. (2023e)
	UT	White yak meat	200/400/600 W, 20 kHz	400 W optimal: Reduced thawing times by 30.95%–64.28% compared to control	UT improves the thawing efficiency and quality	the muscle cell area was decreased when used higher power	Guo et al. (2021)
	UT	Tibetan pork	30 kHz, 300 W, in a 3 s on/3 s off cycle	The best quality in groups (UT, HPT, MT, AT); High thawing efficiency and better tenderness	Fast; better quality	/	Liu et al. (2024)
	UT	Common carp	0/100/300/500 W	UT‐300 thermal stability were much higher than others, UT‐300 can inhibit protein aggregation	UT‐300 effectively inhibits the protein aggregation and structural changes	Too high or too low power had poor effect	Sun, Kong, et al. (2023)
	UT	Duck meat	0/200/400/600 W	200–600 W can shorten the thawing time by 30.96%–55.05%	Fast, good quality	/	Sun, Zhao, et al. (2023)
	UT	Sea Bass	180/220 W; 3 ultrasound transducers	Multifrequency samples had a lower thawing time (44.92 min), higher hardness, higher chewiness compared with the single‐ and dual‐frequency	UT improve the quality, especially at specific power and frequency	/	Yang, Bian, et al. (2024)
	UT	Chicken breast	Frequency: 25, 130 kHz; Operating modes: normal, sweep, degas; Amplitudes: 100%, 60%	Sweep mode at 25 kHz and 100% amplitude as the most effective strategy.	UT reduced thawing time under all conditions	Quality responses depended strongly on the applied parameters	Santos et al. (2025)
	UT	Large yellow croaker	Single frequency (28, 40 kHz), dual frequency (28/40 kHz)	Dual frequency samples had better effects	Dual frequency is a potential method to protect quality	/	Cheng et al. (2024)
	UT	Tuna	160/280/400 W	280 W optimal: minimal negative impact on microstructure, increased immobilized water content	Appropriate power beneficial	Excessive power (400 W) caused structural changes	Li et al. (2019)
	UT	Large yellow croaker	200/240/280/320 W	240 W optimal: maintained freshness and color, closely arranged myofibrils	Fast, good quality	320 W caused protein structure damage	Chu et al. (2022)
	UT	Tuna	160/280/400 W, 6/12/18/24 min	280 W/12 min optimal: highest WHC, stable protein structure	Improves gel properties	/	Ma et al. (2022)
	Atmospheric dielectric barrier discharge (DBD) plasma thawing	Beef	Atmospheric DBD plasma, 16/18/20 kV, thickness 5/10/15 mm	20 kV optimal: thawing time reduced by 82% vs. AT, energy consumption reduced by 82%–95% vs. MT, complete sterilization (0 CFU/g)	Fast, energy saving, thorough sterilization	High voltage (20 kV) increased β‐sheet (37% → 59%)	Atani et al. (2025a)
	High‐pressure thawing (HPT)	Fish and shellfish	100–200 MPa	150 MPa optimal: thawing drip reduced by 30%–70%	Reduced drip loss	> 200 MPa caused color/texture changes	Rouillé et al. (2002)
	HPT	Atlantic salmon	50/100/150/200/300 MPa	100–150 MPa optimal: water loss reduced by 6.77%, color difference reduced by 56.77%	Fast, good quality	Pressure thresholds exist (200/300 MPa)	Li (2024b)
	HPT	Beef rump muscle	50/100/150/200 MPa	HPAT significantly shortened thawing time; FF‐50 MPa had lowest thawing loss	Fast, good WHC	/	LI (2022b)
	Sublimation‐rehydration vacuum steam thawing (SRVST)	Pork loin	50 Pa, 20–30°C (Chamber), initial sublimation degree (0%–15%)	12% sublimation dehydration enabled complete thawing, no drip loss	Uniform thawing, no drip	/	Kopeć et al. (2022)
	SRVST	Pork	SRVST: < 125 Pa, 25.9°C, 19.6 min, 1634 mL VT: 1 kPa	VSRT thawing time 54.60 min, 55.37% shorter than AT, 34.61% shorter than VT; thawing loss reduced by 85.66%–79.27%	Fast, good WHC, low energy consumption (40.67% lower than VST)	Complex process, high equipment requirements	Xue et al. (2025)
	Vacuum thawing (VT)	Pork longissimus lumborum	25°C, 9 kPa, 30 min	Thawing time 30 min, lowest thawing loss (2.78%) and cooking loss (19.37%), most uniform temperature distribution	Best quality retention, minimal muscle fiber damage	Low‐oxygen environment caused lower a* value	Wang et al. (2022)
Functional thawing medium	Plasma‐activated water (PAW) thawing	Beef	PAW activated for 20/40/60/80/100 min	Total plate count reduced by 0.5 log (medium itself 1.7–2.6 log bactericidal), no adverse effect on color/pH, improved WHC	Simultaneous sterilization, antioxidant	/	Wang, C. Ding, et al. (2024)
	PAW/Slightly acidic electrolyzed water (SAEW) thawing	Beef	PAW/SAEW, 20±1°C	Bacteria/fungi/yeast reduced by 0.83–1.76 log, no adverse effect on physicochemical/sensory quality	Simultaneous sterilization, good quality retention	/	Liao et al. (2020)
Conventional thawing optimization	Forced‐air convection thawing	Pork loin	15°C, RH 90 ± 5%, air velocity 1.5 m/s	Most time‐consuming (187 min), uneven temp distribution, muscle fiber tearing, drip loss 1.24%	Simple equipment, low cost, suitable for batch processing	Slowest, significant quality damage	Choi et al. (2017)
	Air‐impingement thawing	Tylose gel	6°C, 23/31/40 m/s, nozzle distance 4.1/9.2/14.2 cm	Thawing time reduced from > 12 h to < 3 h (over four times faster than AT)	Efficient, optimizable via modeling	/	Anderson and Singh (2006)
	Air fryer thawing	Beef liver	80±2°C	Fastest (7.5 min), low myoglobin contend (36.57%)	Fast	Localized overheating, protein damage	Avsar and Uzuner (2026)
	Two‐stage AT	Pork loin	Stage 1: 25°C to −5°C, Stage 2: −1.5°C or 2°C	Significantly reduced total thawing time, better moisture‐related properties than AT	Balances speed and quality	Requires sample transfer, inconvenient	Lee et al. (2021)
	Two‐stage WT + AT	Chicken breast	Water immersion 15 min (15°C) → 4°C, RH 90% to 0–2°C (total 35 min)	Lowest shear force, lowest MDA content, best protein preservation	Fast (35 min), good quality	Requires equipment switch	Zhang et al. (2017)
	Running water thawing	Chicken feet	20±0.5°C, flow rate 33±1 cm3/s, 9.71±1.68 min	Lowest lipid oxidation (TBARS 0.19)	Inhibits lipid oxidation	Severe myofibrillar protein degradation	Yuan et al. (2023)
	Running water thawing	Minced beef/seafood mix	Recirculating faucet device, ∼9 L water recirculated	Thawing time 74–198 min (refrigerator 2–3 days), water use 9 L (running water 709–1466 L)	> 99% water saving, energy consumption comparable to refrigerator	Equipment cost $412	Fry et al. (2025)
Synergistic strategies	HPT + OT	Beef	200 MPa + 40 V/cm	Thawing time only 0.8 min (conventional 43.3 min, pressure 11.5 min, ohmic 5.5 min)	Extremely fast, good quality retention	/	Min et al. (2016)
	IFT + MT	Pork	IR (40°C, 300 W, 10 s) + microwave (120 W, 15 s) alternate	Thawing time 11.81 min (AT 66.5 min), thawing loss 1.92%, good protein structure retention	Efficient, good quality	/	Hu et al. (2023)
	MT + VT	North Pacific krill	vacuum 4.85 min + heating 2.68 min + ice water (30 min)	Thawing time reduced by 62.48 min vs. ice water thawing, delayed ATP degradation	Fast, good umami retention	High equipment cost (1.8–3.3 million yen)	Lin et al. (2025)
	IR pretreatment + OT	Minced turkey breast	Bilateral IR (500 W, 60–120 s) + OT (15 V/cm)	Thawing time reduced by 50.14%–69.23%, total loss reduced by 20%–70%, better protein secondary structure preservation	Efficient, good quality	/	Safari et al. (2024)
	Air impingement + RFT	Frozen food (simulant)	Air impingement (0.5/2.5 m/s) + RF (2750 V, single/dual cavity)	2750 V + 2.5 m/s optimal process conditions, significantly improved temperature uniformity	Improved uniformity	/	Altin et al. (2023)
	MT + air convection thawing	Pork longissimus dorsi	Microwave (100 W to −4°C) + air convection (20–25°C to 2°C)	Best WHC, best color retention, lowest lipid oxidation, minimal protein denaturation	Avoids localized overheating, good quality	/	Zhu et al. (2020)
	RFT + water immersion thawing	Chicken breast	RF tempering to −4°C + water immersion thawing (20°C)	RFWI shortest total time (60 min), least drip loss (5.25%), highest sensory scores	Fast, good quality	/	Kaewkot et al. (2023)
	Two‐stage low‐temperature (TLT) tempering + EFT	Tan mutton	TLT (4°C→8°C, endpoint −1°C) + EFT (1500/2000/2500/3000 V)	TLT‐2000/2500 optimal: protein thermal stability, total sulfhydryl, Ca2+‐ATPase activity closest to fresh sample	Excellent protein protection	/	Zhang and Liu (2024)
	Two‐stage AT + LVEFT	Chicken breast	TLT (fixed 2.5 kV, two‐stage temperature T1/T2)	TLT 22.5–7.5 samples tissue structure almost identical to fresh meat, significantly better water retention	Excellent quality	/	Zhang et al. (2023f)
	LVEFT + high‐humidity thawing	Pork steaks	LVEF (2500 V, 0.2 mA) + 98% RH	Significantly reduced thawing loss and centrifugal loss, good color stability	Good WHC, color protection	/	Hu et al. (2021)
	Ultrasound‐assisted flowing water thawing	Beef	Mono‐freq (22/33/40 kHz); Dual‐freq (22/33,22/40,33/40 kHz); Tri‐freq (22/33/40 kHz)	22 kHz mono and 22/33 kHz dual optimal: thawing time reduced by 15.7%–45.4%, improved WHC	Fast, good WHC	/	Wu et al. (2022)
	MT + medium	Largemouth bass fillets	0.1 mg/mL Fe3O4 solution + 300 W microwave	It had only 13 differentially abundant proteins (MT had 47), effectively inhibited protein structure damage	Inhibits protein damage	/	Cao et al. (2019)
	MT/far‐IFT + medium	Red seabream fillets	0.1 mg/mL Fe3O4 solution + 300 W microwave/far‐IR	The synergism better maintained freshness than single thawing, significantly inhibited amine increase	Maintains freshness, inhibits amines	/	Cai et al. (2020)
	RFT + medium	Minced beef	27.12 MHz, 3 kW, electrode gap 115 mm, 70% glycerol solution surrounding	70% glycerol significantly improved uniformity, temp variation controlled to −3.7 to −5.3°C, RFT rate 40 times of refrigeration tempering	Significantly improved uniformity, nontoxic, odorless	Tempering time prolonged by 3.5 min	Li, Zhu, et al. (2021)
	RFT + medium	White shrimp	27.12 MHz, 12 kW, 50 Ω, compacted crushed ice (CCI)(porosity 0.44) surrounding	Only a 7.8°C difference was found between the hot and cold spots in shrimps surrounded by CCI	Significantly improved uniformity for irregular products	/	Zhang et al. (2024f)
	OT + medium	Minced tuna (model)	20 kHz, stepwise voltage (400 V, 200 V, 100 V), Salt (0%–2.0%)	Stepwise voltage improved tempering uniformity; 0.5% salt model food best matched minced tuna	Stepwise voltage improves uniformity	/	Chen et al. (2022)
	OT + medium	Tuna	40/50/60 V, 0.3/0.4/0.5% NaCl	50 V/0.5%NaCl increased thawing rate 5x, significantly improved protein solubility and reduced thawing loss (p < 0.05)	Solves electrode contact issue, fast	Potential salt introduction	Keshani et al. (2022)
	OT + medium	Tuna cubes	40/50/60 V, 0.3/0.4/0.5% NaCl	50 V/0.3%NaCl shortened thawing time 5.95x, minimal thawing and cooking losses	Fast, low losses	Faster lipid oxidation	Fattahi and Zamindar (2020)
	UT + medium	Shrimp	Ultrasound (35 kHz) + slightly basic electrolyzed water (SBEW)	Thawing time reduced by 48.9%, reduced MDA, TVB‐N, carbonyl compound generation, good muscle fiber integrity	Fast, antioxidant, protein protection	/	Li, Wang, et al. (2024)
	UT + medium	Tibetan pork	1.0% NaCl + 300 W ultrasound	Minimal cooking loss (18.44%), inhibited water migration, increased surface hydrophobicity (47.96 a.u.), stable protein structure	Fast, good WHC, antioxidant	/	Liu, Wu, et al. (2025)
	UT + medium	Mirror carp	0.05/0.10/0.20% NaCl + 200 W ultrasound	1% salt optimal: significantly reduced thawing loss, centrifugal loss, cooking loss, inhibited oxidation	Fast, good quality	/	Li, Wang, et al. (2022)
	UT + medium	Chicken breast	SAEW (pH6.25) + ultrasound (EUT)	EUT significantly increased thawing rate, low thawing loss, water state close to fresh sample, stable protein structure	Fast, good quality	/	Kong et al. (2022)
	UT + medium	Mutton	SAEW + ultrasound (EUT)	EUT group had high immobilized water, low free water, inhibited lipid oxidation, minimal mineral loss, intact microstructure	Good quality, good nutrient retention	/	Kong et al. (2023)
	UT + medium	Chicken	PAW + ultrasound (100 W, 20°C)	Bacteria reduced by 0.62–1.17 log CFU/g, protein loss reduced by 17.1%–23.1%	Simultaneous sterilization, reduced protein loss	/	Qian et al. (2022)
	LVEFT + medium	Spotted sea bass	PAW + LVEF (2.5 kV)	Increased thawing rate, enhanced WHC, MP solubility increased to 89.96%, increased α‐helix and β‐sheet ratio	Efficient, good quality	/	Li, Xu, et al. (2025)
	UT + medium	Tan mutton	PAW + 100–500 W ultrasound	300 W optimal (UPT‐300): effectively inhibited protein oxidation, maintained α‐helix and β‐sheet	Antioxidant, protein protection	/	Li, Zhang, et al. (2025)
Integrated freezing‐ thawing optimization	AEF throughout freezing–thawing–aging	Beef	AEF (2200 V, AC220V, 0.2 mA, 50/60 Hz)	AEF reduced thawing and cooking loss, improved tenderness, maintained color stability	Comprehensive quality protection	/	Wu et al. (2023a)
	SMF repeated freezing–thawing	Lamb meat	5 mT	SMF‐FT significantly reduced thawing and centrifugal loss, good water retention	Good WHC, structural protection	/	Liu, Heming, et al. (2025)
	SMF freezing–thawing	White shrimp	5 mT MF freezing + MF/refrigerator thawing	MF freezing–thawing significantly improved hardness, chewiness, WHC, inhibited protein oxidation	Excellent quality, stable protein structure	/	Zhao, Yang, et al. (2025)
	Ultrasound freezing–thawing	Beef	45 kHz, 30 min (before/during/after freezing, during/after thawing)	Ultrasound during thawing gave highest WHC improvement (to 0.78)	Improves WHC	/	Wang, Dong, et al. (2021)
	IR predehydration + MFF + HVEFT	Beef/pork	IR (40°C, 12 µm, 600 W) + MFF (2, 6 mT) + HVEFT (30 kV/m)	Drip loss reduced by 56.24% (beef)/59.12% (pork), better protein structure retention	Complementary advantages, good quality	/	Hu et al. (2026)
	Antifreeze protein + staged thawing	Minced pork	0.2% AFP + 25/−1°C staged thawing	AFP inhibited ice recrystallization; synergy with staged thawing significantly reduced thawing loss, inhibited oxidation	Effective against repeated freeze–thaw	/	Zhou et al. (2024)
	Waxy Starch Cryogel + UT	Pork	Waxy starch cryogel + 24 kHz high‐intensity ultrasound diffusion	Cryogel restricted ice crystal growth, ultrasound‐assisted thawing; synergistically improved WHC and microstructure	Synergistic enhancement	/	Coria‐Hernández and Meléndez‐Pérez (2024)
	Magnetic nanoparticles (MNPs) freezing–thawing + MT	Atlantic salmon fillets	MNPs‐assisted cryogenic freezing (−80°C) + MNPs‐assisted MT (500 W)	It produced ice crystal equivalent diameter 2.37 µm, MP solubility 87.28%, lowest lipid/protein oxidation	Comprehensive protection, excellent quality	/	Wang et al. (2023b)
	MNPs freezing–thawing + MT	Atlantic salmon	MNPs‐assisted cryogenic freezing + MNPs‐assisted MT	It highest Ca2 +‐ATPase activity	Molecular‐level protein stabilization	/	Li, Wang, Fan, et al. (2024)
	Freezing–thawing rate matching	Yellowfin tuna	Freezing rate (0.35/0.47 cm/h) + RF tempering rate (0.5/1.0/1.5°C/min)	Fast freezing + slow tempering (0.5°C/min) combination optimal, best quality retention	Rate matching optimizes quality	/	Duanmu et al. (2024)
	Freezing–thawing rate matching	Red swamp crayfish meat	Rapid/slow freezing (RF/SF), water immersion/cold thawing (WT/CT)	SF‐CT produced soft flexible gel, RF‐WT produced hard gel; different combinations can tailor gel properties	Allows targeted product property modulation	/	Ye et al. (2022)

3.2. Physical Field‐Driven Thawing Technologies

3.2.1. Microwave Thawing

Microwaves are electromagnetic waves in the frequency from 300 MHz to 300 GHz (Tian et al. 2025). During MT, microwaves induce interactions among polar molecules in the food, generating heat by collision and friction to thaw them (Figure 3a; Jiang et al. ²⁰²⁵; Tian et al. ²⁰²⁵). MT penetrates frozen food, thereby allowing simultaneous internal and external heating and substantially shorter thawing time (Çalışkan Koç et al. 2025). For instance, MT thawed octopus 3.7 times faster than hydrostatic pressure thawing and 43.7 times faster than refrigerator thawing (Jiang et al. 2025). Additionally, MT has advantages such as effectively inhibiting microbes and operational convenience for households and businesses (Çalışkan Koç et al. 2025; He et al. ²⁰²²).

FIGURE 3.

Schematic diagrams illustrating the working mechanisms of physical field‐driven thawing technologies: (a) microwave thawing (MT); (b) radio frequency thawing (RFT); (c) magnetic field thawing (MFT); (d) electric field thawing (EFT); (e) ohmic thawing (OT); and (f) ultrasound thawing (UT), reference from Shi et al. (2024).

However, MT may cause uneven heating and localized overheating due to thawed regions absorbing microwaves much easier than frozen regions (Yang and Chen 2022). This may result in protein denaturation and aggregation, and lipid oxidation which ultimately affects food texture, flavor, and nutritional value (Gan et al. 2022; Köprüalan Aydın and Kaymak Ertekin ²⁰²⁵; Zhu et al. ²⁰²⁵). Novel approaches have been proposed to overcome these limitations. Solid‐state microwave thawing (SMT) replaces traditional magnetrons with semiconductors, enabling precise control over microwave frequency and power, and can effectively improve thawing uniformity (Yang and Chen 2022; Zhu et al. ²⁰²⁵).

3.2.2. Radio Frequency Thawing

Radio frequency thawing (RFT) utilizes electromagnetic waves in frequencies between 300 kHz and 300 MHz (Yang and Chen 2022; Zhu et al. ²⁰²⁵). RFT and MT both belong to dielectric heating technologies, where the electromagnetic energy is converted into heat through intermolecular friction. Since RFT has lower frequency and greater penetration depth, it is more suitable for industrial thawing of bulk materials (Figure 3b; Çalışkan Koç et al. ²⁰²⁵; Choi et al. ²⁰¹⁷). RFT may lead to faster thawing and greater penetration depth. Sun, Jia, et al. (2023) found RFT thawed mutton in 0.5 h versus 8.1 h in air. Additionally, RFT better preserves meat texture and reduces drip loss and color deterioration compared to MT (Choi et al. 2017; Jiang et al. ²⁰²⁵). Moreover, RFT can achieve energy efficiency by fast thawing thereby reducing cold chain maintenance time and product loss (Choi et al. 2017; Jiang et al. ²⁰²⁵).

The main challenge in RFT is nonuniform heating show as edge effects and thermal runaway (Li, Li, et al. ²⁰¹⁸; Li, Zhu, et al. ²⁰²¹; Llave et al. ²⁰¹⁴; Paoletti et al. ²⁰²⁴). Excessive protein denaturation and oxidation at the edges and corners lead to texture hardening and flavor deterioration, while the insufficiently thawed central region exhibits incomplete water reabsorption and increased drip loss (Li, Li, et al. 2018). This issue is related to food shape, size, dielectric properties, electrode gap, and placement position (Chen, Li, et al. ²⁰²³; Jiang et al. ²⁰²⁵; Llave et al. ²⁰¹⁴).

3.2.3. Ohmic Thawing

Ohmic thawing (OT) is a technique that applies an electric current directly to the food. Current passes through frozen food, which acts as a resistor, and electrical energy is converted into thermal energy for volumetric and rapid thawing (Figure 3e; Jiang et al. ²⁰²⁵; Zhang et al. ^2023a). OT is not constrained by energy penetration depth and does not require a medium for heat transfer, having advantages such as high energy conversion efficiency, rapid heating rates and high‐quality preservation (Çalışkan Koç et al. 2025; Fattahi and Zamindar ²⁰²⁰; Keshani et al. ²⁰²²; Zhang et al. ^2023a). Cevik and Icier (2021) found OT (13 V/cm) reduced thawing time for minced beef by 87% compared to refrigeration thawing and by 64% compared to water thawing. Chysirichote et al. (2024) identified 30 V/cm as the optimal parameter for OT, with operating costs ($58.5/100 kg) substantially lower than water thawing ($108/100 kg).

However, OT suffers from uneven heating and localized overheating. It is primarily attributed to different electrical resistivity of food components and poor electrode contact caused by irregular sample surfaces (Çalışkan Koç et al. 2025; Zhang et al. ^2023a; Jiang et al. ²⁰²⁵).

3.2.4. Electric Field Thawing

Electric field thawing (EFT) encompasses technologies that use HVEF, LVEF, or pulsed electric fields (PEF) to thaw food (Çalışkan Koç et al. 2025; Peng et al. ²⁰²⁵; Wu, Xu, et al. ²⁰²⁴). The mechanism involves air ionization to generate ionic wind which enhances heat transfer through convection and then accelerates the thawing rate (Figure 3d; He, Jia, et al. ²⁰¹⁶; Jiang, Zhang, et al. ²⁰²³; Zhang et al. ^2023b). Concurrently, the electric field induces polarization and reorientation of water molecules, which destabilizes the ice crystal structure and then facilitates the phase transition (Hu et al. 2025). EFT substantially increases thawing rates and reduces processing time. For instance, Jia et al. (2017) reported that a 20 kV HVEF shortened the thawing time of rabbit meat by 60% (Lung et al. 2022). By accelerating the phase transition process during thawing, EFT shortens the residence time in the temperature danger zone, thereby suppressing microbial growth and enzymatic activity (Jia et al. 2017).

However, EFT application faces certain constraints. First, ozone generated by corona discharge can accelerate the oxidation reaction, including lipid oxidation and protein denaturation in the product (Mousakhani‐Ganjeh et al. ²⁰¹⁶; Zhang and Ding ²⁰²⁰). Second, the high output voltage of HVEF poses safety risks, and ozone may adversely affect food flavor and environmental safety (Jiang et al. 2025; Rahbari et al. ²⁰¹⁸).

3.2.5. Magnetic Field Thawing

Magnetic field thawing (MFT) includes static MFT and alternating/low‐frequency alternating MFT (Jiang et al. 2025; Zhu et al. ²⁰²³). When a magnetic field is applied, water molecules reorient and move in food and increase their temperature and thus increase their thaw efficiency. Magnetic field modulation of water clusters influences ice crystal melting kinetics, enabling better matching between melting rate and fiber reabsorption capacity, thereby reducing drip loss (Wang, Lin, et al. ²⁰²⁴). Alternating magnetic fields can further reduce thawing time by increasing molecular vibration, which can generate molecular frictional heat and assist in temperature elevation (Figure 3c; Jiang et al. ²⁰²⁵; Mohsenpour et al. ²⁰²³). Recent studies demonstrate that MFT improves thawing efficiency and improves quality (Shi, Sun, et al. ²⁰²⁵; Zhu et al. ²⁰²⁴). Moreover, MFT has advantages such as energy efficiency, environmental friendliness, and operational safety which suggest potential use (Çalışkan Koç et al. 2025; Jiang et al. ^2022b).

There are some limitations of MFT. First, the effects of MFT on food remain debated (Jiang, Zhang, et al. ²⁰²³). Second, specialized equipment is required and it may lead to a high start and maintenance costs, limiting widespread use (Çalışkan Koç et al. 2025). Third, high‐frequency MFT could adversely impact thawing quality (Mohsenpour et al. 2023; Wang, Lin, et al. ²⁰²⁴).

3.2.6. Infrared Radiation Thawing

Infrared radiation thawing (IRT) is a technique that utilizes infrared radiation for heating. When infrared radiation reaches food surface, molecules absorb energy and migrate between energy level and transmit heat by vibration and friction (Gan et al. 2022). Infrared radiation includes near‐, mid‐, and far‐infrared, with far‐infrared being more commonly applied because its frequency matches better with food molecules (Gan et al. 2022).

The advantages of IRT lie in its simple equipment, convenient operation, and relatively fast heating rate. Compared to room temperature thawing, IRT effectively shortens thawing time (Gan et al. 2022). However, due to its shallow penetration depth, energy is only deposited on the food surface, which can lead to surface overheating while the interior remains frozen (Hu et al. 2023).

3.2.7. Ultrasound Thawing

Ultrasound thawing (UT) is an emerging physical field technology that utilizes acoustic wave energy to accelerate foods thawing (Zhang et al. ^2023a). The ultrasound propagation leads to cavitation. The compression and rarefaction cycles of sound waves create negative pressure regions, inducing the formation, oscillation, and implosion of cavitation bubbles which generate instantaneous high temperatures and pressures (up to 5000 K and 100 MPa; Jiang, Zhang, et al. ²⁰²³; Zhang et al. ^2023b). The mechanical forces from cavitation bubble implosion can fragment large ice crystals and promote uniform melting. Meanwhile, the melting rate achieves a better match with the reabsorption capacity of myofibrils, thereby reducing drip loss (Guo et al. 2021). Additionally, high‐frequency oscillations resulting from ultrasound attenuation can be directly converted into thermal energy that enables internal heating of the material (Figure 3f; Çalışkan Koç et al. ²⁰²⁵).

The effectiveness of UT is influenced by frequency, power, and operational mode (Çalışkan Koç et al. 2025). Ultrasound in the 20–100 kHz range is typically regarded as low‐frequency ultrasound in food industry, as its pronounced cavitation effect makes it more suitable for thawing. High‐frequency ultrasound (> 100 kHz) tends to attenuate readily and results in insufficient penetration depth (Santos et al. 2025; Zhang et al. ^2023a). Optimal UT power is critical: too low a level extends thawing time, whereas too high a level compromises quality (Guo et al. 2021; Zhang et al. ^2023b). Moreover, operational mode is also important: sequential mode outperforms simultaneous modes and dual/multifrequency mode are superior to single‐frequency ones (Wang, Wang, et al. ²⁰²⁴; Cheng et al. ²⁰²⁴; Yang, Bian, et al. ²⁰²⁴; Chen, Wang, et al. ²⁰²⁴).

UT ensures fast thawing with good quality retention (Gan et al. 2022; Köprüalan Aydın and Kaymak Ertekin ²⁰²⁵; Sun, Kong, et al. ²⁰²³; Xu et al. ²⁰²²; Zhang et al. ^2023e). However, UT also has certain limitations. Incorrect parameter choice can adversely affect product quality (Sun, Zhao, et al. ²⁰²³; Sun et al. ²⁰²⁴). Furthermore, UT requires specialized equipment, resulting in relatively high costs (Ouyang et al. 2025; Jiang et al. ²⁰²⁵). Additionally, its limited penetration depth may cause uneven thawing in large‐sized products (Çalışkan Koç et al. 2025; Wang et al. ²⁰²²; Zhu et al. ²⁰²⁵).

3.2.8. Atmospheric Pressure Dielectric Barrier Discharge Plasma Thawing

Plasma, known as the fourth state of matter, consists of ionized gases containing ions, electrons, free radicals, and photons. Nonthermal plasma is particularly attractive for food applications because the gas can change food surfaces without severe thermal damage (Atani et al. ^2025a). Atmospheric pressure dielectric barrier discharge plasma thawing (DBD‐PT) is an emerging physical field technology that utilizes nonthermal plasma generated by atmospheric pressure dielectric barrier discharge (DBD) to directly thaw frozen food (Atani et al. ^2025a). Its working mechanism involves synergistic multiphysical effects, including electric field effects, ionic wind, and dielectric heating, to achieve low‐power, high‐efficiency thawing (Atani et al. ^{2025a, 2025b}).

DBD‐PT offers advantages in efficiency, energy consumption, and quality retention. Atani et al. (2025a) reported that DBD‐PT reduced beef thawing time by approximately 82% compared to air thawing, lowered energy consumption by 82%–95% relative to MT, and improved energy efficiency by nearly 50%. In terms of quality retention, Atani et al. (2025b) found that 20 kV plasma treatment reduced thawing loss and cooking loss by 27.81%–56.04% and 36.38%–69.82%, respectively, compared to air thawing. Furthermore, DBD‐PT achieved complete microbial inactivation (0 CFU/g), whereas 3.3–3.8 log CFU/g of bacteria remained after MT (Atani et al. ^2025a). However, PT still faces limitations such as limited penetration depth, high equipment costs, and potential protein denaturation, all of which require further studies (Atani et al. ^2025b).

3.3. Pressure Field Thawing

3.3.1. High‐Pressure Thawing

High‐pressure thawing (HPT) utilizes hydrostatic pressure (100–800 MPa) to thaw (Çalışkan Koç et al. 2025; Zhang et al. ^2023a). High pressure substantially lowers ice melting point. This increases the difference between the medium temperature and the phase change temperature of water, thereby enhancing thawing efficiency (Jiang, Zhang, et al. ²⁰²³; Zhang et al. ^2023a). Additionally, high pressure may alter ice crystal structure, further facilitating the phase transition (Jiang et al. 2025).

HPT has advantages in thawing efficiency and quality retention (Li ^2024b; Rouillé et al. ²⁰⁰²). Li (2024b) reported that HPT (300 MPa) thawed salmon in 10.1 min versus 116.6 min for air thawing. HPT also has bactericidal effects and enhances product safety (Li ^2022b). However, HPT can easily cause quality deterioration and discoloration (Svendsen et al. 2022; Zhang et al. ^2023a), and its effectiveness depends on product size and shape (Çalışkan Koç et al. 2025; Svendsen et al. ²⁰²²), which limits its industrial application.

3.3.2. Vacuum Thawing

Vacuum thawing (VT) includes vacuum‐steam thawing (VST) and sublimation‐rehydration vacuum thawing (VSRT). Under vacuum (∼2400 Pa), water boils at approximately 20°C, generating steam that condenses on the food surface and releases latent heat, enabling rapid thawing (Kopeć et al. 2022; Zhang et al. ^2023a). In VSRT, ice inside the material first sublimates, forming an internal porous structure. Subsequent steam condensation within these pores further shortens the overall thawing time (Kopeć et al. 2022; Xue et al. ²⁰²⁵).

VT, particularly VSRT, can improve thawing efficiency while preserving quality (Çalışkan Koç et al. 2025). Xue et al. (2025) reported that VSRT thawed pork in 54.60 min, 55.37% faster than air thawing and 34.61% faster than VST. Furthermore, Xue et al. (2025) found that VSRT reduced energy consumption by 40.67% compared to VST, demonstrating an energy‐saving advantage. However, VT suffers from high equipment costs and relatively long thawing times (Wang et al. 2022). Although VSRT markedly improves efficiency, its complex process demands more sophisticated equipment and control (Xue et al. 2025). Overall, VT is suitable for heat‐sensitive materials requiring high quality and good oxidation resistance.

3.4. Functional Thawing Medium

Functional thawing medium refers to medium that actively participate in the thawing process due to their special physicochemical properties. The main types include magnetic nanoparticles (MNPs), saline/electrolyte solutions, and plasma‐activated water (PAW)/slightly acidic electrolyzed water (SAEW)/slightly basic electrolyzed water (SBEW; Çalışkan Koç et al. ²⁰²⁵; Li, Zhu, et al. ²⁰²¹).

MNPs, such as Fe₃O₄, are nanoscale materials composed of metal oxides such as iron and cobalt. Under microwave or RF fields, they respond magnetically to convert electromagnetic energy efficiently into heat (Çalışkan Koç et al. 2025; Li, Zhu, et al. ²⁰²¹). Their nanoscale dimensions provide an extremely high specific surface area, which significantly enhances heat transfer between the medium and the food, enabling rapid and uniform thawing (Jiang et al. 2025). A key advantage of MNPs is their ability to mitigate the localized overheating in MT/RFT (Çalışkan Koç et al. 2025; Cao et al. ²⁰¹⁹). However, challenges remain, including the need for long‐term safety validation of nanoparticle, unresolved issues with dispersion stability and recovery, and high equipment costs (Jiang et al. 2025).

Saline and electrolyte solutions optimize electric/electromagnetic field‐based thawing by tailoring the medium's electrical properties (dielectric constant, electrical conductivity). This approach encompasses two main strategies. The first is the dielectric pathway for RFT/MT, where a high‐dielectric‐constant medium surrounds the food to reduce electric field distortion at the air–food interface and greatly mitigate corner overheating (Li, Zhu, et al. ²⁰²¹). The second is the conductive pathway for OT, where a saline medium establishes a larger contact area between electrodes and the food, thereby solving the problem of poor surface contact (Fattahi and Zamindar 2020; Keshani et al. ²⁰²²).

PAW and SAEW/SBEW are biochemically active media that combine efficient thawing with simultaneous sterilization. PAW is generated by gas discharge, which produces reactive oxygen and nitrogen species that dissolve into the water (Çalışkan Koç et al. 2025). SAEW is produced by electrolysis of dilute hydrochloric acid or sodium chloride solutions; its primary bactericidal agent is hypochlorous acid which is 80 times more effective than hypochlorite ions at the same concentrations (Liao et al. 2020). SAEW has been approved as a food additive in the United States, Japan, and South Korea, giving it a regulatory advantage (Liao et al. 2020). SBEW is a beneficial drinking water that helps maintain the body's acid‐base balance. Its negative oxidation–reduction potential effectively inhibits protein and lipid oxidation and improves food texture (Li, Wang, Zeng, et al. ²⁰²⁴). During thawing, the antimicrobial species in these media continuously act on the food surface, attacking microbial cell membranes and DNA, thereby achieving simultaneous sterilization; Simultaneously, antioxidant species inhibit lipid and protein oxidation, preserving product quality (Li, Wang, Zeng, et al. 2024; Liao et al. ²⁰²⁰; Wang, Ding, et al. ²⁰²⁴).

3.5. Process Optimization of Traditional Thawing Technology

Conventional thawing techniques can be substantially enhanced through refined process design using traditional media such as air or water. This section discusses three optimization methods: forced‐air convection, multistage thawing, and recirculating faucets.

Forced‐air convection thawing utilizes fans to circulate air, significantly increasing the heat transfer coefficient. Choi et al. (2017) compared forced‐air convection with RFT and MT for pork thawing. Its simple equipment and low cost make it suitable for batch processing. Its main drawbacks are long processing time and uneven heating. Air‐impingement thawing further intensifies heat transfer by directing high‐velocity jets at the food surface, disrupting the thermal boundary layer (Choi et al. 2017). Moreover, jet‐impingement batch‐process thawing (JIT), which uses cyclically pulsed heated air, enhances heat transfer and reduces bacterial proliferation risk (Tiberi et al. 2022).

Multistage thawing balances thawing rate and quality by modulating the medium and temperature in stages. Zhang et al. (2017) found that 15 min of water immersion followed by low‐temperature, high‐humidity thawing achieved the shortest thawing time (35 min) and best protein preservation. This synergy leverages rapid passage through the danger zone during immersion and subsequent color/moisture protection under low‐temperature, high‐humidity conditions.

Running water thawing is common in commercial kitchens but is associated with substantial water waste (Leung et al. 2007). Recirculating faucet technology offers a viable solution (Fry et al. 2025). Fry et al. (2025) systematically compared recirculating faucets with running water thawing. The recirculating faucet used only 9 L of water per trial, whereas running water consumed 709–1466 L, showing a reduction of over 99%. Energy consumption of the recirculating faucet (0.040 kW h) was comparable to that of refrigerator thawing (0.021–0.036 kW h) and far lower than the embodied energy of running water (due to water supply). Additionally, the device costs only $412, providing an economical, water‐saving option for small and medium food companies.

3.6. Post‐Thawing Process Optimization

Although not strictly part of the thawing process, post‐thawing process, as an extension of the freezing–thawing chain, also affects final product quality and resource efficiency. Antunes‐Rohling et al. (2021) introduced high‐power ultrasound to rehydrate thawed cod fillets. Compared with traditional 3–7 days rehydration, ultrasound‐assisted rehydration (25 kHz, 2.9 W/kg) shortened rehydration time by 33%, increased weight gain by 18.6%, inhibited microbial growth, and reduced reliance on chemical additives. This case demonstrates the potential of physical field technologies to optimize whole‐chain processing efficiency and sustainability.

3.7. Comparison of Individual Thawing Technologies

In practical industrial applications, the selection of an appropriate thawing technology requires comprehensive consideration of multiple factors, including product characteristics, production scale, cost budget, and food safety. This section provides a horizontal comparison of these technologies from an industrial application perspective, and evaluates their compatibility with the three dimensions of Industry 5.0—human‐centricity, sustainability, and resilience (Table 3).

TABLE 3.

Comparison of individual thawing technologies for protein‐based foods.

Technology	Applicable product types	Energy consumption	Main limitations	Industrial maturity	Compatibility with Industry 5.0	References
MT	Small/medium meat cuts, fish fillets	Medium to high	Shallow penetration, localized overheating	Commercialized (household/industrial)	Resilience: mature technology lead to high supply chain resilience	Jiang et al. (2025), Yang and Chen (2022)
RFT	Large meat cuts, whole poultry	Medium	Edge overheating	Commercialized (industrial)	Sustainability: relatively low energy consumption; Resilience: mature technology lead to high supply chain resilience	Li, Li, et al. (2018), Jiang et al. (2025)
OT	Ground meat, surimi	Low	Requires electrode contact; uneven thawing due to differing electrical conductivity	Pilot/Industrial	Sustainability: energy‐efficient; Human‐centricity: safe operation (low voltage)	Cevik and Icier (2021), Keshani et al. (2022)
EFT	Meat cuts, aquatic products	Low	Ozone generation; safety risks (high voltage)	Pilot	Sustainability: low energy consumption	Hu et al. (2025), Jia et al. (2017)
MFT	Meat, aquatic products	Low	Mechanism disputed; high cost	Laboratory/Pilot	Sustainability: low energy consumption	Jiang et al. (2025), Wang, Lin, et al. (2024)
IRT	Thin‐layer products	Medium	Very shallow penetration, surface overheating	Commercialized	Human‐centricity: simple equipment	Gan et al. (2022), Hu et al. (2023)
UT	Small/medium meat cuts, fish	Medium	Shallow penetration, cavitation damage	Laboratory/Pilot	Sustainability: fast thawing, low food waste	Zhang et al. (2023a), Sun et al. (2021)
DBD‐PT	Surfaces, thin slices	Low	Penetration depth ≤ 15 mm	Laboratory	Sustainability: energy saving	Atani et al. (2025a, 2025b)
HPT	Small/medium cuts	High	Expensive equipment, protein denaturation	Laboratory	—	Çalışkan Koç et al. (2025), Li (2024b)
VT	Various meat types	Medium	Long processing time, high equipment cost	Commercialized	Human‐centricity: easy and safe operation; Sustainability: water saving	Wang et al. (2022), Xue et al. (2025)

3.7.1. Technical Characteristics and Applicable Scenarios

The applicable product ranges of different thawing technologies vary significantly. Owing to its long wavelength and large penetration depth, RFT is suitable for large meat cuts such as whole beef cuts, and whole poultry, and can be scaled up by increasing electrode area (Jiang et al. 2025). MT has limited penetration depth (typically only a few centimeters) and is more suitable for thin products such as small‐to‐medium meat slices and fish fillets, and can achieve medium‐scale processing in continuous tunnel microwave equipment (Yang and Chen 2022). OT relies on the electrical conductivity of the material and performs well for minced meat, meat paste, and surimi, but large cuts are prone to uneven heating due to differences in conductivity between fat and muscle (Cevik and Icier 2021). EFT and MFT impose no restrictions on product shape and can be applied to various meat products, but currently remain mainly at the laboratory scale, with limited large‐scale industrial applications (Hu et al. 2025; Jiang, Zhang, et al. ²⁰²³). IRT has an extremely shallow penetration depth (< 1 mm) and is only suitable for thin‐layer products (Gan et al. 2022). UT works well for small‐to‐medium products, but the attenuation of sound waves in solids limits its application in large‐sized meats (Zhang et al. ^2023a). DBD‐PT is currently only applicable to samples with a thickness of ≤ 15 mm (Atani et al. ^2025a). HPT typically operates in batch mode and is difficult to scale continuously (Çalışkan Koç et al. 2025). VT (especially VSRT) has strong adaptability to diverse product dimensions and has been applied in medium‐to‐large commercial thawing equipment (Wang et al. 2022; Xue et al. ²⁰²⁵).

In terms of process limitations, the performance of RFT is significantly affected by product shape, size, and dielectric properties, and irregular or nonuniform products are prone to edge overheating (Li, Li, et al. ²⁰¹⁸). MT has limited penetration depth and is less effective for thick products. OT depends on the uniformity of the material's electrical conductivity, and products with high fat content are difficult to heat evenly. UT and IRT have shallow penetration and are only suitable for thin‐layer products. These limitations determine the optimal application scenarios for each technology.

3.7.2. Food Safety

From a food safety perspective, rapid thawing technologies such as radio frequency, microwave, and ohmic can quickly pass through the temperature danger zone for microbial proliferation, thereby helping to control pathogen growth (Jiang et al. 2025). However, localized overheating in MT and RFT may create hot spots that could provide a niche for heat‐resistant microorganisms (Çalışkan Koç et al. 2025; Jiang et al. ²⁰²⁵). DBD‐PT can achieve complete surface sterilization (0 CFU/g) but only on the surface, so microorganisms may still exist internally (Atani et al. ^2025a). Thawing media, such as PAW, SAEW, and SBEW, can exert bactericidal effects during the thawing process (Liao et al. 2020). OT poses a risk of metal ion migration caused by electrode corrosion, and inert electrodes such as titanium are recommended, and the equipment should be regularly inspected (Çalışkan Koç et al. 2025). HPT has some bactericidal effects, but excessive pressure may cause protein denaturation and color deterioration (Svendsen et al. 2022). The low‐oxygen environment of VT may inhibit aerobic bacterial growth, but could also promote the growth of anaerobic bacteria (Wang et al. 2022).

3.7.3. Industrial/Commercial Applications

In terms of commercial maturity, RFT, MT, and VT already have established commercial equipment suppliers. George Weston Foods (GWF) deployed the world's largest drum‐type VST system at its Castlemaine plant in Australia, combined with a synergistic MT process. This system thaws frozen meat blocks from −18°C to 1–3°C in 5–6 h, which is only 50% of the time required by conventional methods. The company records that it has continuously refined this technology since 2000, with approximately 100 units now in commercial operation worldwide and nearly 50 field tests confirming its performance reliability (GEA Group ²⁰²⁶).

Other advanced thawing technologies are less mature than the three mentioned above, but have seen some industrial/commercial development. For instance, OT has entered pilot and early commercialization stages for homogeneous products such as surimi and minced meat (Keshani et al. 2022). EFT and MFT have small‐scale commercial equipment, such as domestic or small commercial thawing cabinets, but their large‐scale application requires further validation (Hu et al. 2025). UT remains largely at the laboratory research stage, although some pilot equipment has been used for seafood thawing (Zhang et al. ^2023a). DBD‐PT and HPT are still primarily in laboratory research due to high equipment costs, operational complexity, and stringent safety requirements (Atani et al. ^2025a; Li ^2024b).

From an economic perspective, RFT and MT require relatively high equipment investments. However, their fast processing speed and continuous operation make them suitable for large‐scale applications in major meat processing enterprises (Jiang et al. 2025). OT has lower operating costs. For example, 30 V/cm OT has an operating cost of $58.5 per 100 kg, significantly lower than water thawing at $108 per 100 kg, making it a cost‐effective option for small to medium‐scale processing of meat paste products (Chysirichote et al. 2024). VT equipment costs are higher than those of conventional AT, but its VSRT technology provides remarkable water savings and has been adopted by medium to large enterprises (Fry et al. 2025). EFT equipment is relatively low cost, making it suitable for small and medium enterprises (Hu et al. 2025).

3.7.4. Limitations in Evidence Quality Assessment

When comparing the performance of different thawing technologies, it is essential to recognize that there are significant differences in study designs, experimental conditions, and assessment protocols across the current literature. Therefore, reported improvements in thawing efficiency, quality preservation, or energy performance should not be interpreted as equivalent‐strength evidence, because the reliability of these conclusions is strongly influenced by experimental rigor, validation approaches, and reproducibility across different studies.

First, experimental designs and sample conditions vary considerably across studies. The food matrices investigated include pork (Choi et al. 2017; Wang et al. ²⁰²²), beef (Li, Li, et al. ²⁰¹⁸; Chen, Xie, et al. ²⁰²³), lamb (Sun, Jia, et al. ²⁰²³), chicken (Bedane et al. 2018; Zhang et al. ²⁰¹⁷), duck (Lung et al. 2022; Cao et al. ²⁰²⁵), fish (Bian et al. 2022; Lan et al. ²⁰²¹), and shrimp (Sun, Zhang, et al. ²⁰²³; Keshani et al. ²⁰²²). These matrices differ markedly in fat content, water distribution, and connective tissue composition, all of which directly influence thawing behavior and quality deterioration kinetics. Sample sizes range from small cubes (Fattahi and Zamindar 2020) to large commercial cuts (Zhu et al. 2025). Freezing rates, frozen storage durations, numbers of freeze–thaw cycles, and thawing endpoint temperatures also differ according to study objectives (Zhu et al. 2019; Lee et al. ²⁰²¹). Such variations may lead to overestimation or underestimation of technological advantages. For example, optimized laboratory parameters obtained from homogeneous samples may not represent the performance of heterogeneous industrial products with variations in size, composition, and initial freezing history. Therefore, conclusions derived from highly controlled laboratory experiments should be interpreted as mechanistic evidence rather than direct predictions of industrial performance (Zhang et al. ^2023a; Qiao et al. ²⁰²⁴; Çalışkan Koç et al. ²⁰²⁵).

Second, quality assessment indicators and measurement protocols lack standardization. Thawing loss is determined by varying methods, such as gravity drip, centrifugal loss, or cooking loss. Protein oxidation is typically assessed by carbonyl content or total sulfhydryl loss, yet the assay conditions differ across studies (Zhang et al. ^2023a; Chen, Xie, et al. ²⁰²³). Lipid oxidation is predominantly evaluated by the TBARS method, but extraction and chromogenic conditions vary considerably. Water‐holding capacity is assessed by diverse methods, including filter paper press, centrifugation, and LF‐NMR relaxometry (Zhang et al. ^2023g; Li, Xia, et al. ²⁰¹⁸). More importantly, many published studies emphasize improvements in individual indicators rather than comprehensive evaluation of overall thawing performance (Zhang et al. ^2023a). A reduction in thawing time, for instance, does not necessarily indicate superior product quality if accompanied by increased oxidation, structural damage, or microbial risks (Zhang et al. ^2023b; Tian et al. ²⁰²⁵; Qiao et al. ²⁰²⁴). Therefore, evidence based on a single quality parameter may provide an incomplete understanding of practical technological value. Future studies should adopt integrated evaluation strategies that consider multiple quality, safety, and processing parameters simultaneously.

Third, research scale and application contexts differ markedly. The vast majority of published studies are laboratory‐scale investigations with limited sample sizes. The thawing equipment used in these studies differs by orders of magnitude from industrial‐scale production lines in terms of power, cavity size, and material handling capacity. Assumptions underlying energy consumption and cost assessments (electricity price, equipment depreciation rate, labor cost, batch size) are also study‐specific, making direct economic comparisons unreliable (Çalışkan Koç et al. 2025; Balthazar et al. ²⁰²⁴).

In addition, reproducibility remains a critical concern for emerging thawing technologies. Many novel approaches demonstrate promising results based on optimized operating parameters reported by individual research groups, whereas independent validation under comparable conditions is still limited (Zhang et al. ^2023a; Qiao et al. ²⁰²⁴). Consequently, the robustness of some reported improvements remains uncertain, particularly for technologies that rely on complex interactions among electromagnetic fields, biological materials, and processing environments (Svendsen et al. 2022).

Therefore, the evidence supporting different thawing technologies should be interpreted through a hierarchy ranging from mechanistic laboratory validation, pilot‐scale demonstration, to industrial application evidence. Future research should aim to establish unified standards for thawing performance evaluation, including standardized sample preparation protocols, definitions of thawing endpoints, measurement methods for key quality indicators, and frameworks for calculating energy consumption and costs, to enhance the reproducibility of research results and their comparability across studies.

3.8. Compatibility Analysis With Industry 5.0

In terms of human‐centricity, the focus is on how advanced thawing technologies enhance operational safety and convenience. Low‐voltage EFT and VT, owing to their low operating voltage and high degree of automation, reduce the skill requirements and safety risks for operators (Hu et al. 2025; Wang et al. ²⁰²²). The low‐voltage programmed mode of OT is particularly suitable for household and small commercial use (Zhang et al. ^2024e). Additionally, IRT equipment is simple and easy to operate (Gan et al. 2022). However, HPT (high hydrostatic pressure) and DBD‐PT (high voltage) require strict safety protection measures and high operator training requirements, which may be less aligned with the human‐centric concept than other thawing techniques (Çalışkan Koç et al. 2025; Atani et al. ^2025a).

In terms of sustainability, the assessment focuses on energy consumption, carbon emissions, water consumption, and product waste of different technologies. Regarding energy consumption, there are significant differences in energy consumption per unit of product among different thawing technologies. OT has an energy conversion efficiency close to 100% and the lowest energy consumption per unit of product (Çalışkan Koç et al. 2025). RFT and VSRT can reduce energy consumption by 30%–50% compared to conventional AT (Choi et al. 2017; Xue et al. ²⁰²⁵). However, the energy efficiency of other technologies requires further evaluation. For instance, although EFT and MFT have low intrinsic power consumption, their practical application often requires refrigeration systems to maintain low temperatures, potentially making overall energy consumption far higher than theoretical estimates (Jiang et al. 2025; Wang, Lin, et al. ²⁰²⁴). Additionally, DBD‐PT has demonstrated low energy consumption at laboratory scale, but data on its energy efficiency after scale‐up remain unavailable (Atani et al. ^2025a).

In terms of carbon emissions, those generated by the cold chain cannot be overlooked. Studies indicate that low‐temperature (frozen) storage increases approximately 1.7 kg CO₂ eq/kg to meat products (Dong and Miller 2021). Rapid thawing technologies, such as RFT and OT, can reduce carbon emissions during the thawing phase by shortening processing time. HPT equipment consumes high energy and operates in batch mode, resulting in a relatively high carbon footprint per unit product (Çalışkan Koç et al. 2025).

In terms of water consumption and food waste, conventional running water thawing consumes 709–1466 L per batch, whereas a recirculating faucet uses only about 9 L, achieving a water‐saving potential of over 99% (Fry et al. 2025). Quality deterioration due to improper thawing is a major source of food waste. For example, the quality loss caused by localized overheating in MT contributes to increased food waste (Jiang, Zhang, et al. ²⁰²³).

In terms of resilience, advanced thawing technologies should be able to adapt to variations in raw materials, equipment fluctuations, and supply chain disruptions. Regarding raw material adaptability, different technologies exhibit markedly different capacities to handle variations in species, cuts, and freezing history. Although RFT offers deep penetration and high heating rates, sample shape and size significantly affect heating uniformity (Li, Li, et al. ²⁰¹⁸). MT is constrained by limited penetration depth: as sample size increases, surface temperature rises rapidly while the center remains frozen, which leads to poor adaptability for large cuts. OT relies on the electrical conductivity of the material. Differences in fat content lead to varying heating rates, causing uneven heating. VT demonstrates strong adaptability to product shape and size, as its heat transfer mechanism is not limited by product geometry (Çalışkan Koç et al. 2025).

In terms of equipment and process fluctuation response, SMT dynamically adjust frequency and power through real‐time feedback control, effectively counteracting equipment performance drift (Yang and Chen 2022). In OT, optimization of voltage gradient and frequency can partially compensate for nonuniformity caused by conductivity differences (Liu et al. 2017). In contrast, UT is highly sensitive to power and frequency parameters, with cavitation intensity fluctuating substantially with minor parametric variations, resulting in poor resilience in this regard (Zhang et al. ^2023a). However, in terms of supply chain disruption response, individual thawing technologies cannot independently address these problems. Resilient responses require integration with intelligent sensing and decision‐making systems, which will be elaborated in the subsequent section on intelligent thawing systems (Guo et al. 2026; Kannapinn et al. ²⁰²⁶).

In summary, individual thawing technologies each have their own strengths and weaknesses with respect to the three core principles of Industry 5.0 in practical industrial applications. Synergistic strategies can overcome the limitations of individual technologies, achieving superior performance and greater compatibility with Industry 5.0 principles.

4. Synergistic Thawing Strategies

Single thawing techniques may not simultaneously achieve efficiency, consistency and quality. Systematically combining two or more technologies or combining technologies with functional media would bring complementary advantages and additional effects and achieve higher levels of performance and Industry 5.0 value. This section reviews representative synergistic thawing strategies and their applications. It further critically evaluates how different synergistic approaches overcome the intrinsic limitations of individual technologies, while discussing their industrial feasibility and compatibility with the human‐centric, sustainable, and resilient objectives of Industry 5.0. Key applications of synergistic thawing technologies are summarized in Table 2.

4.1. Physical Field–Physical Field Synergy

Based on the synergistic mechanisms, physical field–physical field synergy can be divided into sequential alternating type and simultaneous superimposed type.

The sequential alternating type mitigates the drawbacks of individual technologies by alternately applying different physical fields. Hu et al. (2023) developed an alternating IR + MT method, which is a typical sequential alternating synergistic technology. Infrared has shallow penetration but high surface heating efficiency, while microwaves heat quickly but are prone to localized overheating. Alternating the two methods balances efficiency and uniformity. Results showed that IR + MWT thawed pork in only 11.81 min (66.5 min for AT) with a thawing loss of 1.92%.

The simultaneous superimposed type applies multiple physical fields at the same time, exploiting their coupling effects to enhance thawing performance. Numerous studies have explored this approach. A typical example is Min et al. (2016), who combined HPT with OT. This synergistic technology achieved a thawing time of only 0.8 min, significantly shorter than HPT alone (11.5 min) or OT alone (5.5 min), while maximizing meat quality retention and improving temperature uniformity. Cai et al. (2020) combined UT with MT for largemouth bass, and found that the combined treatment yielded protein viscoelasticity closer to fresh samples and superior gel properties compared to single thawing. Moreover, studies have shown that the combination of MT and VT can simultaneously shorten thawing time and inhibit the degradation of heat‐sensitive nutrients (Lin et al. 2025).

4.2. Physical Field–Medium Synergy

The synergistic mechanisms of physical field‐medium synergy can be classified into three categories. The first is the dielectric matching pathway, mainly for RFT, MT, and IRT processes, where materials with a high‐dielectric constant, such as MNPs and glycerol solution, are applied around the food to accelerate thawing rate and mitigate corner overheating (Tian et al. 2024; Li, Ma, et al. ²⁰²¹). Xu et al. (2024) used MNPs combined with multifrequency UT to thaw salmon, significantly improving thawing rate and water retention while protecting myofibrillar protein structure. Cai et al. (2020) developed MNPs‐MT and MNPs‐IRT synergistic methods, both of which better preserved freshness and inhibited amine formation compared to MT or IRT alone. Furthermore, Li, Ma, et al. (2021) tested crushed ice, ethanol, and glycerol solutions as surrounding media for RFT, confirming that 70% glycerol solution was the most effective, as it is nontoxic, nonflammable, odorless, and could significantly improve tempering uniformity.

The second is the conductive coupling pathway, mainly for OT processes. The food is immersed in an electrolyte solution to establish a low‐impedance ion pathway between the electrodes and the food to solve the problem of poor surface contact of frozen foods and effectively increasing thawing rate (Fattahi and Zamindar 2020). For example, Keshani et al. (2022) developed a novel immersion OT method using brine as the electrolyte, increasing thawing rate fivefold and significantly improving protein solubility and reducing thawing loss compared to conventional methods (p < 0.05).

To address microbial contamination and protein loss in traditional thawing, a third physical field‐medium synergy pathway has been developed: using PAW, SAEW, or SBEW as thawing media, all of which release reactive oxygen/nitrogen species or hypochlorous acid during thawing to achieve simultaneous sterilization and antioxidant effects (Kong et al. 2022; Kong et al. ²⁰²³; Li, Wang, Zeng, et al. ²⁰²⁴). For instance, Qian et al. (2022) combined PAW with UT, reducing bacterial counts by 0.62–1.17 log CFU/g and protein loss by 17.1%–23.1%, respectively. Li, Zhang, et al. (2025) further optimized UT power and found that 300 W UT combined with PAW best preserved product quality.

4.3. Integrated Freezing–Thawing Optimization

Integrated freezing–thawing optimization involves systematic technologies or strategies applied throughout the entire freezing–thawing process to achieve synergistic effects beyond single‐stage interventions (Jiang et al. 2025). Based on the mode of action, integrated freezing–thawing optimization can be divided into four categories: single physical field throughout the process (Wu et al. ^2023a; Wu et al. ^2023b), sequential optimization with different physical fields (Hu et al. 2026), functional material‐assisted throughout (Zhou et al. 2024), and freezing–thawing rate matching (Duanmu et al. 2024).

Some physical field technologies can simultaneously optimize both freezing and thawing, making them suitable for single physical field throughout the freezing–thawing process. Alternating electric field (AEF) is a typical example. Wu et al. (2023a) tested AEF throughout beef freezing–thawing–aging. During freezing, AEF induced water molecule rotation, broke hydrogen bonds, and slowed ice crystal growth. During thawing, it accelerated heat transfer and reduced residence time in maximum ice crystal formation zone. Deng et al. (2024) applied a LVEF throughout tofu freeze–thawing. After freezing at −50°C and thawing at 10°C, the tofu retained 91.1% of its hardness and 87.0% of its elasticity.

Applying different physical fields during freezing and thawing allows their complementary advantages to be fully exploited. Hu et al. (2026) combined MFF with EFT. The magnetic field inhibited ice crystal growth during freezing, while the electrostatic field accelerated thawing. This method reduces drip loss by 56.24% in beef and 59.12% in pork with better retained protein. Safari et al. (2024) combined infrared pretreatment with OT for turkey breast. Infrared pretreatment shortened thawing time by 50.14%–69.23%, reduced total losses by 20%–70%, and better preserved protein secondary structure.

Functional materials introduced before freezing, such as AFPs or MNPs, can act throughout the freezing–thawing cycle: inhibiting ice nucleation and growth during freezing, and aiding uniform heating or protecting protein conformation during thawing. Zhou et al. (2024) added 0.2% AFP to minced pork before subjecting it to 25/−1°C staged thawing. The AFP continuously inhibited ice recrystallization during repeated freeze–thaw cycles; combined with staged thawing, this strategy significantly reduced thawing loss, suppressed lipid and protein oxidation, and minimized microstructural damage. Wang, Li, et al. (2023) and Li, Wang, Fan, et al. (2024) applied MNPs (Fe₃O₄) throughout low‐temperature freezing and MT. This method achieved MP solubility of 87.28% in salmon fillets, minimized lipid and protein oxidation, and stabilized protein secondary and tertiary structures.

Combining freezing and thawing rates optimizes final product quality through ice crystal formation and melting kinetics. Duanmu et al. (2024) studied two freezing rates (0.35 and 0.47 cm/h) and three RF tempering rates (0.5, 1.0, and 1.5°C/min) to thaw tuna, finding that “fast freezing + slow tempering” was optimal. Fast freezing produced small ice crystals, and slow tempering gently regulated their melting. This combination ultimately achieved the best preservation of color and protein solubility.

4.4. Horizontal Comparison of Synergistic Thawing Technologies and Compatibility Analysis With Industry 5.0

Compared with individual technologies, synergistic strategies overcome the limitations of individual technologies through functional complementarity, yet they also introduce new challenges such as increased system complexity and higher costs. From an industrial application perspective, this section provides a cross‐comparison of various synergistic strategies and evaluates their compatibility with the Industry 5.0 framework (Table 4).

TABLE 4.

Comparison of synergistic thawing strategies.

Type	Specific technology	Application scenario	Industrial maturity	Main challenges	Compatibility with Industry 5.0	References
Physical field–physical field	Alternating IRT + MT	Pork, poultry	Laboratory/Pilot	Complex equipment, timing control required	Sustainability: energy saving; Resilience: improved uniformity and stability	Hu et al. (2023)
Physical field–physical field	PHT + OT	Beef	Laboratory	Expensive high‐pressure equipment	Sustainability: ultra‐fast thawing reduces waste	Min et al. (2016)
Physical field–physical field	MT + VT	Krill, pork	Pilot	Cost of vacuum system	Sustainability: low‐temperature avoids thermal degradation, fast thawing reduces waste	Lin et al. (2025)
Physical field–medium	RF + 70% glycerol	Beef	Laboratory	Medium recovery issues	Resilience: improved uniformity and stability	Li, Zhu, et al. (2021)
Physical field–medium	OT + brine	Tuna	Laboratory/Pilot	Salt penetration	Human‐centricity: low voltage, safe operation; Sustainability: water saving	Keshani et al. (2022)
Physical field–medium	MT + MNPs	Seabass, squid	Laboratory	Nanoparticle safety	Sustainability: uniform heating reduces waste	Cao et al. (2019)
Physical field–medium	PAW + UT	Chicken	Laboratory	PAW preparation cost	Sustainability: simultaneous sterilization; Human‐centricity: high operational safety	Qian et al. (2022)
Freezing–thawing integrated	AEF throughout freezing and thawing	Beef	Pilot	Electric field uniformity	Sustainability: reduces losses	Wu et al. (2023a)
Freezing–thawing integrated	MFF + EFT	Beef, pork	Pilot	Equipment integration	Resilience: sequential optimization; Sustainability: reduces thawing loss	Hu et al. (2026)

The synergistic effects of different strategies vary significantly. For instance, among physical field–physical field synergies, alternating IRT and MT shortened thawing time to 11.81 min with a thawing loss of only 1.92%, making it suitable for high‐quality pork products (Hu et al. 2023). The combination of MT and VT reduced thawing time by 62 min for krill while inhibiting the degradation of heat‐sensitive nutrients, making it ideal for high‐value aquatic products (Lin et al. 2025). In physical field‐medium synergies, RFT combined with 70% glycerol solution is particularly effective for uniform thawing of irregularly shaped products (Li, Zhu, et al. ²⁰²¹). In freezing–thawing integrated optimization, the application of an AEF throughout the entire process significantly reduced thawing and cooking losses in beef while stabilizing protein secondary and tertiary structures, making it suitable for quality management of high‐end beef (Wu et al. ^2023a).

Synergistic thawing strategies align well with the Industry 5.0 pursuit of sustainability and resilience. In terms of sustainability, synergistic thawing strategies generally achieve significant reductions in thawing time and thawing loss, thereby decreasing food waste (Min et al. 2016; Qian et al. ²⁰²²; Wu et al. ^2023a). In terms of resilience, synergistic thawing strategies enhance the ability to cope with raw material variability and process fluctuations through functional complementarity, substantially improving system robustness and process stability (Li, Zhu, et al. ²⁰²¹; Cao et al. ²⁰¹⁹). In terms of human‐centricity, synergistic thawing technologies often demand more complex control systems, imposing higher requirements on operator training and operational safety, which may not fully align with this principle. However, intelligent thawing systems and Industry 5.0 enabling technologies, such as augmented reality (AR) assistance, collaborative robots (Cobot), and low‐code platforms, can effectively lower operational barriers (He et al. 2022; Aly et al. ²⁰²³).

5. Intelligent Thawing System

Unlike Sections 3 and 4, which focus on thawing technologies themselves, this section shifts from process technologies to system intelligence, introducing the digital infrastructure that enables the subsequent realization of Industry 5.0 principles. Rather than describing individual digital technologies in isolation, this section analyzes how multidimensional sensing, intelligent decision‐making, and digital twins function as complementary subsystems that collectively support human‐centric, sustainable, and resilient intelligent thawing systems.

5.1. Multidimensional Sensing Networks

5.1.1. Optical Sensing

Within the intelligent thawing unit of Industry 5.0, optical sensing technologies acquire multidimensional information on chemical composition, molecular structure, physical state, and appearance attributes by measuring matter interactions with electromagnetic waves (absorption, emission, scattering, refraction, and reflection), as shown in Figure 4a. Their noncontact nature, rapid response, and imaging capabilities provide the data for precise decision‐making in intelligent thawing units.

FIGURE 4.

Key components of data‐driven intelligent thawing units. (a) Multidimensional sensing networks, reference from Anderssen et al. (2020), Cheng et al. (2018), Ma et al. (2025), Tang et al. (2025), and Zotov et al. (2021). (b) Artificial Intelligence (AI), reference from Anderssen et al. (2020), Hashem et al. (2025), and Sardari et al. (2025). (c) Freezing–thawing cycle. (d) Digital twin (DT), reference from Kannapinn et al. (2022).

Near‐infrared (NIR) spectroscopy can capture information on water status, protein conformation, and lipid oxidation by monitoring vibrational transitions of molecular bonds (Zhang et al. 2023). Ouyang et al. (2022) used a portable visible‐NIR spectroscopic system for rapid, nondestructive detection of cooking loss of frozen pork. It provides a new perspective for frozen meat quality measurements without thawing.

Raman spectroscopy provides molecular fingerprint information such as protein secondary structure and lipid unsaturation through inelastic light scattering (Qiu et al. 2026). Chen, Xie, et al. (2023) predicted thawing loss, water content and color of frozen/thawed beef using Raman spectroscopy and achieved a high correlation coefficient of 0.971 (thawing loss).

Terahertz time‐domain spectroscopy (THz‐TDS) can accurately reflect ice crystal formation and melting processes of high‐protein foods due to its high‐sensitivity to polar molecules (water; Fu et al. ²⁰²⁴). Zotov et al. (2021) used THz‐TDS to analyze frozen depth in meat, and detected ice crystal position and state at a depth of 0.657 mm within 3 cm thick bovine adipose tissue. He, Ung, et al. (2016) used reflective THz‐TDS to study the freezing–thawing process of meat, providing a potential means for monitoring the ice crystal melting front in thawing units.

Hyperspectral imaging (HSI) integrates spectral analysis with spatial imaging to simultaneously capture chemical information and spatial distribution of materials (Zhang et al. 2023). Cheng et al. (2018) scanned frozen pork directly without thawing using a NIR–HSI system, and successfully predicted myofibrillar cold structural deformation degree and avoid secondary damage during thawing. Anderssen et al. (2020) used HSI to predict drip loss in cod samples taken under different freezing–thawing protocols, and found HSI data could accurately predict drip loss under different treatments.

Fluorescence spectroscopy detects the fluorescent properties of endogenous fluorophores in samples, such as tryptophan, tyrosine, and phenylalanine (Zhang, Kim, et al. ²⁰²²). Cheng et al. (2022) successfully predicted the degree of protein oxidation in frozen–thawed pork using fluorescence–HSI and generated visualization maps of oxidation levels, providing spatial dimension information for quality monitoring.

IF thermography collects real‐time temperature distribution data by measuring the IF intensity from object surfaces. Döner et al. (2020) applied thermal imaging to measure temperature distribution in minced beef during OT, and found temperature in rectangular samples distributed more evenly than in cylindrical samples. Pereira et al. (2017) mapped heat transfer coefficient distribution on packaging surfaces during freezing with infrared thermography, revealing local heat transfer heterogeneities which cannot be obtained using only single‐point thermocouple measurements. He et al. (2022) placed an 8 × 8 pixel infrared array sensor into a smart microwave oven and achieved real‐time monitoring of food surface temperature distribution.

Computer vision (CV) technology extracts quality‐related information by capturing appearance features such as color and texture. Sardari et al. (2025) classified images of ultrasound‐assisted frozen meat with deep learning (DL) algorithms and achieved an accuracy of 97.75%. Zade et al. (2025) combined smartphone imaging with machine learning (ML) and found that the HSV color space was more sensitive to storage condition changes than RGB.

Schlieren imaging visualizes flow field distributions in a transparent medium. Zade et al. (2025) using an in situ schlieren imaging system combined with optical flow methods, achieved the first visualization of hydrodynamics during UT, providing direct experimental evidence for studying the hydrodynamic mechanisms of thawing.

5.1.2. Electromagnetic Sensing

In the intelligent thawing unit of Industry 5.0, electromagnetic sensing technologies provide data on cell membrane integrity and water status by applying electric or magnetic fields to materials and measuring their impedance, dielectric constant, and relaxation time (T₂). These measurements provide the data for precise decision‐making.

Impedance spectroscopy measures electrical resistance and capacitance of foods under alternating currents. During freeze–thaw cycles, ice crystal formation disrupts cell membrane structures and increases permeability and intracellular electrolyte leakage, which leads to large changes in resistance and capacity (Abie et al. 2021). Zhang et al. (2024d) developed a multifrequency bio‐impedance spectroscopy monitoring system with ML to assess quality changes in Atlantic salmon during freeze–thaw cycles. The system achieved 96.83% accuracy in identifying freezing–thawing adulterated salmon, demonstrating the potential of impedance spectroscopy as a fast, low‐cost authenticity detector.

LF‐NMR measures the relaxation behavior of hydrogen protons in a magnetic field, enabling nondestructive determination of water status and distribution. Disruption of muscle cell structures by ice crystals during freeze–thaw cycles causes water migration, which can be detected by LF‐NMR. For instance, ultrasound‐assisted IF shortened T₂₁ and T₂₂ in pork, indicating that it could reduce water mobility and thawing loss (Zhang et al. ^2023g). Li, Xia, et al. (2018) used LF‐NMR to study water dynamics in turbot during freeze–thaw cycles and employed principal component analysis (PCA) to distinguish products that have different cycle numbers.

Magnetic resonance imaging (MRI) is a spatial extension of NMR to visualize the spatial distribution of nuclear spins. Image brightness reflects hydrogen proton density and mobility, providing a powerful tool for nondestructive assessment of water status and distribution (Zhang et al. ^2023g). Frelka et al. (2019) used MRI to study quality loss in frozen/thawed chicken breast and found significant differences in proton density images between nonfrozen and freeze–thawed samples. Anderssen et al. (2021) combined MRI with a convolutional neural network (CNN) to quantify and map freezing‐induced tissue damage in cod, achieving 95.2% test set accuracy, demonstrating MRI combined with DL as a powerful technique for assessing frozen food quality.

Emerging RF/microwave sensing technologies offer a new paradigm for thawing process monitoring. Bulusu and Dhekne (2025) proposed uThaw, a solid–liquid phase transition detection system based on ultra‐wideband (UWB) wireless signals for noninvasive monitoring of food thawing. As the complex permittivity of water changes dramatically during phase transition at microwave frequencies (∼4 GHz), it leads to remarkable variations in reflection, absorption, and propagation characteristics of wireless signals between ice and water. The system emits UWB signals and receives reflections, extracts channel impulse response (CIR) features, and compares similarity scores to historical averages to check if the food is frozen, frozen or undergoing phase transition. This technology has advantages including penetration of common packaging materials, direct sensing of water phase transition rather than indirect inference, real‐time continuous monitoring, and low‐cost deployment.

5.1.3. Other Sensing Technologies

In Industry 5.0 intelligent thawing units, beyond optical and electromagnetic response technologies, acoustic, flavor, molecular omics, and microimaging techniques constitute multidimensional sensing complements. These technologies capture material state information from different physical levels, building a comprehensive sensing system ranging from macro to micro, and from appearance to essence.

Acoustic sensing technologies use propagation characteristics of sound waves in medium to obtain information on internal structure, density, and phase transition state of materials (Chen et al. ^2024b; Jha et al. ²⁰²³). Chen et al. (2024b) developed a nondestructive method based on ultrasonic signals and velocity for real‐time quantification of beef thawing degree. Its main advantages include deep penetration capability, high sensitivity to solid–liquid phase transitions, and nondestructive real‐time monitoring (Jha et al. 2023).

Electronic nose simulates the biological olfactory system through gas sensor arrays, performing pattern recognition on volatile compounds released by samples to indirectly reflect quality changes during thawing. Mirzaee‐Ghaleh et al. (2020) applied an electronic nose combined with ML algorithms to identify fresh chicken from frozen–thawed chicken and predict remaining shelf life. Advantages include rapidity, nondestructiveness, and suitability for rapid screening at fresh food supply chain nodes.

Molecular omics technologies reveal molecular‐level changes in freezing–thawing activity from high‐throughput analysis of metabolites, proteins, or enzyme activities, providing deep information for quality assessment and authenticity identification. For instance, metabolomics uses liquid chromatography‐high resolution mass spectrometry (LC‐HRMS) or NMR to directly extract fingerprint information of all small‐molecule metabolites in samples (Chiesa et al. 2020; Shumilina et al. ²⁰²⁰; Stella et al. ²⁰²²). Proteomics extracts protein expression profile differences through 2D electrophoresis (2‐DE) and mass spectrometry (Guglielmetti et al. 2018; Kim et al. ²⁰¹⁵). Enzyme activity assays, based on the principle of cell membrane damage leading to leakage of mitochondrial and cytoplasmic enzymes, trace freezing–thawing history by detecting specific enzyme activities (Biswas et al. 2024; Jaiswal et al. ²⁰²⁵). Immunological detection utilizes enzyme‐linked immunosorbent assay (ELISA) to target specific proteins for high‐sensitivity detection (Rahman, Biswas, et al. ²⁰²⁴).

X‐ray computed tomography (X‐ray CT) is a noninvasive, nondestructive 3D imaging technology that directly visualizes food microstructure through transmission imaging (Zhang et al. ^2023g). In frozen meat products, X‐ray CT could detect ice crystal distribution and pore structure changes, and provide direct evidence for understanding freezing–thawing damage mechanisms (Zhang et al. ^2023g).

5.2. Intelligent Decision‐Making and Control

5.2.1. AI‐Based Control Path

In the intelligent thawing unit of Industry 5.0, the perception network must translate multidimensional real‐time data into autonomous decisions and precise execution commands, though intelligent algorithms are deployed at the edge, including AI‐based and traditional intelligent control paths. As shown in Figure 4b, the AI path utilizes the ML/DL models to learn complex mappings between inputs (sensory data) and outputs (quality indicators, state categories) from historical data, providing real‐time prediction and decision optimization.

5.2.1.1. Quality Classification

Tang et al. (2025) systematically evaluated nine ML algorithms using VIS/NIR spectral data to classify frozen pork storage time, achieving 85.8% accuracy across 13 storage time points. It demonstrates that spectral data are closely related to storage history, allowing for feed‐forward control for thawing based on material frozen storage history. Sardari et al. (2025) employed CNN to classify images of ultrasound‐assisted frozen meat, achieving 97.75% accuracy for thawed meat and 97.68% for cooked meat, highlighting that DL has advantage of automatic feature extraction. Park et al. (2023) combined NIR–HSI with partial least squares (PLS)‐discrimination analysis (DA) and support vector machine (SVM) to classify beef storage states, achieving 96.57% test accuracy and 100% recognition rate for fresh meat.

5.2.1.2. Quantitative Prediction of Quality

Chen, Xie, et al. (2023) provided nondestructive prediction of thawing loss, water content, and color in frozen/thawed beef by Raman spectroscopy and multivariate calibration. Models built with competitive adaptive reweighted sampling (CARS)‐PLS and uninformative variable elimination (UVE)‐PLS achieved correlation coefficients of 0.971 for thawing loss, 0.932–0.994 for color values, and 0.928 for water content.

5.2.1.3. Models Combination and Integration

Given the limitations of single models in complex classification tasks, models combination significantly improves overall stability and accuracy. Turgut et al. (2025) compared 10 ML classifiers across three tasks using a low‐cost multispectral sensor, employing a soft voting classifier (SVC) for integration. The final SVC models (k‐nearest neighbors (k‐NN) + SVM + multilayer perceptron (MLP) + quadratic discriminant analysis (QDA) achieved 93% accuracy in cold chain break detection, 92% in chicken type identification, and 83% in predicting postrefreezing storage time.

5.2.1.4. Explainable AI

Although the AI models provide a good performance in solving complicated patterns, the “black box” characteristic of these models prevents their practical deployment in food safety regulation. Hashem et al. (2025) proposed a complete solution combining explainable AI (XAI) with mechanistic analysis. They compared 22 classifiers on 6000 visible‐shortwave near‐infrared (Vis‐SWNIR) spectra, identifying linear discriminant analysis (LDA) as the optimal method for distinguishing fresh, aged, and frozen–thawed beef with 86.5% accuracy. They further introduced 2 XAI methods (SHAP, LIME) to attribute LDA predictions to certain spectral features, and to reveal molecular dynamics that discriminate “aged” from “frozen” samples. The power analysis showed sufficient statistical power (99.4%) as required for industrial use.

5.2.2. Traditional Intelligent Control Path

The traditional intelligent control path uses prior knowledge of physical mechanisms to introduce first‐principles mathematical models to achieve real‐time state estimation and control decisions using measurable data. These methods require no large‐scale historical datasets, feature physically meaningful model parameters, and are suitable for scenarios with limited sensor deployment but clear physical laws.

5.2.2.1. Physics‐Based Simulation Optimization

Physics‐based simulation enables precise modeling of thawing processes and optimization of process parameters through coupled electromagnetic‐thermal multiphysics models. Zhu et al. (2025) employed COMSOL Multiphysics to simulate beef thawing, identifying an optimal endpoint of no more than −2°C. SMT optimized based on simulation results effectively avoided localized overheating, maintained better water‐holding capacity and lower oxidation, and reduced thawing time. Boillereaux et al. (2011) developed a software sensor CLPP to solve the industrial problem of unmeasurable internal temperatures during MT. Using only measurable variables (real‐time upper and lower surface temperatures and microwave power settings), the algorithm estimated the temperature at 21 internal nodes and the temperature‐dependent dielectric property function.

5.2.2.2. Physics‐Based Feature Extraction

Physics‐based feature engineering designs feature extraction algorithms based on optical, electrical, and other physical principles, producing features with clear physical meaning, strong model generalization, and no requirement for extensive training data. Cheng et al. (2018) first introduced the spectral angle mapping (SAM) algorithm for food quality prediction. This measurement approach is insensitive to illumination variations, naturally suited for handling interference from uneven surface reflection in frozen samples. Partial least squares regression (PLSR) models built on SAM features achieved R ² values of 0.896 for surface hydrophobicity prediction and 0.879 for Ca²⁺‐ATPase activity prediction.

5.2.2.3. Statistics‐Based Feature Extraction

Statistics‐based feature extraction uses multivariate statistical methods to automatically identify key variables from high‐dimensional data to discover unknown biomarkers. Shumilina et al. (2020) employed NMR metabolomics to distinguish fresh Atlantic salmon from thawed ones, input data into PCA models for dimensionality reduction and visualization, ultimately identifying aspartate as a biomarker for freezing–thawing history. Velioğlu et al. (2015) combined Raman spectroscopy with PCA for species identification and freshness analysis of 64 samples from six fish species, successfully achieving sample clustering and revealing correlations between freeze–thaw cycles and principal component spatial distribution.

5.2.2.4. Device‐Level Real‐Time Control Based on Classical Algorithms

Device‐level real‐time feedback control provides reliable autonomous control for precision thawing, while classical control theory algorithms provide reliable, independent control on low‐cost embedded devices. Yang and Chen (2022) developed dynamic defrosting strategies to address thermal runaway issues in SMT. The system dynamically adjusts frequency and power input by real‐time capture microwave power reflections, enabling the thawing process to adapt to sample size, shape, and position variations. Adaptive power control successfully reduced temperature standard deviation and hot spot temperatures in thawed‐frozen pork chops, demonstrating that real‐time feedback‐based dynamic control effectively improves thawing uniformity. Feedback mechanisms and optimization algorithms in solid‐state microwave systems avoid localized overheating due to real‐time monitoring and energy output adjustment, thus being more reliable and accurate.

5.3. Digital Twins

In the intelligent thawing unit, DT technology serves as a cutting‐edge tool for enabling dynamic virtualization and predictive control across the entire supply chain (Xu et al. 2023). The construction of a DT involves three steps: first, establishing a physics‐based multiphysics coupling model; second, fusing real‐time sensor data with model predictions to perform online calibration of model parameters; third, conducting state prediction and decision‐making based on the calibrated model (İnanlar and Altay 2026). The DT acts as a bridge between physical processes and virtual models, aiming to achieve dynamic perception, prediction, and optimization of the thawing process through real‐time data (Xu et al. 2023).

In thawing scenarios, the core monitoring objects encompass three aspects: physical fields (spatiotemporal temperature distribution within the material and evolution of dielectric properties with temperature and frequency), phase transition kinetics (propagation rate and spatial distribution of the ice crystal melting front), and quality evolution (real‐time estimation of water‐holding capacity changes and protein denaturation). For instance, temperature fields can be acquired in real time via infrared thermography (Cheng et al. 2025). Ice crystal status can be tracked online through ultrasonic attenuation spectroscopy or LF‐NMR (Jha et al. 2023; Tatarinov et al. ²⁰²⁶; Diao et al. ²⁰²¹). Dielectric properties can be continuously measured via impedance spectroscopy (Dell'Osa et al. ²⁰²¹; Abie et al. ²⁰²¹). For microbial risk prediction, current approaches based on spectroscopic sensing have enabled real‐time microbial monitoring. For instance, Wang et al. (2025d) employed DL‐driven HSI to achieve real‐time monitoring and growth modeling of Pseudomonas and Lactobacillus in chilled beef. Park et al. (2026) successfully predicted total aerobic bacteria counts in beef using NIR–HSI and ML. Although no studies have yet integrated microbial risk prediction into DT systems for thawing, DT systems capable of predicting and controlling microbial community dynamics have already been established in the fermentation field (Zhao, Jiao, et al. ²⁰²⁵). Integrating the spectroscopically driven microbial prediction models into thawing DT frameworks represents an important direction for future research.

The core value of DT lies in their ability to fuse these dispersed sensing data into a unified “virtual internal view” through physics‐based models, enabling operators to “see” deep states that sensors cannot directly measure, thereby supporting more precise decision‐making. For example, Jiang, Yang, et al. (2023) developed a numerical model for RFT of irregularly shaped hairtail fish, systematically evaluating the effects of different placement configurations and stacking layers on thawing performance. Goñi et al. (2022) developed a three‐dimensional transient multiphysics model for 40.68 MHz RF heating of chicken. They established a DT model of the experimental system and process through coupled electromagnetic and heat transfer modeling, and successfully reproduced and predicted heating efficiency and uniformity under different spatial configurations. Furthermore, Cabeza‐Gil et al. (2023) used a neural network model trained on over 400,000 simulated data points to develop a noninvasive DT, and it achieved real‐time accurate estimation of cooking processes with mean absolute percentage errors below 5%.

The reliability of DT models should be established through a multilevel validation system. At the laboratory level, model predictions must be compared with multipoint thermocouple measurements to validate the accuracy of the multiphysics coupling model. For instance, Goñi et al. (2022) validated the predictive accuracy of their multiphysics model by comparing simulation results with experimental measurements. At the pilot‐scale validation level, models must be validated under near‐industrial‐scale conditions. For example, Jiang Yang, et al. (2023) validated the effectiveness of their RFT numerical model for irregularly shaped aquatic products through comparison with experimental data. At the online validation level, DT models must undergo continuous calibration with real‐time sensing data during actual thawing processes, validating the correlation between predicted values and actual quality indicators. For example, Kannapinn et al. (2026) developed a data‐driven reduced‐order model (ROM) DT model for chicken breast thermal processing, achieving faster‐than‐real‐time prediction capability. By resynchronizing model predictions to measured food states at each control interval, they achieved continuous correction of model‐real discrepancies with a relative time series error of only 0.18%–0.49%. This closed‐loop mechanism of “prediction → measurement → correction” essentially represents the integrated implementation of online validation and control.

Food heterogeneity poses a core challenge for DT models, manifesting as compositional differences such as dielectric property variations between fat and muscle, geometric shape variations, and initial condition differences such as prior freezing rate and frozen storage duration. To address this issue, DT models can adopt a multiregion coupled modeling strategy. For instance, Goñi and Purlis (2010) developed a method for acquiring geometric models of different subregions of food through color segmentation, providing a geometric basis for multiregion modeling. By inferring internal parameter distributions from measured surface temperature data of different subregions, real‐time compensation for heterogeneity can be achieved. In numerical simulation studies of RF heating, Jiang, Yang, et al. ⁽²⁰²³) have preliminarily validated the feasibility of region‐based modeling strategies. They assigned independent thermophysical parameters to different parts of irregularly shaped aquatic products, and found it effectively improved the accuracy of temperature field prediction.

5.4. Compatibility Analysis of Intelligent Thawing Systems With Industry 5.0

The multidimensional sensing system enhances the perception capabilities of the intelligent thawing system. In terms of human‐centric perspective, the multidimensional sensing network transforms “invisible” quality attributes, such as temperature field distribution, water migration status, protein conformational changes and microbial activity, into visualizable data (Chen, Xie, et al. ²⁰²³; Cheng et al. ²⁰²²; Bulusu and Dhekne ²⁰²⁵). This transformation itself embodies the extension of human capability: operators no longer rely on guesswork or tactile judgment but make decisions based on data (Zade et al. 2025). In terms of sustainability, the value of sensing network lies in its nondestructive, real‐time, waste‐reducing characteristic. Nondestructive techniques such as Raman spectroscopy and HSI can acquire quality information without thawing or dissecting samples, thereby preventing large‐scale food waste caused by quality deterioration (Ouyang et al. 2022; Chen, Xie, et al. ²⁰²³). In terms of resilience, the multimodal fusion architecture of sensing network equips the system to cope with individual sensor failures, as alternative sensing modalities can provide redundant information to maintain continuous awareness of material status (Pereira et al. 2017).

The intelligent decision‐making and control module constitutes the cognitive core of the intelligent thawing system. From a human‐centric perspective, the value of intelligent decision‐making lies in lowering operational barriers and enhancing operational safety. AI‐based quality classification and prediction models enable operators to access expert‐level decision recommendations (Tang et al. 2025; Turgut et al. ²⁰²⁵). Furthermore, the application of XAI renders model decisions transparent, allowing operators to gain genuine cognitive empowerment rather than being dominated by a “black box” (Hashem et al. 2025). In terms of sustainability, intelligent decision‐making reduces energy waste and product loss through refined control. For instance, dynamic power/frequency regulation in solid‐state microwave systems enables precise energy matching to material status (Yang and Chen 2022); COMSOL‐based simulation optimization avoids quality losses caused by excessive heating (Zhu et al. 2025). In terms of resilience, the multitask learning architecture of AI models enables simultaneous adaptation to thawing requirements across different materials and freezing histories (You et al. 2025). When a batch deviates from quality expectations, the AI model can instantly adjust thawing strategies to prevent whole‐batch rejection (Yang and Chen 2022).

DT represent the subsystem with the strongest compatibility with Industry 5.0 within intelligent thawing systems. From a human‐centric perspective, first, DT enables engineers and operators to simulate hazardous scenarios such as equipment failures and abnormal operating conditions in a virtual environment, avoiding high‐risk experiments on physical equipment and thereby reducing personnel safety risks (Wang et al. ^2025c; Zafar et al. ²⁰²⁴). Second, DT‐based virtual operation training allows operators to master equipment operation and exception handling skills before engaging in actual production, thereby substantially shortening learning time. Third, DT visualizes complex multiphysics processes, enabling operators to “see” the internal state of equipment and make more informed decisions (Xu et al. 2023). In terms of sustainability, the value of DT lies in reducing resource consumption. Traditional process optimization relies on repeated physical experiments, consuming substantial materials, energy, and time. DT substitutes some physical experiments with virtual trial‐and‐error, significantly shortening research and development cycles, reducing costs, and minimizing material waste (Kannapinn et al. 2026; Cabeza‐Gil et al. ²⁰²³). In terms of resilience, DT systems continuously calibrate deviations between models and actual conditions through a “prediction → measurement → correction” mechanism, thereby enabling adaptive responses to raw material variability, equipment drift, and environmental disturbances (Kannapinn et al. 2026). When abnormal supply chain disruptions occur, DT can rapidly simulate quality outcomes under different thawing strategies, providing operators with decision‐making support to avoid whole‐batch losses (Zafar et al. 2024; İnanlar and Altay ²⁰²⁶).

In summary, the three subsystems of intelligent thawing systems support the human‐centric, sustainability, and resilience goals of Industry 5.0 through “extending perception,” “enhancing cognition,” and “previewing the future,” respectively. Unlike the automation logic of Industry 4.0, intelligent thawing systems under the Industry 5.0 framework are designed to augment rather than replace human capabilities, liberating people from repetitive, experience‐based operations and transitioning them toward higher level tasks requiring judgment, creativity, and decision‐making (Rahman, Biswas, et al. ²⁰²⁴; Zafar et al. ²⁰²⁴).

6. Industry 5.0 Enabling Technologies for Thawing Systems

Building upon the intelligent thawing architecture described in Section 5, this section focuses on the enabling technologies that translate digital intelligence into the three core values of Industry 5.0: human‐centricity, sustainability, and resilience. Beyond introducing emerging enabling technologies, this section critically discusses how they empower operators, strengthen system resilience, and improve sustainability within practical food thawing scenarios, thereby bridging intelligent thawing systems and the broader Industry 5.0 vision.

6.1. Human‐Centric Enabling Technologies

6.1.1. Augmented Reality

In the intelligent thawing unit of Industry 5.0, AR is an interface connecting digital information with the physical world and empowering frontline workers. In Figure 5, AR digitally extends operators senses by overlaying computer‐generated information onto their real‐world view (Fritz et al. 2023; Zhang et al. ^2023h).

FIGURE 5.

Key components of human‐centric novel thawing units, including augmented reality (Christensen and Engell‐Nørregård ²⁰¹⁶; Jagtap et al. ²⁰²¹), collaborative robots (Cobot; Romanov et al. ²⁰²²), low‐code development platforms (LCDP; Michael and Wortmann ²⁰²¹).

First, AR provides real‐time instructions for food thawing. An operator wearing smart glasses or with a handheld device can view virtual procedures, equipment settings, and step‐by‐step instructions overlaid directly onto actual equipment, lowering error rates during complex operations (Beck et al. 2016; Rejeb et al. ²⁰²¹).

Second, AR goes beyond geographical boundaries and enables remote expert support. When on‐site operators encounter problems, they can share their real‐time view with remote experts via AR devices. Experts can then annotate, sketch diagrams, or overlay guidance directly in the operator's vision, guiding them through the troubleshooting as if the experts were present (Lindell 2018; Rejeb et al. ²⁰²¹). This immediate collaboration may dramatically reduce thawing equipment downtime, cut expert travel costs, and enhance supply chain resilience.

Third, AR transforms multidimensional data from sensing networks into intuitive visual information to allow operators to “see” invisible states (Crofton et al. 2019; Tizhe Liberty et al. ²⁰²⁴). For example, temperature distribution data from IF thermography can be projected as heat maps directly onto meat surfaces using AR, so operators can quickly identify unfrozen “cold spots” or overheated “hot spots” (Fritz et al. 2023). This visualization aids in precise thawing endpoint determination and provides decision support for subsequent processing operations.

Last but not least, AR enhances traceability and transparency in food processing. Protogeros et al. (2025) developed two AR applications, one projecting nutritional information and videos onto milk cartons, and another providing detailed biological product information through package label scanning. In thawing units, such applications can display real‐time information to operators or quality inspectors, including raw material frozen storage history and quality predictions.

AR is now deployed on actual production lines in the food industry. For instance, Maple Leaf Foods, a Canadian meat processor, implemented an AR training system at its Guelph facility. Integrated with DT of the equipment, this system allows operators to simulate maintenance procedures in a virtual environment before working on physical machinery. The facility also deployed a real‐time monitoring application that automatically identifies causes of downtime and generates AR‐guided troubleshooting workflows (Rockwell Automation ²⁰²⁵). An international pasta manufacturer adopted Glartek's AR platform for digital on‐the‐job training, achieving a 20% reduction in error rates and significantly faster onboarding for new employees (Glartek 2025).

In thawing scenarios, the core value of AR lies in overlaying “invisible” data onto the operator's field of view in the form of heat maps or dynamic models. From the perspective of small and medium enterprise adaptability, hardware costs (smart glasses typically range from $1500 to $5000) and content development costs remain the primary barriers, though tablet‐based AR solutions offer a more economical alternative path (Glartek 2025).

6.1.2. Collaborative Robots

In the human‐centric intelligent thawing production unit envisioned by Industry 5.0, Cobots are key to bridging human intelligence and machine precision. Unlike Industry 4.0 which replaces humans with machines for full automation, Industry 5.0 emphasizes complementary collaboration between humans and robots (André‐Zarna et al. ²⁰²⁶; Rahman, Khatun, et al. ²⁰²⁴).

Cobots enable safe and efficient cooperation with humans in shared workspaces. Zafar et al. (2024) outlined the evolution from “caged robots” to “human–robot teaming (HRT),” marking the transition of modern robots from passive tools to proactive team members. For example, Ma et al. (2026) proposed an AR‐based human–robot collaboration (HRC) assembly system that enables Cobots to understand operator intent by recognizing their posture and actions in real time. Simultaneously, the operators can use the AR interface to see the states of Cobots in real time. Ensuring safety in HRC is a prerequisite for the widespread application of Cobots in the food industry. International standards such as ISO/TS 15066 define safety requirements for Cobots, including safety‐rated monitored stop, hand guiding, speed/separation monitoring, and power and force limiting, providing a solid safety base for HRC in thawing plants (Rahman, Khatun, et al. ²⁰²⁴).

The use of Cobots in the food industry has been proven effective on multiple occasions. In cleaning applications, the use of FANUC CRX‐10iA/L Cobots has enabled the automated cleaning of large areas in food processing plants, reducing cleaning time by 30% (Automation Magazine ²⁰²⁵). In meat processing applications, Liu et al. (2022) reported a robotic system for pork belly cutting, generating 3D point clouds of suspended pigs via laser sensors and planning cutting trajectories using an adaptive genetic algorithm, achieving a 90% success rate.

In thawing plants, Cobots can undertake heavy physical tasks such as automated loading and unloading of frozen raw materials and their transport between processes. This not only reduces operators’ physical strain and lowers the risk of musculoskeletal disorders, but also enables operators to focus on high‐value‐added tasks such as monitoring the thawing process and handling abnormal batches (Aly et al. 2023; Rahman, Khatun, et al. ²⁰²⁴). Moreover, Cobots integrated with advanced sensing technologies achieve higher precision and more stable execution, thereby reducing losses and enhancing product value (Liu et al. 2022). This labor division elevates operators from “physical executors” to “system supervisors” and “anomaly decision‐makers,” which is a key embodiment of the Industry 5.0 human‐centric philosophy.

6.1.3. Low‐Code Development Platforms

If AR allows operators to “see more clearly” and Cobots enable them to “work with less physical strain,” then low‐code development platforms (LCDP) empower them to evolve from passive technology users into active technology creators (Figure 5).

An LCDP is a development environment that enables application creation through visual drag‐and‐drop, graphical logic orchestration, and prebuilt modules, allowing nonprofessional programmers to independently build and deploy software (Sanchis et al. 2020). LCDPs bridge the gap between operational requirements and IT resources and allow domain experts to quickly build applications for specific business needs (Shi, Dong, et al. ²⁰²⁵). In thawing plants, frontline operators can independently build data collection tools, design quality tracking dashboards, and configure anomaly alert rules. When production units no longer rely on external technical support, they transform from rigid execution nodes into flexible systems capable of responsing quickly to change.

The integration of LCDPs with AI elevates the operator's “designer” role to new heights. Shi, Dong, et al. (2025) noted that future LCDPs would increasingly embed AI/ML modules to automate complex workflows. Chen et al. (2025) proposed a multiagent collaborative framework for automated development of industrial robot control software, where multiple large language model (LLM) agents play different roles to collaboratively complete software development, enabling operators to create digital tools more efficiently and intelligently. Such systems harness machine computational advantages while retaining human judgment capabilities, perfectly embodying the human‐centric philosophy of Industry 5.0.

In industrial settings, vast amounts of valuable operational experience reside as “tacit knowledge” in the minds of skilled workers, making it difficult to transfer and reuse. LCDPs provide a mechanism to externalize and digitize this tacit knowledge. As İnanlar and Altay (2026) conceptualize with “cognitive twin ecosystems,” humans are no longer merely a link on the assembly line but the creative subjects defining production methods. For example, Redchuk et al. (2023) demonstrated how process engineers and operators at a North American food ingredient company participated in configuring ML models via an LCDP, translating their deep understanding of boiler thermal efficiency into executable optimization algorithms. This process not only improved thermal efficiency by 2.5% but, more importantly, codified operator experience into reusable digital assets, achieving knowledge transfer from individuals to the organization. In thawing scenarios, by allowing operators to build quality dashboards, configure alert rules, and create maintenance logs without programming expertise, LCDP directly translates the human‐centric vision of Industry 5.0 into practice, turning frontline workers from passive executors into active designers.

6.2. Building Resilient and Sustainable Thawing Supply Chains

The freezing–thawing unit is the critical link between raw material production and consumer markets. By enabling full‐chain data transparency, dynamic resource scheduling, and closed‐loop feedback optimization, a future thawing supply chain that is both resilient and sustainable can be constructed.

6.2.1. Blockchain

Globalization of food supply chains presents challenges for product traceability. Traditional information systems struggle with the complexity of multiactor participation and cross‐border transportation, leading to frequent food fraud and declining consumer trust (Arvana et al. 2023; Bosona and Gebresenbet ²⁰²³). Blockchain technology, with its decentralized, immutable, transparent, and traceable characteristics, can be used to build trustworthy food supply chains. Technically, Hyperledger Fabric‐based blockchains can achieve transaction throughput exceeding 200 TPS, meeting industrial traceability demands (Mohammed et al. 2023; Pavithra et al. ²⁰²⁵).

Data manipulation, privacy, and confidentiality are key considerations. By adopting a blockchain‐based multisignature supply chain governance architecture and equipping each member with a cryptographic identification hash, data immutability and accountability are ensured, and trade secrets are protected (Cao et al. 2022). Game‐theoretic analysis by Sun, Song, et al. (2023) further reveals that consumer traceability preferences directly influence a company's decision to use blockchain. When the proportion of consumers with high traceability preference reaches a specific threshold, adopting blockchain can become a competitive advantage.

The practical effectiveness of blockchain in food supply chain traceability is supported by quantitative data. After integrating full‐process data from over 200 multinational enterprises, the IBM Food Trust platform has improved food traceability efficiency by over 90% and reduced quality risk control costs by 65% (Liu 2025). Iftekhar and Cui (2021) designed a Hyperledger Fabric‐based blockchain system for imported frozen meat during COVID‐19, storing critical data such as worker health records, disinfection operations, and temperature monitoring on the blockchain, enabling traceable risk control for cold chain products.

In a food thawing unit, key data including pretreatment parameters, thawing parameters, temperature curves, and predictive quality indicators, form a complete evidence chain of thawed product quality evolution. Integrating this data with upstream information (farming/catching details, frozen storage history, cold chain records) and downstream logistics and sales data creates a traceability system from farm to fork (Cao et al. 2022; Conter ²⁰²⁴). However, the cost of deploying blockchain technology remains a practical barrier for small and medium‐sized enterprises. Furthermore, differences in data formats and consensus mechanisms across various blockchain platforms limit the mutual recognition of information across different blockchain platforms (Arvana et al. 2023; Conter ²⁰²⁴).

6.2.2. Edge–Cloud Collaboration

Real‐time capability is essential for food supply chain resilience, as thawed products may have a shelf life of only a few days, and any decision delay leads to waste. The edge–cloud collaborative architecture provides the technological basis for this. On the cloud side, data from various nodes are aggregated and analyzed, integrated with order, inventory, and logistics information to achieve global optimization. On the edge side, computing resources deployed close to data sources enable millisecond‐level responses, avoiding the bandwidth overhead and high latency of centralized cloud computing (Pérez‐Pons et al. ²⁰²¹; Zafar et al. ²⁰²⁴).

The performance of the edge–cloud collaborative architecture in food processing is supported by quantitative data. Liu, Zhou, et al. (2025) designed an edge–cloud collaborative IoT architecture for meat processing, achieving real‐time pork freshness detection with peak memory usage below 600 MB, average CPU usage under 20%, and gateway response times within 100 ms. Additionally, the Edge–IoT platform deployed by Alonso et al. (2020) in a dairy farming scenario preprocessed sensor data at the edge, reducing data transmitted to the cloud by 46.72% while improving communication reliability.

In a food thawing unit, the edge–cloud collaborative architecture has unique value: whenever a food is thawed, portable devices allow fast quality grading and shelf‐life prediction; if a batch is detected with significantly shortened shelf life, the edge system triggers alerts and the cloud reassigns distribution priorities accordingly (Arya et al. 2025; Guo et al. ²⁰²⁶). Furthermore, edge–cloud collaboration enables a continuously evolving closed‐loop optimization mechanism: the cloud periodically retrains prediction models based on aggregated data uploaded from the edge, pushing optimized lightweight models back to edge devices, forming a complete “collection, training, optimization, deployment” cycle (Guo et al. 2026). When post‐thawing quality data for a batch consistently deviate from targets, the cloud can trace and analyze its upstream freezing conditions, identify key influencing factors, and automatically generate recommendations for optimizing the upstream process. This collaborative architecture makes the thawing unit a dual engine for rapid response and global optimization within the supply chain.

6.2.3. Data‐Driven Closed‐Loop Optimization

Resilience in Industry 5.0 is not only about responding to sudden disruptions but also about a system's capacity for continuous self‐improvement. Feeding thawed product quality data back upstream to optimize freezing processes, forming a closed‐loop feedback optimization, is the key mechanism for achieving this evolution (Guo et al. 2026).

The value of closed‐loop optimization in food processing is well‐documented. Kusherbaeva and Zhou (2023) proposed a user‐feedback‐based multiobjective production optimization system, achieving dynamic balance between business and operational goals. Wang et al. (2026) integrated real‐time sensing with AI prediction to build an AI‐driven fermentation optimization framework. By dynamically adjusting the glucose feeding rate through real‐time feedback control, enzyme yield increased by 46%.

In a food thawing unit, if a batch of material consistently exhibits high drip loss after thawing, the system can trace and analyze temperature fluctuation records during cold chain transportation to establish a correlation model linking thawing quality indicators and upstream process parameters (Guo et al. 2026; Yun, Kim, et al. ²⁰²⁵). For example, when thawing results reveal deficiencies in the upstream freezing process, the system can automatically suggest adjustments to freezing parameters, such as optimizing physical field settings or adding cryoprotectants. This “thawing results to freezing process” reverse optimization elevates the system from “post hoc remediation” to “preventive action,” which is a concrete embodiment of the adaptive system proposed by Industry 5.0 in food thawing.

7. Challenges and Perspectives

7.1. Current Challenges

Although advanced thawing technologies are promising, there are several challenges that must be addressed before widespread adoption.

First, the complexity and reliability of multiphysics coupling equipment pose major technical hurdles. While synergistic systems integrating microwave, RF, ultrasound, and EF may be beneficial at the laboratory scale, their industrial implementation is hampered by low equipment integration, complex control logic, and insufficient long‐term operational stability (Jiang, Zhang, et al. ²⁰²³).

Second, the lack of standardization hinders intelligent thawing integration. The industrial IoT landscape features multiple competing communication protocols (MODBUS, OPC UA, MQTT), making data barriers that severely limit system integration efficiency (Arya et al. 2025; İnanlar and Altay ²⁰²⁶; Liu, Zhou, et al. 2025). Additionally, blockchain applications in food traceability face similar standardization challenges, as different data formats and consensus mechanisms across platforms limit cross‐chain information interoperability (Arvana et al. 2023; Conter ²⁰²⁴).

Third, the substantial cost of deploying highly integrated thawing systems may be a major barrier to the adoption of advanced technologies. In the protein‐based food processing industry, there exists a significant gap in technological absorption capacity between the organized sector (large enterprises) and the unorganized sector (small‐ and medium‐sized enterprises). The unorganized sector commonly faces challenges including limited technical knowledge, constrained financing channels, and inadequate brand and marketing capabilities (Singh 2023). Advanced thawing equipment such as RFT and DBD‐PT cost several to dozens of times more than conventional equipment, and the construction and validation of high‐fidelity DT entail considerable expense and difficulty. All of these constitute insurmountable barriers for small and medium‐sized enterprises with tight cash flows (Balthazar et al. 2024; Kannapinn et al. ²⁰²⁶). This implies that even when these technologies itself matures, its benefits may remain confined to large enterprises for an extended period, while the small and medium‐sized enterprises that constitute the majority of the industry will continue to rely on conventional methods, thereby widening rather than narrowing the technological gap within the industry.

Fourth, challenges remain in the integration of food safety management systems. During thawing, dormant microorganisms gradually regain activity, and thawing exudate which is rich in nutrients provides an ideal medium for microbial growth (Çalışkan Koç et al. 2025). Rapid thawing technologies (RF, MT, OT) can shorten the time spent passing through the temperature danger zone, but localized overheating may create niches for heat‐resistant microorganisms (Jiang et al. 2025). DBD‐PT can achieve complete surface sterilization (0 CFU/g), but it acts only on the surface, leaving internal microorganisms potentially viable (Atani et al. ^2025a). Active thawing media such as PAW and SAEW can exert bactericidal effects during the thawing process, but most studies report reductions in total viable counts rather than systematic evaluations against specific foodborne pathogens (Liao et al. 2020). More critically, there is currently no mature framework for integrating intelligent thawing systems into existing HACCP plans and risk‐based preventive control systems. Regulatory barriers and insufficient long‐term safety validation constrain the industrialization of emerging technologies. For food companies to deploy these new technologies, they must reassess critical control points, establish new monitoring procedures, and obtain regulatory recognition (Sartoni et al. 2025).

Fifth, food fraud risks may intensify with increasing digitalization. Digital tools such as blockchain, with their tamper‐proof characteristics, hold promise for enhancing supply chain transparency and reducing traditional food fraud (Arvana et al. 2023; Bosona and Gebresenbet ²⁰²³). However, blockchain's tamper‐proof quality does not equate to fraud prevention. It can only ensure that data already on the chain cannot be altered, but it cannot guarantee the authenticity of data before it enters the chain. The risk of data tampering in digitally enabled supply chains primarily manifests in the preblockchain stage, where data may be compromised by human input errors (such as falsified temperature records or forged origin information), sensor calibration deviations, or information omission at intermediate stages. Once erroneous or falsified data are written to the blockchain, they become immutable permanent records, paradoxically making traceability more difficult (Brooks et al. 2021). Therefore, as digitalization advances, it is necessary to simultaneously establish data integrity safeguards and fraud detection mechanisms.

Moreover, workforce skill transformation and consumer perception of high‐technology food products are limited. The human‐centric concept of Industry 5.0 requires technology to empower people, but we lack sufficient interdisciplinary talents skilled in both food engineering and advanced digital technologies (Qiao et al. 2024). Furthermore, consumer acceptance and trust in foods processed by physical fields such as EF, MF, and RF remain insufficient (Wang, Li, et al. ²⁰²¹).

7.2. Future Prospects

Future work may focus on safe, efficient, and recyclable smart functional media. For MNPs, systematic evaluation of the food safety of materials such as Fe₃O₄ is needed, along with the development of controlled recovery technologies to address dispersion stability issues (Jiang et al. 2025). For dielectric matching media, exploring smart materials such as temperature‐sensitive hydrogels could improve thawing uniformity (Li, Zhu, et al. ²⁰²¹). For biochemically active medium, it requires more study to understand the regulatory mechanisms of PAW, SBEW, and SAEW to establish quantitative relationships between medium addition and the bactericidal/antioxidant efficacy (Liao et al. 2020; Wang, Ding, et al. ^2024c).

Second, it is preferable to develop lightweight, modular DT model libraries for multiple thawing technologies. Open‐source sharing of such model libraries would dramatically lower the barriers for small and medium‐sized enterprises and promote the popularity of DT in the thawing field (Kannapinn et al. 2026).

In terms of sustainability, future efforts should conduct full supply chain life cycle assessments covering “farming/catching, freezing pretreatment, cold chain transportation, thawing, and consumption,” to quantify the carbon footprint, water footprint, energy consumption, and food waste rates of different thawing technology pathways (Zhang et al. ^2023a).

Furthermore, enabling technologies based on AR, Cobots, and LCDP should be actively explored in the food thawing industry to follow the human‐centric conception of Industry 5.0 (İnanlar and Altay 2026; Kheirabadi et al. ²⁰²⁵; Redchuk et al. ²⁰²³). Additionally, traceability systems facing consumers should be established, allowing consumers to access full supply chain information via QR code scanning, thereby enhancing trust in high‐technology products (İnanlar and Altay 2026; Kheirabadi et al. ²⁰²⁵; Redchuk et al. ²⁰²³).

8. Conclusion

This review systematically outlines next‐generation thawing systems aligned with Industry 5.0, encompassing freezing pretreatment, thawing technologies, and intelligent integration. During freezing, physical field‐assisted, medium‐assisted, and chemical/physical pretreatments precisely regulate ice crystal nucleation and growth, establishing a foundation for high‐quality thawing. For thawing, while physical field‐driven technologies present distinct advantages and limitations, medium‐enabled approaches and multitechnology synergy can overcome single‐technology bottlenecks. Intelligent thawing systems within the Industry 5.0 framework represent an integration of multidimensional sensing, intelligent decision‐making, and digital twins. In human‐technology interaction, AR allows operators to visualize quality states, Cobot relieve workers from strenuous physical labor, and low‐code platforms enable frontline workers to participate in tool creation. Regarding resilience and sustainability, blockchain offers a promising approach to establishing trusted traceability records, edge–cloud collaboration provides a technical foundation for real‐time response and data‐driven optimization, and closed‐loop feedback presents a pathway toward continuous improvement. Currently, industrial deployment of these advanced thawing technologies still faces challenges, including equipment complexity, standardization gaps, high costs, and talent shortages. Future breakthroughs are needed in functional medium development, lightweight digital twin model libraries, and human‐machine collaborative interface design.

Author Contributions

Fangye Zeng: conceptualization, methodology, software, data curation, Writing – original draft, investigation. Min Zhang: writing – review and editing, funding acquisition, supervision, project administration. Chung Lim Law: writing – review and editing. Luming Rui: writing – review and editing, validation, formal analysis, visualization, resources.

Conflicts of Interest

The authors declare no conflicts of interest.