Food Engineering Reviews最新文献

Food processing, a longstanding practice, employs a range of technologies to regulate microbial populations and ensure consistent quality and safety of products, instilling consumer trust in the food industry. As contemporary demands and safety standards evolve, a pressing need arises for less processed, nutrient-rich food items that adhere to more stringent microbiological criteria, promoting innovative and sustainable processing methods. Among these, hydrodynamic cavitation is presented as a promising technology due to its energy efficiency, low thermal impact, and ability to significantly reduce microbial loads without compromising nutritional value. Recent studies have explored hydrodynamic cavitation application in liquid food sterilization, beverage preservation, and water decontamination, demonstrating notable reductions in both spoilage and microorganisms. However, the exact mechanisms underlying this microbial inactivation ability of hydrodynamic cavitation remain partially understood, posing a challenge to process optimization and broader industrial adoption. This review critically examines the current understanding of hydrodynamic cavitation antimicrobial action, explores key design and operational parameters, and identifies knowledge gaps. Future research directions are proposed to enhance treatment efficacy and to support the integration of hydrodynamic cavitation into scalable, sustainable food processing workflows.

Cheese-making is a complex process involving numerous stages, with multiple factors contributing and complex interactions occurring among the physicochemical elements involved. Understanding the process and optimizing its stages has attracted the attention of numerous investigations. In recent years, Machine Learning (ML) has established itself as one of the most advanced tools for data analysis and modeling thanks to its ability to capture complex and non-linear patterns. In the area of food science and engineering, these algorithms have started to be used as an alternative to more traditional statistical and mathematical prediction models. This paper explores the main research on ML applied to the study of cheese, from its production stages (i.e., fermentation or coagulation process) to the final product (i.e., detection of adulterations or food fraud). Particularly, we review 42 papers published between January 2014 and January 2025, with the aim of identifying common approaches. First, we present an explanation of the main concepts required to bring these approaches closer to researchers who are not experienced in applying ML. Then, we analyze the selected publications to detail the tasks of interest and the algorithms proposed to solve them. Finally, we detect gaps and opportunities to incorporate ML into future cheese research.

The proliferation of research on Artificial Intelligence (AI) in food science and engineering has made it increasingly difficult to synthesise relevant insights effectively. Although AI adoption in the food industry has grown, it lags behind sectors like finance and healthcare due to the complexity of food systems, including high process variability, risk aversion towards novel technologies, and constrained investment appetite. Historically, computational techniques and AI-adjacent technologies like expert systems and empirical modelling have supported food research and development for decades. More recently, AI applications have broadened to include process control, food safety, ingredient and product quality, sensory evaluation, traceability, and supply chain management. In response to the rapid increase in AI-related food science publications – particularly since 2019 – this review introduces tools for dynamically synthesising and exploring this evolving knowledge base. We present an interactive dashboard that integrates a curated dataset of food AI review articles with advanced bibliometric analyses, enabling user-driven exploration of research trends and thematic relationships. Additionally, we demonstrate the use of customised large language model (LLM) tools for targeted literature interrogation, enhancing accessibility for researchers and industry stakeholders. Complementing this academic synthesis, we profile selected industry case studies where AI plays a central role in ingredient discovery, product development, intelligent sorting, and sensory analytics. By combining interactive research tools with real-world case studies, this review offers a comprehensive snapshot of Food AI and begins to bridge the gap between academic research and industry implementation, providing a valuable resource for those seeking both domain-specific knowledge and actionable insights.

Pulsed Electric Field (PEF) is an emerging non-thermal food processing technology utilizing electroporation to engineer food matrices for microbial inactivation, bioactive compounds extraction, and modification of the food structure while maintaining nutritional and sensory qualities. The purpose of this review article is to present important mechanisms, characteristics, design considerations, enhance energy efficiency, and process scalability for optimal PEF operation with regard to three significant influencing parameters i.e., electric field strength, pulse length, and material conductivity. Discussions on applications in juice extraction, protein recovery, and microbial control highlight scaling issues, economic feasibility, and promising treatment uniformity across various food matrices. Future perspectives focus on the potential of PEF in sustainable food processing, development of functional foods, and how PEF interacts with innovative techniques like AI-driven optimization and hybrid processing. This review provides a perspective on the application of PEF technology for sustainable and eco-efficient food systems that can meet consumer requirements on nutrient retention and minimal processing.

The implementation of innovative volumetric food preservation technologies has the potential to reduce the overprocessing of products, by intensifying the preservation effects of treatments and reducing their exposure to them. However, empirical data are insufficient for engineers and food technologists to optimize and develop safe processing protocols. It is therefore essential to develop digital models that can provide a comprehensive representation of the system, as well as a foundation for the computer-assisted optimization of novel technologies. However, a gap in the literature hinders the acquisition of the insights necessary to accomplish this task. This review paper provides an overview of conventional and innovative preservation technologies, outlining their fundamental principles and operational mechanisms. It then presents a comprehensive examination of the numerical methodologies employed for the digital simulation of these technologies, delineating their distinctive requirements. Furthermore, the review assesses potential strategies for ensuring the reliability of data validation and techniques for reducing the complexity of numerical modelling with artificial intelligence (AI). This literature review identified the requisite knowledge for the implementation of numerical simulations and important details that need to be considered to avoid the divergence of the calculations and save costly computational hours. Furthermore, the review proposed a time–temperature integrator-based validation for the obtained data and evaluated the benefits of supporting numerical simulations with AI.

Freeze drying (FD) is a leading method for preserving food quality, particularly for heat-sensitive products; however, significant operational costs and slow throughput constrain its use. These drawbacks, along with growing environmental awareness and the need for sustainability, are putting pressure on the industry to enhance energy efficiency and the overall sustainability of FD practices. This review outlines the latest advances in improving the energy efficiency of freezing and freezing and FD processes, thereby reducing processing time and energy consumption. These improvements involve a combination of innovative technologies such as radiofrequency (RF), high-pressure (HP), magnetic field (MF), high-voltage electric field (HVEF), microwave (MW), infrared radiation (IR), ultrasound (US), and instant controlled pressure drop (DIC). In particular, hybrid systems that integrate these technologies with FD have shown synergistic benefits in enhancing drying kinetics and reducing processing costs. This review also discusses the influence of alternative pretreatments on the efficiency of FD and the quality of the resulting dry products. Combining advanced technologies with freezing and FD processes significantly increased mass transfer rates, shortened processing times, reduced energy consumption, and produced superior-quality foods. Recent trends also include the application of artificial intelligence (AI) and machine learning to model, monitor, and optimise FD processes, enabling data-driven decision-making and improved process control. These advances have the potential to revolutionise the food industry by allowing greater control over ice crystal nucleation rates and sizes, reducing production costs, and improving the overall quality of final products. In addition, novel pretreatments significantly improve mass and heat transfer, shorten processing time, minimise nutrient losses and improve the overall quality of the end products. These innovations have important implications for scaling FD in industrial applications and advancing sustainable food processing systems.

High-Pressure Thermal Processing (HPTP) is an emerging food preservation technology that combines elevated pressure with moderate to high temperatures to achieve microbial inactivation while preserving product quality. This review presents a comprehensive overview of the scientific principles, technological developments, and potential commercial applications of HPTP. Key mechanisms such as adiabatic compression heating and the synergistic effects of pressure and temperature are explored alongside advances in equipment design, predictive modeling, and process optimization. The manuscript also highlights applications across diverse food categories, including juices, dairy, meats, seafood, and ready-to-eat meals, and emphasizes HPTP’s ability to reduce the formation of heat-induced food processing contaminants. Recent innovations, such as multilayer canister systems enabling HPTP in conventional HPP equipment, are discussed in the context of scaling the technology from research to industrial use. As consumer demand for minimally processed, high-quality foods continue to rise, HPTP stands poised to play a transformative role in the future of food processing.

The amount of products, especially food, wasted worldwide continues to rise due to poor storage. Freezing approaches have been extended to food-related research to extend the shelf life and preserve the physicochemical properties of food. However, the process involves unavoidable ice crystal formation, growth, and recrystallization, resulting in cryodamage of the stored food. Mitigating the ice-damaging effects often requires the addition of green cryoprotective agents (GCAs) in the preserving medium to reduce and/or prevent ice formation. Certain non-toxic chemicals and materials have been reported to possess anti-icing properties and thus serve as promising GCAs in food and food-related applications. This review presents the development timelines, challenges, limitations, optimization, mechanism, elementary parameters to be considered, and most importantly, toxicity concerns regarding novel GCA materials and molecules in the food industry. Possible future research in the area of food and food industries was summarized.

Freezing can reduce the storage temperature of food and inhibit the growth of microorganisms, thereby extending the shelf life of food. Ice crystal formation during freezing causes tissue damage and nutrient loss, impacting food quality. The number and size of ice crystals formed during freezing or freezing are closely related to water migration. Therefore, understanding the migration of water during freezing is crucial. This paper reviews the water distribution at the cellular level and discusses the mechanism of water migration caused by freezing from temperature gradient, ice crystal, and food composition. The size and quantity of ice crystals, freezing rate and time, freeze–thaw process and other factors affecting water migration are introduced in detail. Finally, the freezing methods and new freezing technologies affecting water migration are introduced, as well as their specific applications in food. These applications all involve controlling the migration of water to improve the quality of frozen products. It has been found that emerging freezing technologies such as ultrasonic assisted freezing, magnetic field assisted freezing, electric field assisted freezing, high pressure assisted freezing and the use of cryoprotectants can control the migration of water. It may be possible to improve the quality of frozen food by combining the freezing methods with the new freezing technologies, and provide a reference for the practice and research of the food industry.

Radio frequency (RF) heating has emerged as a key innovation in food processing operations such as drying, pasteurization, and thawing due to its ability to deliver rapid and volumetric heating. However, the inherent heterogeneity of food matrices and their complex interactions with electromagnetic fields often lead to uneven electric field distribution, resulting in heating inconsistencies and potential quality deterioration. Addressing these challenges requires strategies that enhance heating uniformity while preserving food quality. A promising solution is the integration of RF heating with complementary processing technologies. Hybrid techniques such as plasma treatment, cold shock, ultraviolet (UV) irradiation, ultrasound, infrared heating, and high hydrostatic pressure processing can improve heating efficiency and mitigate the limitations of RF heating. This review systematically examines the principles of RF heating and its integration with emerging technologies. It explores the mechanisms underlying heating non-uniformity, evaluates existing solutions, and identifies future research priorities. Special attention is given to the development of customized RF heating strategies tailored to the physicochemical properties of different food matrices. Furthermore, the integration of intelligent control systems, algorithmic optimization, and interdisciplinary advancements is expected to enhance the precision and efficiency of RF heating, offering innovative solutions for high-performance thermal processing while maintaining superior food quality.