Food Engineering Reviews最新文献

Stone tools were the oldest pressure-related food processing tools (approx. 3.3 million years ago) until the use of fire for thermal processing (approx. 0.5–0.3 million years ago) became the prime food processing aid. During the last 40 years, gentle, resource-efficient pressure-related technologies for partial replacement of thermal processes were developed and gained rapid dissemination and acceptance. This paper provides an overview of food processes where pressure is the key mode of action ranging from negative pressures (below 0.00001 MPa) to very high pressure (1400 MPa). Working principles, applications, advantages/limitations as well as needs and opportunities for these processes using dynamic or static pressures are presented. Based on the high number of existing and developing pressure-related unit operations, we propose a new pressure-based food processes classifications system organized in pressure ranges (max. 0.1, 1.0, 10, 100, 1000, > 1000 MPa) embracing the temperature range used in food processing.

Meat is a perishable food, and one of the most important and efficient preservation methods is frozen storage. However, traditional thawing methods can negatively impact the texture and water-holding capacity (WHC) of frozen food. To address these challenges, electric field (EF) assisted thawing, including pulsed electric field (PEF), low volt electrostatic field (LVEF), and high volt electrostatic field (HVEF), offers a promising alternative as a nonthermal preservation method for fresh food. EF treatment can speed up thawing process, inhibit growth of microorganisms, control enzyme activity, and help with energy transfer. EF-assisted thawing may also affect meat properties including thawing rate, physical (myofibril protein stability, rheological/textural, WHC, thawing loss, and color), and chemical properties (lipid and protein oxidation, fatty acid profile, and volatile profile), as well as microbiological aspects. Therefore, this review aims to provide more in-depth information on the effects of EF technology on meat, as a reference for potential application in meat-assisted thawing processes. The results show that the variations in fundamental operations, equipment design mechanisms, and operating conditions have different effects on food components.

The rapid detection of food contaminants is crucial for food safety and human health. Portable sensing platforms are widely used, especially in resource-limited areas. Intelligent mobile diagnostic platforms (IMDPs) integrate multifunctionality, computing power, and data transfer, offering portability, scalability, and cost-effectiveness. However, challenges in detection reliability remain. This review introduces IMDP characteristics, components, and design, covering signal detection, data analysis, visualization, and power management. It explores diverse types of IMDPs, including paper-based, chip-based, hydrogel-based, and wearable devices, and their applications in detecting food freshness, contaminants, and pathogens. The review highlights that integrating stretchable materials, lab-on-a-chip technology, and smart devices enhances detection accuracy, while advanced algorithms like machine learning improve reliability. Consequently, IMDPs provide cost-effective, portable, and rapid food safety monitoring.

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.