Towards a holistic approach for multi-objective optimization of food processes: A critical review

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

Nanofood Process Technology: Insights on How Sustainability Informs Process Design

Nanostructured products are an actively growing area for food research, but there is little information on the sustainability of processes used to make these products. In this Review, we advocate for selection of sustainable process technologies during initial stages of laboratory-scale developments of nanofoods. We show that selection is assisted by predictive sustainability assessment(s) based on conventional technologies, including exploratory ex ante and "anticipatory" life-cycle assessment. We demonstrate that sustainability assessments for conventional food process technologies can be leveraged to design nanofood process concepts and technologies. We critically review emerging nanostructured food products including encapsulated bioactive molecules and processes used to structure these foods at laboratory, pilot, and industrial scales. We apply a rational method via learning lessons from sustainability of unit operations in conventional food processing and critically apportioned lessons between emerging and conventional approaches. We conclude that this method provides a quantitative means to incorporate sustainability during process design for nanostructured foods. Findings will be of interest and benefit to a range of food researchers, engineers, and manufacturers of process equipment.

Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps

The current methods applied to material screening for adsorption-based heat pumps are based on a fixed set of temperatures or their independent variation, providing a limited, insufficient, and unpractical evaluation of different adsorbents. This work proposes a novel strategy for the simultaneous optimization and material screening in the design of adsorption heat pumps by implementing a meta-heuristic approach, particle swarm optimization (PSO). The proposed framework can effectively evaluate variable and broad operation temperature intervals to search for viable zones of operation for multiple adsorbents at once. The criteria for selecting the adequate material were the maximum performance and the minimum heat supply cost, which were considered the objective functions of the PSO algorithm. First, the performance was assessed individually, followed by a single-objective approximation of the multi-objective problem. Next, a multi-objective approach was also adopted. With the results generated during the optimization, it was possible to find which adsorbents and temperature sets were the most suitable according to the main objective of the operation. The Fisher-Snedecor test was applied to expand the results obtained during PSO application and a feasible operating region built around the optima, enabling the arrangement of close-to-optima data into practical design and control tools. This approach allowed for a fast and intuitive evaluation of multiple design and operation variables.

Computer-aided food engineering

Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems.

Fresh-Cut Vegetables Processing: Environmental Sustainability and Food Safety Issues in a Comprehensive Perspective

The fresh-cut industry supplies the food market with healthy fresh fruit and vegetables and, in that way, may contribute to improve the nutritional status of the general population. On the other hand, over the last few years increasing concerns have been raised regarding the environmental impact of the fresh-cut industry, human health risks from exposure to disinfection by-products found in fresh-cut products and chlorine-based disinfection treatments during produce processing. This review provides a comprehensive view of the main interlinked aspects related to food safety and environmental impact of processing of fresh-cut vegetables. Advantages and downsides of the mainstream disinfection strategy, based on the use of chlorine-related disinfecting agents, along with some alternative treatments close to a wide commercial application, are discussed. Limitation in the application of these strategies to processing of organic fresh-cut produce are also highlighted, examining the specific environmental and food safety problems in the organic sector. Areas where lack of available information hinders at present a clear understanding of priorities of research and action are pointed out. Innovative conceptual tools are proposed to address these multiple and interlinking issues and to overcome limitations of currently available technologies. A comprehensive and multidisciplinary approach is suggested to move toward a more safe and environmentally sustainable production of fresh-cut products.

Dynamics of Microbial Inactivation and Acrylamide Production in High-Temperature Heat Treatments

In food processes, optimizing processing parameters is crucial to ensure food safety, maximize food quality, and minimize the formation of potentially toxigenic compounds. This research focuses on the simultaneous impacts that severe heat treatments applied to food may have on the formation of harmful chemicals and on microbiological safety. The case studies analysed consider the appearance/synthesis of acrylamide after a sterilization heat treatment for two different foods: pureed potato and prune juice, using Geobacillus stearothermophilus as an indicator. It presents two contradictory situations: on the one hand, the application of a high-temperature treatment to a low acid food with G. stearothermophilus spores causes their inactivation, reaching food safety and stability from a microbiological point of view. On the other hand, high temperatures favour the appearance of acrylamide. In this way, the two objectives (microbiological safety and acrylamide production) are opposed. In this work, we analyse the effects of high-temperature thermal treatments (isothermal conditions between 120 and 135 °C) in food from two perspectives: microbiological safety/stability and acrylamide production. After analysing both objectives simultaneously, it is concluded that, contrary to what is expected, heat treatments at higher temperatures result in lower acrylamide production for the same level of microbial inactivation. This is due to the different dynamics and sensitivities of the processes at high temperatures. These results, as well as the presented methodology, can be a basis of analysis for decision makers to design heat treatments that ensure food safety while minimizing the amount of acrylamide (or other harmful substances) produced.

Finite Element Method for Freezing and Thawing Industrial Food Processes

Freezing is a well-established preservation method used to maintain the freshness of perishable food products during storage, transportation and retail distribution; however, food freezing is a complex process involving simultaneous heat and mass transfer and a progression of physical and chemical changes. This could affect the quality of the frozen product and increase the percentage of drip loss (loss in flavor and sensory properties) during thawing. Numerical modeling can be used to monitor and control quality changes during the freezing and thawing processes. This technique provides accurate predictions and visual information that could greatly improve quality control and be used to develop advanced cold storage and transport technologies. Finite element modeling (FEM) has become a widely applied numerical tool in industrial food applications, particularly in freezing and thawing processes. We review the recent studies on applying FEM in the food industry, emphasizing the freezing and thawing processes. Challenges and problems in these two main parts of the food industry are also discussed. To control ice crystallization and avoid cellular structure damage during freezing, including physicochemical and microbiological changes occurring during thawing, both traditional and novel technologies applied to freezing and thawing need to be optimized. Mere experimental designs cannot elucidate the optimum freezing, frozen storage, and thawing conditions. Moreover, these experimental procedures can be expensive and time-consuming. This review demonstrates that the FEM technique helps solve mass and heat transfer equations for any geometry and boundary conditions. This study offers promising insight into the use of FEM for the accurate prediction of key information pertaining to food processes.

Goals in Nutrition Science 2020-2025

Five years ago, with the editorial board of Frontiers in Nutrition, we took a leap of faith to outline the Goals for Nutrition Science - the way we see it (1). Now, in 2020, we can put ourselves to the test and take a look back. Without a doubt we got it right with several of the key directions. To name a few, Sustainable Development Goals (SDGs) for Food and Nutrition are part of the global public agenda, and the SDGs contribute to the structuring of international science and research. Nutritional Science has become a critical element in strengthening work on the SDGs, and the development of appropriate methodologies is built on the groundwork of acquiring and analyzing big datasets. Investigation of the Human Microbiome is providing novel insight on the interrelationship between nutrition, the immune system and disease. Finally, with an advanced definition of the gut-brain-axis we are getting a glimpse into the potential for Nutrition and Brain Health. Various milestones have been achieved, and any look into the future will have to consider the lessons learned from Covid-19 and the sobering awareness about the frailty of our food systems in ensuring global food security. With a view into the coming 5 years from 2020 to 2025, the editorial board has taken a slightly different approach as compared to the previous Goals article. A mind map has been created to outline the key topics in nutrition science. Not surprisingly, when looking ahead, the majority of scientific investigation required will be in the areas of health and sustainability. Johannes le Coutre, Field Chief Editor, Frontiers in Nutrition.

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requirements; environmental performance; and process control. Typical objective functions in these categories are described, selection/ranking techniques are outlined, and available software for MOO are listed. It is concluded that MOO is gaining popularity as an important tool and is having an increasing use and impact in chemical process engineering.

Issues surrounding consumer trust and acceptance of existing and emerging food processing technologies

The purpose of food processing today is to make food safer, more nutritious and tastier, and to increase storage life. Consumers have a lack of trust in the way food is produced, formulated and processed, particularly with possible contaminants or chemical residues from production. Food manufacturers are not seen as being highly trusted sources. This may partly result from manufacturers' reluctance to share all information and to protect intellectual property via patents and thus maintain a competitive edge. There is a need to inform the consumer better about what operations the involved ingredients are subjected to and why. Various ways of food processing are reviewed. New food processing technologies face challenges when introduced and factors influencing consumers' and other stakeholders' acceptance should be part of decision-making process when adopting new technologies. Consumers' perception of risks is not the same as the risk assessment made by experts. A few specific cases are being discussed to further highlight the multiplicity of factors that may contribute to the development of a certain consumer perception about a product or a class of products. This is also linked to the emergence of certain terminologies that are associated with an increasingly negative perception of the processing of foods. We recommend more transparency on food formulation and food processing to restore consumer trust, which enables to take the advantage of the benefits different processing methods offer. Food manufacturers must make an effort to let consumers know how their food is being processed within the walls of the factory and highlight the benefits vis-à-vis preparing foods in a domestic environment.

Effects of Thermal Treatment on the Physical Properties of Edible Calcium Alginate Gel Beads: Response Surface Methodological Approach

Calcium alginate gel (CAG) has been widely investigated for the development of artificial foods; however, there are few studies on its thermal stability. This study aimed to monitor changes in the physical properties of CAG beads during heat treatment using response surface methodology. Heating temperature (X₁, 40–100 °C) and heating time (X₂, 5–60 min) were chosen as independent variables. The dependent variables were rupture strength (Y₁, kPa), size (Y₂, μm), and sphericity (Y₃, %). The heating temperature (X₁) was the independent variable that had a significant effect on the rupture strength (Y₁) and size (Y₂). Rupture strength (Y₁) increased as the heating temperature (X₁) increased; at the same time, the CAG beads size (Y₂) decreased. With all conditions, the values of sphericity (Y₃) were over 94%. SEM images revealed that increase in the rupture strength of the CAG beads by heat treatment resulted from their porous structures. Loss of moisture by syneresis, occurring with heat treatment, was judged to create a dense porous structure of CAG beads. Our findings offer useful information for cooking or sterilizing food products utilizing CAG beads. In addition, thermal treatment could be applied to produce hard CAG beads with a high rupture strength.