Food quality and safety risk diagnosis in the food cold chain through failure mode and effect analysis

Unveiling the impact of high pressure and low temperature coupling on gelatin gel properties

Balancing shelf life extension and food quality is a key challenge in food processing. Conventional air freezing (CAF) methods inhibit microbial growth but often create large ice crystals that damage food texture, nutrition, flavor, and water holding capacity. High-pressure and low-temperature coupling (HPLT) technologies, such as pressure-shift freezing (PSF) and pressure-assisted freezing (PAF), offer innovative solutions to these limitations. This study explores the effects of HPLT on gelatin gel, focusing on ice crystal morphology, mechanical properties, and water distribution. PSF and PAF produce smaller, more uniform ice crystals, reducing structural damage and preserving gel strength and texture. HPLT also decreases water loss, enhancing gel integrity during freezing. These results demonstrate HPLT's potential to revolutionize frozen food processing, minimizing quality degradation, reducing food waste, and promoting global food security.

Estimating Salmonella Typhimurium Growth on Chicken Breast Fillets Under Simulated Less-Than-Truckload Dynamic Temperature Abuse

Companies may have insufficient freight to fill an entire truck/trailer, and instead only pay for space that their products occupy (i.e., "less-than-truckload" shipping; LTL). As LTL delivery vehicles make multiple stops, there is an increased opportunity for product temperature abuse, which may increase microbial food safety risk. To assess LTL effects on Salmonella Typhimurium growth, commercially produced boneless skinless chicken breast fillets were inoculated and incubated under dynamic 2-h temperature cycles (i.e., 2 h at 4°C and then 2 h at 25°C), mimicking a commercially relevant LTL scenario. Bacterial kinetics were measured over 24 h and then observations compared with predictions of three published Salmonella secondary models by bias and accuracy factor measurement. One model produced more "fail-safe" estimates of Salmonella growth than the other models, although all models were defined as "acceptable." These developed tertiary models can help shippers assess supply chain performance and produce proactive food safety risk management systems.

UV-ozone sterilization system: An intelligent solution for Northern shrimp (Pandalus borealis) cold chain transportation emergencies

Sterilization of Northern shrimp (Pandalus borealis) is a key tool to ensure their freshness for post-production transportation. However, in the face of the specific problem of quality deterioration caused by the increase of storage environment temperature due to unexpected circumstances or the prolongation of temporary storage time, it is still a technical challenge to realize intelligent decision-making and higher sterilization efficiency. In this paper, we propose an intelligent UV-Ozone sterilization system suitable for cold chain transportation of Northern shrimp (Pandalus borealis). Using hierarchical analysis, equipartition method and the prediction method of generalized linear model, combined with the technology of intelligent control and remote control, we realized the automatic control of the system's UV irradiance from 324 ∼ 1620 J/m2, and ozone concentration from21.4 ∼ 107 mg/cm3 in a graded manner. The accuracy of the predicted structure was verified using a combination of direct measurement and simulation. In addition, the key model of the system, the intensity level decision model, was tested, and the test results showed that the decision model was able to accurately make decisions during the sterilization of Northern shrimp (Pandalus borealis), and the system was able to achieve a sterilization effect of 1-3 orders of magnitude. This reduces quality loss due to unexpected conditions, facilitates real-time monitoring of transported samples by staff, extends the shelf life of the samples, and improves the accuracy of sterilization, increasing the economic value of Northern shrimp (Pandalus borealis).

Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster-Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.

Risk Assessment of Import Cold Chain Logistics Based on Entropy Weight Matter Element Extension Model: A Case Study of Shanghai, China

The development of world trade and fresh-keeping technology has led to the rapid development of international cold chain logistics. However, the novel coronavirus epidemic continues to spread around the world at the present stage, which challenges disease transmission control and safety supervision of international cold chain logistics. Constructing an Import Cold Chain Logistics Safety Supervision System (ICCL-SSS) is helpful for detecting and controlling disease import risk. This paper constructs an evaluation index system of ICCL safety that comprehensively considers the potential risk factors of three ICCL processes: the logistics process in port, the customs clearance process, and the logistics process from port to door. The risk level of ICCL-SSS is evaluated by combining the Extension Decision-making Model and the Entropy Weight Method. The case study of Shanghai, China, the world's largest city of ICCL, shows that the overall risk level of ICCL-SSS in Shanghai is at a moderate level. However, the processes of loading and unloading, inspection and quarantine, disinfection and sterilization, and cargo storage are at high risk specifically. The construction and risk assessment of ICCL-SSS can provide theoretical support and practical guidance for improving the safety supervision ability of ICCL regulation in the post-epidemic era, and helps the local government to scientifically formulate ICCL safety administration policies and accelerate the development of world cold chain trade.

Application of Cold-Chain Logistics and Distribution Systems Using Deliver Schedule Management

Cold chain logistic firms have been motivated to decrease overall operating costs and carbon emissions to capture economic edge and maintain profitability by intense competition and financial energy requirements. C Our analysis develops a model cold-chain logistic and distribution system (CC-LDS) for logistics and transport firms working together to manufacture chilled and frozen goods by introducing carbon tax policies. The CC-LDS model provides a logistics and transport network. Virtual annealing (VA) algorithm for optimising the model is implemented based on actual customer information from multiple cold storage firms and 30 clients. The findings suggest that the second derivative is optimal compared to the individual distribution to slash overall expense and carbon pollution. The net cost is strongly associated with the cost of carbon, and energy consumption is similar to the price of carbon grows. Moreover, carbon caps have little effect on the direction of distribution.To best leverage social and technological capital to accomplish equal financial and ecological gains.

Effect of Different Degrees of Deep Freezing on the Quality of Snowflake Beef during Storage

In order to elucidate whether deep freezing could maintain the quality of snowflake beef, three different deep freezing temperatures (−18 °C, −40 °C, and −60 °C) were used in order to evaluate the changes in tissue structures, quality characteristics and spoilage indexes, and their comparative effects on the quality of snowflake beef. Compared to samples frozen at −18 °C, those stored at −40 °C and −60 °C took a shorter time to exceed the maximum ice crystallization zone (significantly reduced by 2–6 h). In terms of short-term storage, samples frozen at −40 °C and −60 °C had better tissue structure and lower drip loss rate than those frozen at −18 °C; significant differences between groups in drip loss were observed between −18 °C and −60 °C. Moreover, a better bright red color and lower shear force were maintained at −40 °C and −60 °C, with significant differences in shear force between the −18 °C group and the other two groups on day 60. Although there were significant effects on the inhibition of lipid and protein oxidation at −40 °C and −60 °C; no significant variation was observed between these two groups throughout storage. A similar phenomenon was found in flavor, with 1-pentanol identified as an important potential indicator of flavor change in snowflake beef during storage. This study demonstrated that −40 °C and −60 °C had favorable impacts on the quality maintenance of snowflake beef compared to −18 °C. These findings provide a theoretical basis for effective stability of snowflake beef quality during frozen storage.

A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

This article proposes a calibrated individual semantic (CIS)-based failure mode and effect analysis (FMEA) to deal with the risk evaluation of industrial internet platforms (IIP) from four perspectives: network security, data processing capability, equipment performance, and openness. The novelty of the CIS model is based on the deviation between linguistic terms and numerical values to calibrate linguistic scales of decision-makers (DMs). Not only can it handle situations in which different DMs have different understandings of the same term, but it is also suitable for multiple attributes decision-making with uncertainty. In addition, this new FMEA framework considers the consensus-reaching process as a way to eliminate the disagreement among DMs from different departments. Finally, a comparison between the proposed and traditional method is presented to illustrate the advantages of new method.

Food cold chain management improvement: A conjoint analysis on COVID-19 and food cold chain systems

Cold chains are effective in maintaining food quality and reducing food losses, especially for long-distance international food commerce. Several recent reports have demonstrated that frozen foods are serving as carriers of SARS-CoV-2 and transmitting the virus from one place to another without any human-to-human contact. This finding highlights significant difficulties facing efforts to control the spread of COVID-19 and reveal a transmission mechanism that may have substantially worsened the global pandemic. Traditional food cold chain management practices do not include specific procedures related to SARS-CoV-2-related environmental control and information warnings; therefore, such procedures are urgently needed to allow food to be safely transported without transmitting SARS-CoV-2. In this study, a conjoint analysis of COVID-19 and food cold chain systems was performed, and the results of this analysis were used to develop an improved food cold chain management system utilizing internet of things (IoT) and blockchain technology. First, 45 COVID-19-related food cold chain incidents in China, primarily involving frozen meat and frozen aquatic products, were summarized. Critical food cold chain control points related to COVID-19 were analyzed, including temperature and cold chain requirements. A conceptual system structure to improve food cold chain management, including information sensing, chain linking and credible tracing, was proposed. Finally, a prototype system, which consisted of cold chain environment monitoring equipment, a cold chain blockchain platform, and a food chain management system, was developed. The system includes: 1) a defining characteristic of the newly developed food cold chain system presented here is the use of IoT technology to enhance real-time environmental information sensing capacity; 2) a hybrid data storage mechanism consisting of off-chain and on-chain systems was applied to enhance data security, and smart contracts were used to establish warning levels for food cold chain incidents; and 3) a hypothetical food cold chain failure scenario demonstration in which information collection, intelligent decision making, and cold chain tracing were integrated and automatically generated for decision-making. By integrating existing technologies and approaches, our study provides a novel solution to improve traditional food cold chain management and thus meet the challenges associated with the COVID-19 pandemic. Although our system has been shown to be effective, subsequent studies are still required to develop precise risk evaluation models for SARs-CoV-2 in food cold chains and more precisely control the entire process. By ensuring food safety and reliable traceability, our system could also contribute to the formulation of appropriate mechanisms for international cooperation and minimize the effect of the COVID-19 pandemic on international food commerce.

Nanostructured Temperature Indicator for Cold Chain Logistics

Food, chemicals, agricultural products, drugs, and vaccines should be transported and stored within an appropriate low-temperature range, following cold chain logistics. Violations of the required temperature regime are generally reported by time-temperature indicators; however, current sensors do not cover a sufficiently broad low-temperature range and may lack thermal and photostability. Here, we report a nanostructured solvatochromic temperature indicator formed from cellulose nanocrystals decorated with carbon dots (C-dots). The indicator utilizes a strong nonlinear dependence of photoluminescence of C-dots on the composition of water/dimethyl sulfoxide (DMSO) solvent and a composition-dependent variation of the melting temperature of the water/DMSO mixture. Exceeding the temperature of the frozen mixed solvent above a designated threshold value results in solvent melting, flow, and impregnation of the nanostructured film, thus causing an irreversible change in the intensity and wavelength of photoluminescence emission of the film, which is reported both qualitatively and quantitatively. The indicator covers a temperature range from -68 to +19 °C and is cost-efficient, portable and photo- and thermostable.

An Application of Analytic Hierarchy Process and Entropy Weight Method in Food Cold Chain Risk Evaluation Model

The food cold chain is a special type of cold chain that refers to a system in which refrigerated and frozen food is always kept in the specified low-temperature environment in all links from production, storage, transportation, sales, distribution to consumption, so as to ensure food quality and to prevent food deterioration caused by temperature fluctuation. In recent years, the coronavirus disease 2019 (COVID-19) has brought a great impact on people's life and the social economy and also threatened the large-scale food cold chain. Through the effective identification and evaluation of high-risk factors in the food cold chain, this article has found the major risks that have a great impact on the entire food cold chain and proposes the specific measures of risk management and control to solve the problems of food cold chain and reduce risks quickly and efficiently to ensure the stability and safety of food cold chain and avoid the serious food safety accidents. The contribution of this article is reflected in three aspects, namely, (1) applies the expert system based on professional knowledge and rich experience and constructs a classification and identification system structure of food cold chain risk indexes, which lay a foundation for further identifying and evaluating the major risks of the food cold chain; (2) designs a comprehensive index weighting method combining the AHP method and entropy weight method to quantitatively evaluate the major risks. This comprehensive method combines a hierarchical structure system, evaluation algorithm, subjective factor correction algorithm, and so on. The evaluation results are more accurate, have a high matching degree with reality, and have good theoretical and practical significance; (3) analyzes and explains the major risks of the food cold chain in the non-epidemic situations and COVID-19 situations. Proposals and measures for risk management and control are put forward, which have wide practical significance.

Contamination by Listeria monocytogenes in Latin American Meat Products and Its Consequences

Background and objective:

Listeria monocytogenes is one of the most important bacteria in food technology, causing listeriosis, a disease with high mortality rates, important especially in developing countries. Thus, the objective of this review was to gather recent work on the presence of L. monocytogenes in meat and meat products in Latin America, in addition to pointing out control methods and resistance genes that can be disseminated.

Methods:

Original research articles in Portuguese, Spanish and English published since 2017 were selected, reporting the presence of L. monocytogenes in meat and meat products in Latin American countries. Articles were also reviewed on innovative methods for controlling the bacteria in food, such as intelligent packaging and the use of essential oils, and on resistance genes found in L. monocytogenes, pointing out the possible implications of this occurrence.

Results and conclusion:

Some negligence was observed in determining the prevalence of this bacterium in several countries in Latin America. Although studies on L. monocytogenes have been found in milk and dairy products, demonstrating the existence of the necessary structure and knowledge for research development, studies on meat and meat products have not been found in most countries. In control methods developed against L. monocytogenes, the versatility of the approaches used stands out, enabling their use in different types of meat products, according to their technological characteristics. Several resistance genes have been determined to be carried and possibly disseminated by L. monocytogenes, which adds more importance in the establishment of methods for its control.

Microwave reheating enriches resistant starch in cold-chain cooked rice: A view of structural alterations during digestion

Cold-chain cooked rice is an instant food consumed worldwide. Through inspecting rice structural alterations during digestion, this work discloses how microwave reheating tailors the starch digestibility of cooked rice following cold storage. The cold storage allowed approximately 2% of B-type (not V-type) starch crystallites, more nanoscale and short-range orders, and smaller pores in the rice matrix. These changes retarded the hydrolysis of structural domains (e.g., amorphous regions and short-range orders) during digestion, which increased the content of slowly digestible starch to about 38.16%. Then, microwave reheating partially disrupted the B-type crystallites and nanoscale orders, but unaffected the contents of V-type crystallites and short-range orders. Even with such structural disruptions, the resistant starch content was apparently increased to approximately 30.06%, as the structural domains became less susceptible to the digestion. Additionally, for the rice samples, the percentage of V-type crystallites could be largely increased from ca. 3% to 13%-14% during digestion.

Economic Impact of Temperature Control during Food Transportation-A COVID-19 Perspective

Temperature fluctuation and abuse in the food cold chain (FCC) is becoming an increasingly crucial factor in the process of food production and for the logistic business, especially in COVID-19 pandemic. The quality of perishable food products depends largely on accurate transport and maintenance temperature. The evidence for temperature-related food waste and loss is extensive. The research problem is thus: how to decrease and control food losses caused by temperature abuse in the FCC and restrictions due to the COVID-19 pandemic. The primary objective is to propose a framework for real-time temperature measurement protocols supported by passive RFID, IoT and Statistical Process Control (SPC) charts. This method allows not only the signaling of temperature abuse alerts but, in addition to hitherto methods, investigation and mitigation of the causes of process instability of individual FCC links in the future. The secondary objective is to delineate the necessary data sources and ways of their collection and utilization in order to decrease food losses and waste via process stabilization of temperature in transport and storage. As contribution to current literature and practice, we offer an in-depth analysis of threats in the FCC in food transport and storage infrastructure and a solution supplemented by SPC charts and tested in controlled experiments that is practicable from economic and technical standpoints.

Evaluating agricultural food supply chain resilience in the context of the COVID-19 pandemic

With the spread of the global COVID-19 pandemic, agricultural food supply chains (AFSC) have suffered from unprecedented challenges and disruption. AFSC must possess extremely high levels of resilience when confronted by the pandemic. In this study, we have identified six important resilience criteria and eighteen sub-criteria of AFSC in the context of the COVID-19. The research presents the conceptual mixed multi-criteria decision-making (MCDM) technology to prioritize the resilience criteria based on understanding their interrelationships. Results identify three essential resilience criteria, namely “coordination and collaboration in the supply chain”, “efficiency”, and “strategic management” and the top five key sub-criteria as “velocity”, “visibility”, “continuity management”, “connectedness”, “collaborative planning and replenishment”. The framework proposed in this study contributes to the interdisciplinary understanding towards building resilience within AFSC, and has the potential to be extended to other types of supply chains in response to COVID-19.

Cold-chain cooked rice with different water contents: Retarded starch digestion by refrigeration

Cold-chain cooked rice is a widely-consumed instant food. While the quality of cooked rice as affected by processing has been widely studied, it remains largely unexplored how concurrent cold-chain conditions (e.g., refrigeration time with specific water contents) tailor the structure and starch digestibility of cooked rice. Here, as shown by combined techniques (e.g., scanning electron microscopy and small angle X-ray scattering), the cold storage (1 to 3 days) of cooked rice at 1.1:1 w/w water-to-rice ratio increased the uniformity of the rice matrix, strengthened the nonperiodic structure, and allowed more B-type starch crystallites and short-range orders. This induced an increase in the slowly digestible starch (SDS) content (from ca. 33.7% to 38.5%) as the refrigeration time rose. In contrast, for cooked rice with 1.5:1 w/w water-to-rice ratio, the cold storage (mainly 1 day) strengthened the matrix uniformity and the nonperiodic structure, and eventually increased the resistant starch (RS) content from ca. 10.3% to 17.7%. The present data could facilitate the design of cold-chain cooked rice with tailored starch digestibility.

Analysis of the Temperature Distribution in a Refrigerated Truck Body Depending on the Box Loading Patterns

The main purpose of cold chain is to keep the temperature of products constant during transportation. The internal temperature of refrigerated truck body is mainly measured with a temperature sensor installed at the hottest point on the body. Hence, the measured temperature cannot represent the overall temperature values of transported products in the body. Moreover, the airflow pattern in the refrigerated body can vary depending on the arrangement of loaded logistics, resulting temperature differences between the transported products. In this study, the airflow and temperature change in the refrigerated body depending on the loading patterns of box were analyzed using experimental and numerical analysis methods. Ten different box loading patterns were applied to the body of 0.5 ton refrigerated truck. The temperatures inside boxes were measured depending on the loading patterns. CFD modeling with two different turbulence models (k-ε and SST k-ω) was developed using COMSOL Multiphysics for predicting the temperatures inside boxes loaded with different patterns, and the predicted data were compared to the experimental data. The k-ε turbulence model showed a higher temperature error than the SST k-ω model; however, the highest temperature point inside the boxes was almost accurately predicted. The developed model derived an approximate temperature distribution in the boxes loaded in the refrigerated body.

A Hybrid Traceability Technology Selection Approach for Sustainable Food Supply Chains

Traceability technologies have great potential to improve sustainable performance in cold food supply chains by reducing food loss. In existing approaches, traceability technologies are selected either intuitively or through a random approach, that neither considers the trade-off between multiple cost–benefit technology criteria nor systematically translates user requirements for traceability systems into the selection process. This paper presents a hybrid approach combining the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with integer linear programming to select the optimum traceability technologies for improving sustainable performance in cold food supply chains. The proposed methodology is applied in four case studies utilising data collected from literature and expert interviews. The proposed approach can assist decision-makers, e.g., food business operators and technology companies, to identify what combination of technologies best suits a given food supply chain scenario and reduces food loss at minimum cost.

The Effect of Traceability System and Managerial Initiative on Indonesian Food Cold Chain Performance: A Covid-19 Pandemic Perspective

This study aims to determine the effect of managerial initiatives on the adoption of traceability systems on food cold chain performance during the Covid-19 pandemic. Managerial initiatives are allegedly needed to improve the company's performance because it improves the traceability system in the supply chain. In addition, the effect of the traceability system adoption on the Indonesian food cold-chain performance during the Covid-19 pandemic is also discussed in this study. This study uses a quantitative approach and purposive sampling with a questionnaire research instrument obtained 250 statements of Indonesian consumers and retail employees. Partial least squares for structural equation modeling (PLS-SEM) were used to analyze latent variables' relationships. This study indicates that the traceability system has a significant effect on the performance of the food cold-chain during the Covid-19 pandemic. In addition, the adoption of electronic data exchange (EDI), radio frequency identification (RFID), and blockchain significantly impacted traceability systems during the Covid-19 pandemic. The managerial application of the initiative showed a positive and significant impact on the performance of the food cold-chain during the Covid-19 pandemic. However, the managerial initiative is not able to moderate the adoption of the traceability system.

Food Safety Gaps between Consumers' Expectations and Perceptions: Development and Verification of a Gap-Assessment Tool

In recent decades, food safety has become a major concern due to frequent food safety incidents in many countries. This may lead to increased health risks associated with low quality food consumption, thereby reducing consumer trust in food safety. A better understanding of consumer perceptions of food safety can improve indicators that do not meet consumer expectations. We propose a food safety gap model with four gap-construct based on consumer expectations and perceptions. The model was empirically tested through a survey of 25 items, and then assessed for gaps through the importance-performance analysis (IPA). From a sample of 697 Taiwanese consumers, we found a huge gap between consumer expectations and perceptions related to food safety. More importantly, the results of the IPA indicate that most items must be immediately improved, which is vital in order to mitigate the risk of food safety.