Time-temperature abuse in the food cold chain: Review of issues, challenges, and recommendations

Modeling the growth of Salmonella in raw ground pork under dynamic conditions of temperature abuse

Salmonella contamination of pork products is a significant public health concern. Temperature abuse scenarios, such as inadequate refrigeration or prolonged exposure to room temperature, can enhance Salmonella proliferation. This study aimed to develop and validate models for Salmonella growth considering competition with background microbiota in raw ground pork, under isothermal and dynamic conditions of temperature abuse between 10 and 40 °C. The maximum specific growth rate (μmax) and maximum population density (MPD) were estimated to quantitatively describe the growth behavior of Salmonella. To reflect more realistic microbial interactions in Salmonella-contaminated product, our model considered competition with the background microbiota, measured as mesophilic aerobic plate counts (APC). Notably, the μmax of Salmonella in low-fat samples (∼5 %) was significantly higher (p < 0.05) than that in high-fat samples (∼25 %) at 10, 20, and 30 °C. The average doubling time of Salmonella was 26, 4, 2, 1.5, 0.8, and 1.1 h at 10, 15, 20, 25, 30, and 40 °C, respectively. The initial concentration of Salmonella minimally impacted its growth in ground pork at any temperature. The MPD of APC consistently exceeded that of Salmonella, indicating the growth of APC without competition from Salmonella. The competition model exhibited excellent fit with the experimental data, as 95 % (627/660) of residual errors fell within the desired acceptable prediction zone (pAPZ >0.70). The theoretical minimum and optimum growth temperatures for Salmonella ranged from 5 to 6 °C and 35 to 36 °C, respectively. The dynamic model displayed strong predictive performance, with 90 % (57/63) of residual errors falling within the APZ. Dynamic models could be valuable tools for validating and refining simpler static or isothermal models, ultimately improving their predictive capabilities to enhance food safety.

Growth of Listeria monocytogenes in fresh vegetables and vegetable salad products: An update on influencing intrinsic and extrinsic factors

The ability of foodborne pathogens to grow in food products increases the associated food safety risks. Listeria monocytogenes (Lm) is a highly adaptable pathogen that can survive and grow under a wide range of environmental circumstances, including otherwise inhibitory conditions, such as restrictive cold temperatures. It can also survive long periods under adverse environmental conditions. This review examines the experimental evidence available for the survival and growth of Lm on fresh vegetables and ready-to-eat vegetable salads. Published data indicate that, depending on certain intrinsic (e.g., nutrient composition) and extrinsic factors (e.g., storage temperature, packaging atmosphere), Lm can survive on and in a wide variety of vegetables and fresh-cut minimally processed vegetable salads. Studies have shown that temperature, modified atmosphere packaging, relative humidity, pH, water activity, background microbiota of vegetables, microbial strain peculiarities, and nutrient type and availability can significantly impact the fate of Lm in vegetables and vegetable salads. The influence of these factors can either promote its growth or decline. For example, some studies have shown that background microbiota inhibit the growth of Lm in vegetables and minimally processed vegetable salads, but others have reported a promoting, neutral, or insignificant effect on the growth of Lm. A review of relevant literature also indicated that the impact of most influencing factors is related to or interacts with other intrinsic or extrinsic factors. This literature synthesis contributes to the body of knowledge on possible strategies for improving food safety measures to minimize the risk of Lm-associated foodborne outbreaks involving vegetables and vegetable salads.

Potential risk of biofilm-forming Bacillus cereus group in fresh-cut lettuce production chain

Bacillus cereus and Bacillus thuringiensis, which belong to the B. cereus group, are widely distributed in nature and can cause food poisoning symptoms. In this study, we collected 131 isolates belonging to the B. cereus group, comprising 124B. cereus and seven B. thuringiensis isolates, from fresh-cut lettuce production chain and investigated their potential risk by analyzing genotypic (enterotoxin and emetic toxin gene profiles) and phenotypic (antibiotic susceptibility, sporulation, and biofilm formation) characteristics. Enterotoxin genes were present only in B. cereus, whereas the emetic toxin gene was not detected in any of the B. cereus isolates. All isolates were susceptible to vancomycin, which is a last resort for treating B. cereus group infection symptoms, but generally resistant to β-lactam antimicrobials, and had the ability to form spores (at an average sporulation rate of 24.6 %) and biofilms at 30 °C. Isolates that formed strong biofilms at 30 °C had a superior possibility of forming a dense biofilm by proliferating at 10 °C compared to other isolates. Additionally, confocal laser scanning microscopy (CLSM) images revealed a notable presence of spores within the submerged biofilm formed at 10 °C, and the strengthened attachment of biofilm inner cells to the substrate was further revealed through biofilm structure parameters analysis. Collectively, our study revealed the prevalence and contamination levels of B. cereus and B. thuringiensis at fresh-cut lettuce production chain and investigated their genotypic and phenotypic characteristics, aiming to provide valuable insights for the development of potential risk management strategies to ensure food safety, especially along the cold chain.

Changes in meat quality, metabolites and microorganisms of mutton during cold chain storage

During the cold chain storage process, changes in metabolites and microorganisms are highly likely to lead to changes in meat quality. To elucidate the changes in the composition of metabolites and microbiota during cold chain storage of mutton, this study utilized untargeted metabolome and 5R 16S rRNA sequencing analyses to investigate the changes in the longissimus dorsi under different cold chain temperatures (4 °C and -20 °C). With the extension of cold chain storage time, the meat color darkened and the content of C18:2n-6, C20:3n-6, and C23:0 were significantly increased in mutton. In this study, nine metabolites, including 1,2-Dioleoyl-sn-glycero-3-phosphoethanolamine, alanylphenylala-nine, indole-3-acrylic acid and the others, were significantly altered during cold chain storage. The abundance of the dominant microorganisms, including Brachymonas, Aeromonas, Corynebacterium and Steroidobacter, was significantly altered. Furthermore, a high correlation was observed between the different metabolites and microorganisms. These findings provide an in-depth understanding of the effects of different cold chain storage temperatures and times on the quality of mutton.

Assessment of the exposure to cytotoxic Bacillus cereus group genotypes through HTST milk consumption

This study addresses the limited tools available for assessing food safety risks from cytotoxic Bacillus cereus group strains in contaminated food. We quantified the growth, in skim milk broth, of 17 cytotoxic B. cereus strains across 6 phylogenetic groups with various virulence gene profiles. The strains did not grow in HTST milk at 4 or 6°C. At 10°C, 15 strains exhibited growth; at 8°C, one strain grew; and all strains grew at temperatures ≥ 14°C. Using growth data from 16 strains, we developed linear secondary growth models and an exposure assessment model. This model, simulating a 5-stage HTST milk supply chain and up to 35 d of consumer storage with an initial contamination of 100 cfu/mL, estimated that 2.81 ± 0.66% and 4.13 ± 2.53% of milk containers would surpass 105 cfu/mL of B. cereus by d 21 and 35, respectively. A sensitivity analysis identified the initial physiological state of cells (Q0) as the most influential variable affecting predictions for specific isolates. What-if scenarios indicated that increases in mean and variability of consumer storage temperatures significantly affected the predicted B. cereus concentrations in milk. This model serves as an initial tool for risk-based food safety decision making regarding low-level B. cereus contamination.

Flash heating process for efficient meat preservation

Maintaining food safety and quality is critical for public health and food security. Conventional food preservation methods, such as pasteurization and dehydration, often change the overall organoleptic quality of the food products. Herein, we demonstrate a method that affects only a thin surface layer of the food, using beef as a model. In this method, Joule heating is generated by applying high electric power to a carbon substrate in <1 s, which causes a transient increase of the substrate temperature to > ~2000 K. The beef surface in direct contact with the heating substrate is subjected to ultra-high temperature flash heating, leading to the formation of a microbe-inactivated, dehydrated layer of ~100 µm in thickness. Aerobic mesophilic bacteria, Enterobacteriaceae, yeast and mold on the treated samples are inactivated to a level below the detection limit and remained low during room temperature storage of 5 days. Meanwhile, the product quality, including visual appearance, texture, and nutrient level of the beef, remains mostly unchanged. In contrast, microorganisms grow rapidly on the untreated control samples, along with a rapid deterioration of the meat quality. This method might serve as a promising preservation technology for securing food safety and quality.

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Produce

A review of quantitative risk assessment (QRA) models of Listeria monocytogenes in produce was carried out, with the objective of appraising and contrasting the effectiveness of the control strategies placed along the food chains. Despite nine of the thirteen QRA models recovered being focused on fresh or RTE leafy greens, none of them represented important factors or sources of contamination in the primary production, such as the type of cultivation, water, fertilisers or irrigation method/practices. Cross-contamination at processing and during consumer's handling was modelled using transfer rates, which were shown to moderately drive the final risk of listeriosis, therefore highlighting the importance of accurately representing the transfer coefficient parameters. Many QRA models coincided in the fact that temperature fluctuations at retail or temperature abuse at home were key factors contributing to increasing the risk of listeriosis. In addition to a primary module that could help assess current on-farm practices and potential control measures, future QRA models for minimally processed produce should also contain a refined sanitisation module able to estimate the effectiveness of various sanitisers as a function of type, concentration and exposure time. Finally, L. monocytogenes growth in the products down the supply chain should be estimated by using realistic time-temperature trajectories, and validated microbial kinetic parameters, both of them currently available in the literature.

Kinetic models applied to quality change and shelf-life prediction of fresh-cut pineapple in food cold chain

This study developed a predictive model of the remaining shelf-life of fresh-cut pineapple by considering microbial, chemical, and sensory indicators under storage temperatures.The effect of temperature on quality indices (Ea)was29.57-96.8 kJ/mol. The acceptable limits for vitamin C loss and weight loss for fresh-cut pineapple were 27.71 mg/100 g fresh weight and 4.32 %, respectively, in accordance with cutoffs determined on the basis of sensory changes. Key quality indicators that could predict the remaining shelf-life between 5 °C and 25 °C were the TVC and WL. Vitamin C loss was only suitable when storage temperature was above 10 °C. The remaining shelf-life in a real supply chain in terms of the TVC, vitamin C loss, and WL were 9.9, 3.5, and 4 days, respectively. Overall, the shelf-life prediction model developed using selected key indicators may help in managing temperature in the fresh-cut fruit supply chain and improving storage shelf-life.

Effect of nisin, EDTA, and abuse temperature on the growth of Salmonella Typhimurium in liquid whole egg during refrigerated storage

Salmonella spp. can be present in pasteurized liquid egg products because of its heat resistance or post-processing contamination, thereby representing a food safety risk. The effect of 1000 IU nisin/ml plus 20 mM disodium ethylenediaminetetraacetic acid (EDTA), two refrigerated temperatures (7 °C and 10 °C), and two inoculation levels (103 and 105 cfu/ml) were studied in the growth of S. Typhimurium in pasteurized liquid whole egg (LWE). Two mathematical models were used to fit the microbial curves. Physicochemical characteristics of LWE, such as pH and color, were assessed for 31 days at the two storage temperatures, and no significant changes (p < 0.05) were observed for most of the samples. Results showed the significant impact of temperature on microbial growth. Samples kept at 7 °C showed the decay of microbial cells during storage; meanwhile, the effect at 10 °C was shown as fast growth. The combination of nisin plus EDTA and 7 °C accelerated the decay of microbial cells during the storage showing a synergistic effect. The Weibull model described the decline of cells during the shelf-life. Meanwhile, the logistic model fitted the growth of Salmonella in LWE at 10 °C. Adding these additives to LWE, combined with the correct temperature during pasteurization and adequate conditions during the cold chain, can minimize the food safety risk related to Salmonella.

The Effect of Terminal Freezing and Thawing on the Quality of Frozen Dough: From the View of Water, Starch, and Protein Properties

Frozen dough is suitable for industrial cold chain transportation, but usually experiences temperature fluctuations through the cold chain to the store after being refrigerated in a factory, seriously damaging the product yield. In order to analyze the influence mechanism of temperature fluctuation during the terminal cold chain on frozen dough, the effects of terminal freezing and thawing (TFT) on the quality (texture and rheology) and component (water, starch, protein) behaviors of dough were investigated. Results showed that the TFT treatment significantly increased the hardness and decreased the springiness of dough and that the storage modules were also reduced. Furthermore, TFT increased the content of freezable water and reduced the bound water with increased migration. Additionally, the peak viscosity and breakdown value after TFT with the increased number of cycles were also increased. Moreover, the protein characteristics showed that the low-molecular-weight region and the β-sheet in the gluten secondary structure after the TFT treatment were increased, which was confirmed by the increased number of free sulfhydryl groups. Microstructure results showed that pores and loose connection were observed during the TFT treatment. In conclusion, the theoretical support was provided for understanding and eliminating the influence of the terminal nodes in a cold chain.

Research on Flesh Texture and Quality Traits of Kiwifruit (cv. Xuxiang) with Fluctuating Temperatures during Cold Storage

Kiwifruits are often exposed to various temperature fluctuations (TFs) during postharvest transportation and storage. To evaluate the effect of TFs on the qualities of kiwifruits during storage, kiwifruits were stored at 2 °C, 2 °C or 5 °C (TF2 °C-5 °C, alternating every 12 h), 2 °C or 7 °C (TF2 °C-7 °C, alternating every 12 h) for 3 d before long time storage at 2 °C. Observations revealed that kiwifruits stored at a constant 2 °C showed the lowest loss of weight and vitamin C because of minimized ethylene production and respiratory rate compared with that of TF2 °C-5 °C and TF2 °C-7 °C. Moreover, the results of RT-qPCR verified that the expression levels of genes encoding polygalacturonase, β-galacturonidase, and pectin methylesterase were significantly increased by the treatment of TF. Hence, TF accelerated the degradation of cell walls, softening, translucency, and relative conductivity of the flesh of kiwifruits. In addition, the impact of TF2 °C-7 °C on kiwifruits was more significant relative to TF2 °C-5 °C. The present study provides a theoretical basis for kiwifruit during cold storage.

Conformal Temperature/Impedance Sensing Patch Based on Graphene Materials for Nondestructive Detection of Fish Freshness

Rapid nondestructive detection of fish freshness is essential to ensure food safety and nutrition. In this study, we demonstrate a conformal temperature/impedance sensing patch for temperature monitoring, as well as freshness classification during fish storage. The optimization of the flexible laser-induced graphene electrodes is studied based on both simulation and experimental validation, and dimensional accuracy of 5‰ and high impedance reproducibility are obtained. A laser-assisted thermal reduction technology is innovatively introduced to directly form a reduced graphene oxide-based temperature-sensitive layer on the surface of a flexible substrate. The comprehensive performance is superior to that of most reported temperature-sensitive devices based on graphene materials. As an application demonstration, the fabricated flexible dual-parameter sensing patch is conformed to the surface of a refrigerated fish. The patch demonstrates the ability to accurately sense low temperatures in a continuous 120 min monitoring, accompanied by no interference from high humidity. Meanwhile, the collected impedance data are imported into the support vector machine model to obtain a freshness classification accuracy of 93.07%. The conformal patch integrated with crosstalk-free dual functions costs less than $1 and supports free customization, providing a feasible methodology for rapid nondestructive detection or monitoring of food quality.

Photonic Crystal Sensor Evaluating the Effectiveness of Medical Products under Different Storage Conditions

The effectiveness of time- and temperature-sensitive medical products (TTSMPs) (vaccines, medicines, and biological agents) is generally evaluated by sporadically checking the storage conditions recorded in electronic thermometers. However, electronic thermometers do not achieve all-time and all-regional record, resulting in the wrong evaluation of a single TTSMP and seriously endangering public health. Herein, we report a photonic crystal sensor for evaluating the effectiveness of a single TTSMP processing storage environment. The photonic crystal sensor assembled by colloidal microspheres (WO3-x nanospheres were added into the microsphere gap) generates a fascinating composite color of angle-dependent structural color (maximum reflectivity = 45%) and durative color (WO3-x coloration). Effectiveness evaluation principle reveals that the pattern on the sensor, which was printed by the composite color, fades sensitively to time and temperature, thus having different visible periods (0-21 days affected by temperature). The visible periods of the patterns can be used to evaluate a single TTSMP's effectiveness stored under different temperatures. Furthermore, the photonic crystal sensor shows outstanding flexibility and slight adhesion, offering a promising application toward the effectiveness evaluation of TTSMPs throughout storage, transportation, and sales processes.

The importance of supercooled stability for food during supercooling preservation: a review of mechanisms, influencing factors, and control methods

Supercooling can preserve food in its original fresh state below its ice point temperature without freezing. However, the supercooled state is unstable in thermodynamics, state breakdown can occur at any moment, resulting in irregular and larger ice crystals formation, leading to food tissue damage, and loss of quality and nutrients. While the effectiveness of supercooling preservation has been verified in the lab and pilot scale tests, the stability of the supercooled state of food remains an open question, posing a limitation for larger industrial-scale application of supercooling preservation. Based on this background, this review presents the instability mechanisms of supercooling preservation and summarizes the factors such as food properties (e.g., material size, food components, specific surface area, and surface roughness) and preservation circumstances (e.g., cooling rate, temperature variation, and mechanical disturbance) that influence the stability of the supercooled state of food. The review also discusses novel techniques for enhancing the supercooling capacity and their limitations (e.g., precise temperature control and magnetic field). Further studies are necessary to comprehensively evaluate the effects of influence factors and supercooling technologies on supercooling, realizing the true sense of 'no-crystal' food products under subzero temperature preservation conditions in commercial applications.

Mechanisms and Performances of Physically and Chemically Crosslinked Gelatin-Based Hydrogels as Advanced Sustainable and Reusable "Jelly Ice Cube" Coolants

A novel reusable, plastic-free, and stable cooling medium, Jelly Ice Cubes (JIC), is developed based on crosslinked gelatin hydrogels for sustainable temperature control. A novel process involving a rapid-freezing-slow-thawing treatment and a subsequent photo-crosslinking reaction induced by menadione sodium bisulfite, a newly discovered photosensitizer, is able to effectively consolidate a three-dimensional (3-D) hydrogel network to survive repeated application freeze-thaw cycles (AFTCs). This study reveals the mechanisms and evidence of the synergistic effects of the physical and chemical crosslinking reactions. The results experimentally prove that the rapid-freezing-slow-thawing treatment induces the generation of gelatin microcrystalline domains, refines the protein polymeric network, and reduces the intervening distance for subsequent photo-crosslinking sites. The refined hydrogel 3-D network is consolidated by the photo-crosslinking reaction occurring at the intersectional areas of the gelatin microcrystalline domains. The proposed crosslinking approach yields JICs with superior mechanical properties, robustness, and consistent water content, even after repeated AFTCs, all the while retaining cooling efficiency and biodegradability. The proposed crosslinked hydrogel structure is potentially applicable to engineering other hydrogel materials, offering sustainble and biodegradable solutions with enhanced resilience against phase changes.

Isothermal Storage Delays the Senescence of Post-Harvest Apple Fruit through the Regulation of Antioxidant Activity and Energy Metabolism

The purpose of this work was to elucidate the influence of TF (5 ± 5 °C, and 5 ± 1 °C) and CT (5 ± 0.1 °C served as an isothermal state) storage environment on the antioxidant ability and energy metabolism in post-harvest apple fruit during storage. Specifically, compared with fruit in TFs groups, the quality attributes of apples in the CT group, including firmness, fresh weight, contents of SSC, and TA were maintained at a higher level. In addition, fruit stored in the CT environment revealed a suppressed respiration rate and EL, lower MDA, O₂·^-, and H₂O₂ accumulation but increased the activities of SOD, CAT, APX, and GR. At the end of storage, the SOD, CAT, APX, and GR activities of fruit in the CT group were 38.14%,48.04%, 115.29%, and 34.85% higher than that of the TF5 group, respectively. Fruit in the CT environment also revealed higher AsA, GSH, total phenols, and total flavonoid content. In addition, fruit stored in the CT environment maintained higher ATP content, EC, and more active H⁺-ATPase, Ca²⁺-ATPase, CCO, and SDH. At the end of storage, the SDH and CCO activities of fruit in the TF0.1 group were 1.74, and 2.59 times higher than that in the TF5 group, respectively. Taken together, we attributed the fact that a constant temperature storage environment can retard the fruit senescence to the enhancement of antioxidant capacities and maintaining of higher energy status in apple fruit.

A Machine Learning-Based Anomaly Detection Method and Blockchain-Based Secure Protection Technology in Collaborative Food Supply Chain

The complexity of the collaborative food supply chain has resulted in the frequent occurrence of food safety incidents, which harm people's health and life. Therefore, the maintenance of food safety has become a key value. This study expects to solve the food safety problem and bring more benefits to people using intelligent systems. To meet the safety needs of the collaborative food supply chain, this study designed a food safety protection system architecture which collects the supply and sales data of various suppliers, as well as the data of equipment used in production. The architecture can carry out anomaly detections with machine learning to make a preliminary judgement on whether a problem has occurred in this batch of food during the transaction, and then implement in-depth anomaly detections with the supplier's equipment to determine the stage at which this problem occurred. The proposed system can help food operators achieve effective food monitoring, prediction, prevention, and improvement, thereby improving food safety.

Microbiological and Sensorial Quality of Beef Meat (Longissimus dorsi) Marinated with Cinnamon Extract and Stored at Various Temperatures

Meat spoilage caused by temperature abuse is a major problem for producers, retailers, and consumers that can generate large economic losses to industries. Microbial growth of Pseudomonas spp. is the main source of spoilage during storage. Cinnamon has antimicrobial properties that may potentially be used to reduce the spoilage caused by Pseudomonas. The objectives of this study were to determine the inhibitory effect of cinnamon extract (CE) against Pseudomonas aeruginosa (ATCC 27853) and evaluate the treatment of CE on meat quality during different storage temperatures (5 °C, 10 °C, 15 °C, and 25 °C). The anti-Pseudomonas result showed that 100% (w/v) CE concentration produced a 13.50 mm zone of inhibition in a disc diffusion assay. The minimum inhibitor concentration (MIC) of CE was noted at 25% (v/v), whereas the minimum bactericidal concentration (MBC) value was observed at 50% (v/v) concentration of CE. The time-kill showed the growth of P. aeruginosa decreased from 7.64 to 5.39 log CFU/mL at MIC concentration. Total phenolic content and IC₅₀ value of the cinnamon extract was expressed as 6.72 ± 0.87 mg GAE/g extract and 0.15 mg/mL, respectively. When the meat was marinated with 50% (v/v) CE and stored at various temperatures, the total viable count (TVC) and growth of Pseudomonas spp. were lowered as compared to the control sample. However, the reduction in microbial count in all samples was influenced by the storage temperature, where the lowered microbial count was noted in the sample treated with CE and stored at 5 and 10 °C for 48 h. The pH of meat treated with or without CE ranged from pH 5.74 to 6.48. The sensory attributes of colour, texture, and overall acceptability have a significant difference, except for odour, between marinated meat and control. The results indicate that the use of cinnamon extract as the marination agent for meat could reduce the growth of Pseudomonas spp. and therefore assist in extending the shelf life of meat at 5 and 10 °C storage temperatures.

Building “First Expire, First Out” models to predict food losses at retail due to cold chain disruption in the last mile

Current less-than-truckload (LTL) shipping practices allow for temperature abuse (TA) in the last segment (last mile) of the food supply chain. When this TA is combined with “First In, First Out” product rotation methods, it could lead to food spoilage and food waste; therefore, data-based decision models are needed to aid retail managers. An experiment was designed using pallets (4 layers/pallet × 5 boxes/layer) of commercially produced boneless chicken breast filet trays. The pallets were exposed to 24 h of simulated LTL TA (cyclic 2 h at 4°C, then 2 h at 23 ± 2°C). Filet temperatures were recorded for all 20 boxes using dataloggers with thermocouple wires. Additionally, microbiological sampling of filets [aerobic plate counts (APC) and psychrotrophic plate counts (PSY)] was conducted before (0 h of LTL TA) and after (24 h of LTL TA) the TA experiment for select boxes of the pallet and compared to control filets (maintained at 4°C). After TA, a shelf-life experiment was conducted by storing filets from predetermined boxes at 4°C until spoilage (7 log CFU/ml). Temperature and microbiological data were augmented using Monte Carlo simulations (MC) to build decision making models using two methods; (1) the risk of each box on the pallet reaching the bacterial “danger zone” (&gt;4°C) was determined; and (2) the risk-of-loss (shelf-life &lt; 4 days; minimum shelf-life required to prevent food waste) was determined. Temperature results indicated that boxes on the top and bottom layers reached 4°C faster than boxes comprising the middle layers while the perimeter boxes of each layer reached 4°C faster than centrally located boxes. Shelf-life results indicate simulated LTL TA reduced shelf-life by 2.25 and 1.5 days for APC and PSY, respectively. The first MC method showed the average risk of boxes reaching 4°C after 24 h of simulated LTL TA were 94.96%, 43.20%, 27.20%, and 75.12% for layers 1–4, respectively. The second MC method indicated that exposure at &gt;4°C for 8 h results in a risk-of-loss of 43.8%. The findings indicate that LTL TA decreases shelf-life of chicken breast filets in a heterogenous manner according to location of boxes on the pallet. Therefore, predictive models are needed to make objective decisions so that a “First Expire, First Out” method can be implemented to reduce food wastes due to TA during the last mile.

Cold chain relevance in the food safety of perishable products

The food cold chain is an effective tool that allows food markets to maintain food quality and reduce losses. Poor logistics may result in foodborne disease outbreaks and greenhouse gas emissions caused by organic matter decay. The ongoing pandemic of COVID-19 makes it necessary to study the chances of SARS-CoV-2 transmissions in food products. This study reviews cold chain logistics as a handy tool for avoiding food safety risks, including COVID-19. The cold chain of perishables and its proper management make it possible to maintain quality and safety at any stage of the food supply chain. The technology covers each link of the food chain to prevent microbial spoilage caused by temperature fluctuations and the contamination with SARS-CoV-2 associated with perishable foods. Given the lack of knowledge in this field in Latin America, the region needs new research to determine the impact of the cold chain on perishable foodstuffs. The perishable cold chain is only as strong as its weakest link, and the national and international markets require new traceability protocols to minimize the effect of COVID-19.

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.

E-grocery logistics: exploring the gap between research and practice

Purpose

This paper investigates the logistics management in the e-grocery sector. It contrasts the key issues faced by practitioners and the topics addressed in the academic literature, to identify potential misalignments between research and practice and propose avenues for future efforts.

Design/methodology/approach

This work adopts a twofold methodological approach. From an academic perspective, a systematic literature review (SLR) is performed to define the topics addressed so far by scholars when analysing e-grocery logistics. From a managerial perspective, a Delphi study is accomplished to identify the most significant issues faced by logistics practitioners in the e-grocery context and the associated significance.

Findings

The study develops a conceptual framework, identifying and mapping the 9 main logistics challenges for e-grocery along 4 clusters, in the light of a logistics-related revision of the SCOR model: distribution network design (area to be served, infrastructures), order fulfilment process (picking, order storage, consolidation, delivery), logistics-related choices from other domains (product range, stock-out management) and automation. These elements are discussed along three dimensions: criticalities, basic and advanced/automation-based solutions. Finally, the main gaps are identified – in terms of both under-investigated topics (order storage and stock-out management) and investigated topics needing further research (picking and automation) – and research questions and hypotheses are outlined.

Originality/value

This paper provides a threefold contribution, revolving around the developed framework. First, it investigates the state of the art about e-grocery logistics, classifying the addressed themes. Second, it explores the main issues e-grocery introduces for logistics practitioners. Third, it contrasts the two outcomes, identifying the misalignment between research and practice, and accordingly, proposing research directions.

Effects of various logistics packaging on the quality and microbial variation of bigeye tuna (Thunnus obesus)

Bigeye tuna (Thunnus obesus) is an economically valuable ocean fish species. It is susceptible to contamination during storage and transportation. Having proper transportation packaging and stable temperature during transportation are critical to prevent quality deterioration. However, the influence of packaging on retaining freshness in transit remains unknown. Here, the impact of different transportation packaging on the quality and microbiological variation of bigeye tuna during the logistics process was investigated by measuring physical-chemical indexes and microbial diversity. It turned out that aluminum foil paper (AFP) group had minimum temperature fluctuation, exhibited preferable water retaining capacity and color protection effect. AFP packaging could efficiently prevent TVB-N increase and microbial growth. After 40 h, the TVB-N value was 21.28 mg/100 g and microbial total plate count was 3.53 lg CFU/g, which was within the acceptable range. Temperature fluctuations and packaging materials had a major effect on the microbial community structure of bigeye tuna. Chitinophagaceae, Acinetobacter, and Knoellia were dominant in the AFP group, while Pseudomonas, Acinetobacter, and Macrococcus were dominant in the expanded polystyrene foam (EPSF) and European logistics (EUL) groups. AFP packaging could effectively slow down the growth and reproduction of Pseudomonas, restraining the growth of microorganisms and preserve the quality of bigeye tuna. This study provides insights into understanding the effects of packaging material on maintaining quality during logistics transportation.

Food Quality, Drug Safety, and Increasing Public Health Measures in Supply Chain Management

Over the last decade, there has been an increased interest in public health measures concerning food quality and drug safety in supply chains and logistics operations. Against this backdrop, this study systematically reviewed the extant literature to identify gaps in studying food quality and drug safety, the proposed solutions to these issues, and potential future research directions. This study utilized content analysis. The objectives of the review were to (1) identify the factors affecting food quality and possible solutions to improve results, (2) analyze the factors that affect drug safety and identify ways to mitigate them through proper management; and (3) establish integrated supply chains for food and drugs by implementing modern technologies, followed by one another to ensure a multi-layered cross-verification cascade and resource management at the different phases to ensure quality, safety, and sustainability for the benefit of public health. This review investigated and identified the most recent trends and technologies used for successfully integrated supply chains that can guarantee food quality and drug safety. Using appropriate keywords, 298 articles were identified, and 205 were shortlisted for the analysis. All analysis and conclusions are based on the available literature. The outcomes of this paper identify new research directions in public health and supply chain management.

Predictive models for the growth of Salmonella spp., Listeria spp., and Escherichia coli in lettuce harvested on Taiwanese farms

This study aimed at developing predictive models for Salmonella, Listeria, and E. coli in lettuce iceberg (Lactuca sativa) locally grown in Taiwan. The models were developed under constant temperature levels (5, 10, 15, 20, and 25°C) and validated under dynamic temperature conditions (18°C for 4 h, 7°C for 48 h, 23°C for 4 h). The result showed that (1) all strains were unable to grow at 5°C except for standard strain of Listeria obtained from the BCRC and (2) the growth rate of locally isolated strains of Salmonella and Listeria was higher than the standard one at certain temperature levels and lower than the growth rates of E. coli. The findings in this study enhance our understanding about the growth variability between Salmonella, Listeria, and E. coli strains on vegetables locally grown in Taiwan and may be used to improve the management of proper storage temperature in the lettuce supply chain in this country. Considering the temperature recommendation for refrigerated food in Taiwan, the findings in this study therefore recommend that fresh vegetables (e.g., lettuce) should be stored at 5°C or lower to prevent the rapid growth of these microorganisms. Finally, the developed models can be used in the assessment of the microbiological risk of Salmonella, Listeria, and E. coli contamination in lettuce locally grown in Taiwan. PRACTICAL APPLICATION: This study developed predictive models describing the growth of Salmonella, Listeria, and E. coli in lettuce locally grown in Taiwan. The models developed in this study can be used in quantitative microbial risk assessment.

Dual bacterial strains TTI for monitoring fish quality in food cold chain

Most microbial time-temperature indicators (TTIs) considered only one spoilage strain. This research compared single and dual spoilage strains-based microbial TTI for quality changes of chilled grouper fish (Epinephelus fuscoguttatus x E. lanceolatus) fillet products during distribution. The next-generation sequencing (NGS) and traditional plate count approach showed that Pseudomonas fragi and Vibrio parahaemolyticus were specific spoilage bacteria at 7 and 15°C. A dual-strain TTI response provides more accurate results than a single-strain TTI and provides an irreversible color change from yellow to reddish-brown, showing levels of fish freshness. The microbial TTI comprises fish spoilage bacteria strains with 3 log CFU/ml, a nutrient broth supplemented with 2% NaCl as a medium, and phenol red with 0.25 mg/ml as a pH indicator. Overall, this study points to the applicability of a dual-strain microbial TTI as a valuable tool for monitoring fish quality changes during cold chain break condition.

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.

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.

Numerical Simulation of Heat Transfer and Fluid Flow at Different Stacking Modes in a Refrigerated Room: Application of Pyramidal Stacking Modes

By means of the porous media theory, computational fluid dynamic models of heat transfer and fluid flow at different pack stacking modes in a refrigerated room are elaborated. A practical case is simulated, where brick-shaped packs with aquatic products, partially frozen to 261.15 K, are loaded in the room to complete the freezing process down to 255.15 K, followed by long-term frozen food storage at the latter standard temperature. The best freezing completion effect (defined as the maximum reduction of the highest product temperature during a certain residence time) is achieved by using the pyramidal stacking mode whose upper package is in the center of four lower packages (UPF-PSM) with two piles. The highest temperature of aquatic products at a two-pile-UPF-PSM can be reduced from 261.15 to 255.60 K for a residence time of 24 h. Within the same time, the product temperature becomes most uniform at a UPF-PSM. Simultaneously, the best uniformity of flow distribution and highest efficiency of air circulation in a refrigerated room are obtained by using the neat stacking mode (NSM) during the long-term frozen storage. Furthermore, a comprehensive stacking mode is proposed (using UPF-PSM for freezing completion and NSM for long-term frozen storage), which enhances both the freezing completion effect and the efficiency of air circulation in the studied refrigerated room.

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.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.