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This study aimed to evaluate the contamination levels of fresh products by ESBLs-producing Enterobacterales (ESBLs-E) or AmpC-producing Enterobacterales and characterize ESBLs genes. A total of 132 samples (67 vegetables and 65 fruits) were collected from markets in Tebessa, eastern Algeria. Among the samples, 16 third-generation cephalosporin-resistant Enterobacterales isolates were identified with a prevalence of 19.40% in vegetable samples, while there was no positive finding in fruit samples. Isolates showed resistance to most β-lactams, and all of them displayed multidrug resistance. Phenotypic tests for ESBLs detection, using double-disk synergy test and double-disk test were positive for 14 strains, including Klebsiella pneumoniae (n = 5), Klebsiella oxytoca (n = 4), Klebsiella terrigena (n = 2), Kluyvera spp. (n = 2), and Enterobacter cloacae (n = 1). Two AmpC-producing strains (Citrobacter freundii and E. cloacae) were identified through the AmpC disk test. Contamination rates of vegetables by ESBLs-E and AmpC-producing Enterobacterales were 19.40% and 2.98%, respectively. PCR results showed the presence of at least one ESBL gene in seven selected strains, with the dominance of blaCTX-M gene. Notably, K. pneumoniae strains showed the co-occurrence of two or three genes. Sequencing identified uncommon variants of ESBLs genes for the first time in Algeria, including blaCTX-M-79 (2/7), blaCTX-M-107 (2/7), blaCTX-M-117 (2/7), blaTEM-112 (1/7), blaTEM-125 (2/7), blaTEM-194 (1/7), and blaSHV-176 (3/7). [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products. Design/methodology/approach: This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit. Findings: Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit. Originality/value: This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain. [ABSTRACT FROM AUTHOR]

By constructing the consumers’ utility function, this article calculates the optimal order quantity and optimal expected profit of a fresh retailer under the centralized and decentralized inventory strategy in an omni-channel supply chain. In addition, the impact of consumers’ channel preference, consumers’ demand transfer behavior, and the "buy-online-pickup-instore" (BOPS) service on retailers’ inventory strategy are analyzed. The results show that opening a BOPS service is not always beneficial to retailers. However, it is always beneficial for retailers to eliminate the differences between channels. Under the centralized inventory strategy, the optimal order quantity and expected profit are only affected by the freshness-keeping cost, while under the decentralized inventory strategy, they are affected by various consumer behaviors. A reasonable inventory management strategy should be adopted according to the product type, inventory cost, consumer preferences, and order quantity. [ABSTRACT FROM AUTHOR]

The lifecycle of fresh produce involves a sequence of biochemical events during their ontology, and these events are particularly significant for climacteric fruits. A high demand during ripening is observed in these plant products, which is reflected in a high rate of respiration and ethylene production. Increased respiratory demand triggers the activation of secondary pathways such as alternate oxidase, which do not experience critical increases in energy consumption in non-climacteric fruit. In addition, biochemical events produced by external factors lead to compensatory responses in fresh produce to counteract the oxidative stress caused by the former. The dynamics of these responses are accompanied by signaling, where reactive oxygen species play a pivotal role in fresh product cell perception. This review aims to describe the protection mechanisms of fresh produce against environmental challenges and how controlled doses of abiotic stressors can be used to improve quality and prolong their shelf-life through the interaction of stress and defense mechanisms.

Temperature prediction is important for controlling the environment in the preservation of fresh products. The phase change materials for cold storage make the heat transfer process complex, and the use of physical models for characterization and temperature prediction can be challenging. In order to predict the variation of the thermal environment in a temperature-controlled container with a cold energy storage system, we propose an LSTM model based on historical temperature data in which the trends of temperature variations of the fresh-keeping area, the phase change material (PCM), and the fresh products can be predicted immediately without considering the complex heat transfer process. An experimental platform of a temperature-controlled container with a cold energy storage system is built to obtain the experimental data for the prediction model's construction and validation. The prediction results based on the LSTM model are compared to the results of a physical model. In order to optimize the input data for better prediction performance, the proportion of input samples from the dataset is set to 80%, 50%, 20%, and 10%. The prediction results from different input groups are compared and analyzed. The results show that the LSTM model is able to accurately predict temperature variations of the fresh-keeping area and products, and the predicted values are in agreement with the actual values. The LSTM-based prediction model has a higher accuracy compared to the physical-based prediction model; the RMSE, MAE, and MAPE are 0.105, 0.103, and 0.010, respectively, and the relative error for the prediction of effective control hours of environmental temperature is 0.92%. It is suggested to use the initial 20% of the historical temperature data as the input to predict the future temperature variation for better prediction performance. The results of this paper offer valuable insights for accurate temperature prediction in the fresh-keeping environment with a cold energy storage system. [ABSTRACT FROM AUTHOR]

Fresh agricultural products (FAPs) require special handling to maintain product quality, especially during socio-economic crises. In developing countries, there is a compelling need for a robust supply chain governance strategy to regulate the supply chain actors involved in the logistics and distribution system. This paper proposes an innovative way to design FAP logistics governance to address specific problems in the FAP logistics networks of developing countries, where FAP supply chains are not well integrated. Three logistics governance scenarios were modelled using discrete-event simulation. The results revealed that the fully distributed governance could effectively reduce process delay and consequently minimise logistics costs, especially within the logistics networks serving multiple markets. This is due to that fact that the distributed governance allows the actors involved in the network to have some autonomy to manage their resources. The paper contributes to improving the fundamental understanding of the governance for FAP logistics networks consisting of small-holder farms. Our work also offers valuable insights for policymakers and practitioners into how to improve FAP supply chain management. [ABSTRACT FROM AUTHOR]

The deterioration of fresh products is often severe during fresh product delivery in urban areas, which is a significant challenge. We propose a novel model for the urban delivery of fresh products with different perishability. The objective of this model is to minimise the total deterioration value (TDV) and distance travelled. The TDV is related to the perishability, deterioration rates and quantities of different fresh products. A customised genetic algorithm is proposed to solve the model. The fitness value is calculated according to the preferential delivery of fresh products with high deterioration rates. Finally, a case study of fresh product delivery for a company in Shanghai is explored to assess the effectiveness of the proposed model. The results show that the proposed approach can lead to the highest drop of 11.8% of TDV compared to that based on the traditional shortest-path model. [ABSTRACT FROM AUTHOR]

With the continuous improvement of people's income level and consumption level, the demand for fresh products is driven by the strong demand, and at the same time, low-carbon and green development puts forward new requirements for the cold chain logistics industry. Starting from the perspective of considering the green cost, by constructing a distribution path optimization model of fresh cold chain products considering green cost with the optimal total distribution cost as the research objective, energy saving and emission reduction are integrated into the path optimization problem so that we can explore how to protect the environment while realizing the benefits of the enterprise, and the model is solved by using the ant colony algorithm. By observing the cold chain logistics distribution path arrangement before and after optimization, it is found that the fresh cold chain product distribution path optimization considering green cost can effectively reduce the transportation cost, refrigeration cost, carbon emission cost, and cargo damage cost in the distribution process. Under the optimal distribution strategy, the total cost is reduced by about 16.6% compared to the original route, and the environmental cost is reduced while reducing the distribution cost. It shows that this strategy can improve transportation efficiency and customer satisfaction while saving resources and protecting the environment. And this study comprehensively considers the actual operation of logistics enterprises, so this study has a certain significance of reference value for the green transformation of enterprises. It further promotes the sustainable development of the cold chain industry and reduces the distribution costs of cold chain logistics companies. It also provides a certain degree of inspiration and reference for other cold chain logistics companies to realize the unification of economic and environmental benefits in actual operation. [ABSTRACT FROM AUTHOR]

The cap-and-offset regulation is a practical scheme to lessen carbon emissions. The retailer selling fresh products can adopt sustainable technologies to lessen greenhouse gas emissions. We aim to analyze the optimal joint strategies on order quantity and sustainable technology investment when the retailer faces stochastic market demand and can only acquire the mean and variance of distribution information. We construct a distributionally robust optimization model and use the Karush-Kuhn-Tucker (KKT) conditions to solve the analytic formula of optimal solutions. By comparing the models with and without investing in sustainable technologies, we examine the effect of sustainable technologies on the operational management decisions of the retailer. Finally, some computational examples are applied to analyze the impact of critical factors on operational strategies, and some managerial insights are given based on the analysis results. [ABSTRACT FROM AUTHOR]

The metabolic process and physiological activities of fresh fruit and vegetables continue after harvesting and lead to quality reduction and waste enhancement. Modified atmosphere packaging (MAP) is a well-proven method to avoid anaerobiosis activities and extend the shelf life of agricultural products. As a result, researchers have proposed MAP technology as an appropriate and cost-effective method. The design of MAP systems depends on various factors, including the respiration and transpiration rates of the product, the temperature and humidity of the environment, and the permeability rate of the package. Therefore, the integration of mentioned information in the design of a successful MAP system is crucial. The modeling approach is a reliable method for understanding the incorporation of the effective parameters and achieving the mass balance equation to determine the optimal conditions of the MAP. This review investigates the developed models for achieving a MAP design for various agricultural products. Also, the existing challenges in the developed models are discussed. In addition, the strengths and shortcomings of the integrated models are described, and the prospects for the optimization of MAP are highlighted. Considering the interaction of effectual parameters and sub-models, mathematical modeling revealed acceptable accuracy to predict the shelf life of fresh produce under the actual supply chain conditions and MAP systems. Due to the difference in the optimum MAP requirements based on specific parameters such as gas concentration, research is needed to optimize the effective factors by taking into account the perm-selectivity of the packaging materials. More efforts should focus on developing the comprehensive mathematical model and simulation, including product characteristics, package properties, environmental conditions, and mass and heat transfer phenomena for optimal MAP design. • Integrated modeling to develop modified atmosphere packaging (MAP) was investigated. • Mathematical models have been used to describe physicochemical quality of fresh fruit and vegetables under MAP process. • Respiratory energy and heat transfer play a key role inside the package. • The existing limitations and trends of modeling to design MAP systems were discussed. • Recent developments in mathematical modelling have the potential of optimization MAP systems. [ABSTRACT FROM AUTHOR]

Temperature prediction is important for controlling the environment in the preservation of fresh products. The phase change materials for cold storage make the heat transfer process complex, and the use of physical models for characterization and temperature prediction can be challenging. In order to predict the variation of the thermal environment in a temperature-controlled container with a cold energy storage system, we propose an LSTM model based on historical temperature data in which the trends of temperature variations of the fresh-keeping area, the phase change material (PCM), and the fresh products can be predicted immediately without considering the complex heat transfer process. An experimental platform of a temperature-controlled container with a cold energy storage system is built to obtain the experimental data for the prediction model’s construction and validation. The prediction results based on the LSTM model are compared to the results of a physical model. In order to optimize the input data for better prediction performance, the proportion of input samples from the dataset is set to 80%, 50%, 20%, and 10%. The prediction results from different input groups are compared and analyzed. The results show that the LSTM model is able to accurately predict temperature variations of the fresh-keeping area and products, and the predicted values are in agreement with the actual values. The LSTM-based prediction model has a higher accuracy compared to the physical-based prediction model; the RMSE, MAE, and MAPE are 0.105, 0.103, and 0.010, respectively, and the relative error for the prediction of effective control hours of environmental temperature is 0.92%. It is suggested to use the initial 20% of the historical temperature data as the input to predict the future temperature variation for better prediction performance. The results of this paper offer valuable insights for accurate temperature prediction in the fresh-keeping environment with a cold energy storage system.

The retail sector supplies the daily fresh products and increasingly plays a key role in the stability and livability of cities. However, public health events such as COVID-19 have caused frequent product shortages in recent years. The risk of fresh product shortages not only causes retailers to lose profits, but also affects the normal life of residents. In this paper, we address the problem of designing a resilient retail supply network for fresh products under the supply disruption risks and propose a bi-objective mixed-integer programming model. This model can help retailers to select suppliers, distribution centers and transportation routes under different scenarios and implement three resilience strategies, namely, priority supply, multiple sourcing and lateral transshipment. We use the ε -constraint method to transform the multi-objective problem into a single objective model and develop a Lagrangian relaxation algorithm to solve the different scale instances. This model is solved for a real-life case of a supermarket to obtain managerial insights. In the case study, this paper shows the set of Pareto fronts with different inventory periods, maximum allowed decay and decay rate. We calculate the expected total cost under targeted disruption scenarios and evaluate the effectiveness of these resilience strategies when implemented concurrently or separately. Our results identify the most critical suppliers and distribution centers that should be fortified. We elaborate that more resilience strategies are not always better and managers need to take appropriate resilience strategies according to their own problems.

With the continuous improvement of people’s income level and consumption level, the demand for fresh products is driven by the strong demand, and at the same time, low-carbon and green development puts forward new requirements for the cold chain logistics industry. Starting from the perspective of considering the green cost, by constructing a distribution path optimization model of fresh cold chain products considering green cost with the optimal total distribution cost as the research objective, energy saving and emission reduction are integrated into the path optimization problem so that we can explore how to protect the environment while realizing the benefits of the enterprise, and the model is solved by using the ant colony algorithm. By observing the cold chain logistics distribution path arrangement before and after optimization, it is found that the fresh cold chain product distribution path optimization considering green cost can effectively reduce the transportation cost, refrigeration cost, carbon emission cost, and cargo damage cost in the distribution process. Under the optimal distribution strategy, the total cost is reduced by about 16.6% compared to the original route, and the environmental cost is reduced while reducing the distribution cost. It shows that this strategy can improve transportation efficiency and customer satisfaction while saving resources and protecting the environment. And this study comprehensively considers the actual operation of logistics enterprises, so this study has a certain significance of reference value for the green transformation of enterprises. It further promotes the sustainable development of the cold chain industry and reduces the distribution costs of cold chain logistics companies. It also provides a certain degree of inspiration and reference for other cold chain logistics companies to realize the unification of economic and environmental benefits in actual operation.

With the gradual development of a capital city subcenter, numerous residents gradually move to the subcenter, increasing the demand for fresh food. Thus, an adequate supply of fresh food to the subcenter should be guaranteed, while reducing the logistics costs, maintaining the quality and quantity, complying with sustainable development, and cooperating with the city to complete the food basket logistics and distribution. Accordingly, we propose the optimization of logistics distribution paths for fresh products. After field research, we found that the main fresh product distribution point in the Beijing subcenter is the Baliqiao wholesale market, one of Beijing's vegetable basket projects, and the main distribution targets are large- and medium-sized markets in the subcenter. We optimize a fresh food distribution path model using minimum total cost and carbon emissions as the objective function according to the layout of the subcenter, and the best path is determined using improved ant colony optimization. The optimization results provide a basis for fresh food distribution in the capital city subcenter. We use scientific methods to analyze the travel routes of vehicles transporting fresh products in the subcenter to obtain the best distribution path for logistics companies, aiming to reduce the waste of resources and pollution to the environment by reducing the overall distribution costs and carbon emissions. Therefore, this type of study can benefit logistics companies and the subcenter population while contributing to sustainable development. [ABSTRACT FROM AUTHOR]

Introduction. Canning requires thermophysical calculations for thermal or cold processing. These calculations are based on thermophysical characteristics of raw materials. The research objective was to analyze the thermophysical characteristics of tropical fruits. Study objects and methods. The study featured kiwi, papaya, avocado, and figs. Their thermal conductivity was analyzed with the help of stationary fiber plate method. The cryoscopic temperature was determined from the flat area of the thermogram obtained during freezing. The heat capacity and the amount of frozen moisture were determined by calculation based on the available data on the chemical composition of the fruits. The density was defined by using the hydrostatic weighing method, the sugar content – by the refractometric method, the moisture content – by drying to constant mass. Results and discussion. The research revealed the physicochemical parameters of kiwi, avocado, papaya, and figs. Papaya demonstrated the highest moisture content – 86.32 ± 0.02%, while kiwi appeared to have the highest density – 1,065 ± 1 kg/m3. Figs had the highest sugar content – 16.0 ± 0.1%. The thermal conductivity coefficient of fresh and frozen tropical fruits was determined experimentally and increased after freezing. Frozen avocado showed only a slight increase in thermal conductivity coefficient because of its low moisture content while frozen figs demonstrated a significant increase in the thermal conductivity coefficient – by 3.3 times. This product possessed the highest thermal conductivity: 0.63 ± 0.02 W/(m·K) for the fresh samples and 2.06 ± 0.02 W/(m·K) for the frozen samples. The thermal conductivity coefficient of kiwi and papaya increased by 2 and 4.2 times, respectively. The experiment also examined the effect of protective plastic wrap and ripeness on the thermal conductivity coefficient. The film proved to have a negative effect on the reliability of thermophysical analysis. The cryoscopic temperature was determined empirically. Such thermophysical properties as heat capacity, thermal diffusivity, and frozen moisture were based on the available chemical composition. Conclusion. The research revealed the physicochemical parameters of kiwi, avocado, papaya, and figs. It included a set of experiments on the thermal conductivity coefficient of fresh and frozen tropical fruits. The obtained values can be used to develop the optimal parameters of thermal processing, refrigeration, and thermal treatment of new products. They can also be useful for fortifying dairy and bakery products with exotic fruits.

With the level of science and technology, the logistics industry is paying more and more attention to sustainable development. However, the current cold chain logistics industry generally has problems such as backward refrigeration technology, irrational distribution management, and outdated equipment, which make its energy consumption serious and cause severe carbon emission effects on the environment. Optimizing the carbon emission structure of the cold chain logistics industry is the top priority for ensuring the sustainable development of the logistics industry. The circulation of fresh products is mainly through cold chain logistics. However, there are some problems of excessively high cost of the cold chain and easy disconnection during the cold transportation process in Chinese cold chain logistics, which severely restricts the development of Chinese cold chain logistics. Because of the perishability and strong timeliness of distribution of fresh products, inventory costs, penalties, and damage costs need to be considered. Because fresh products should utilize refrigerated transportation, it is also necessary to consider refrigerated costs during transportation and cold storage cost in distribution centers. Based on the goal of the lowest total cost, this paper constructs an integer programming model and analyzes cases. By comparing the simulation results of the genetic algorithm and the hybrid algorithm, it is concluded that the hybrid algorithm is more effective.

Due to extensive harvesting, the ecological environment of Dendrobium officinale has been destroyed, and there are very few wild D. officinale today. As the tissue culture technology of D. officinale continues to mature, there are more and more imitation, wild cultivation, greenhouse cultivation and other large-scale production of D. officinale to meet the market demand, but the storage and preservation of fresh products is a big problem. The existing problems in storage of D. officinale and its preservation technology are discussed, so as to provide some reference for the storage and preservation of D. officinale fresh products. [ABSTRACT FROM AUTHOR]

Strict control taken in response to public health emergencies (PHEs) can hinder fresh products supply, while stock-outs and the risk of epidemics can increase fresh products demand. To explore the interrelationship between outbreak control measures and the availability of fresh products, this paper establishes a dynamic synergistic model of epidemic transmission subsystem and fresh products supply-consumption subsystem based on system dynamics (SD). Taking the spread of COVID-19 in Shanghai from March to May 2022 as the actual background, the impact of different storage strategies and control measures on epidemic prevention and material supply was quantitatively evaluated. The results show that risk transmission factor (RTF) affects the number of infections and the intensity of community control contributes to controlling the spread of the epidemic, but it can continue to affect fresh product supply security. Therefore, increasing fresh product protection inputs and reducing in-transit time can reduce fresh product losses and improve fresh product supply security under PHEs. The findings can enable fresh products to effectively match supply and demand under PHEs, mitigate the impact of epidemic risks and provide decision support for relevant retailers.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objectives] To compare anti-inflammatory and analgesic effects of fresh and dry products of Zhuang medicine Asystaia gangetica on mice. [Methods] Experiments were performed using glacial acetic acid to increase capillary permeability in mice, cotton ball to cause granulomatous formation in mice, xylene to cause auricular swelling in mice, chronic inflammation, and acetic acid-induced pain and hot-plate pain. Besides, the anti-inflammatory and analgesic effects of fresh and dry products of A. gangetica on mice were observed with the indicators of capillary permeability in the abdominal cavity, swelling rate of auricles, cotton ball granulomas and writhing times, and thermal pain threshold. [Results] Fresh products of A. gangetica had significant inhibitory effects on peritoneal capillary permeability increase in mice induced by glacial acetic acid, mouse auricle swelling induced by xylene, and mouse granuloma formation induced by cotton balls. Dry products of A. gangetica also significantly inhibited the auricle swelling in mice induced by xylene, but the effect intensity was weaker than that of fresh products. In addition, dry products of A. gangetica showed a certain tendency to inhibit the increase of capillary permeability in the abdominal cavity of mice induced by glacial acetic acid. In the analgesic experiment, both fresh and dry products of A. gangetica showed better analgesic effect, but caused pain on the hot plate, and the analgesic effect time of fresh products was shorter than that of dry products. [Conclusions] Both fresh and dry products of Zhuang medicine A. gangetica have certain anti-inflammatory and analgesic effects. [ABSTRACT FROM AUTHOR]

The way in which food reaches consumers is a high profile component of the food chain's Greenhouse Gas (GHGs) emissions, but is changing rapidly as technology facilitates online and new targeted logistic solutions which deliver directly to the consumer's home, workplace or other convenient locations. The challenge is how can new, more fragmented supply chains be developed without increasing GHGs emissions. More broadly speaking, digitalisation is transforming how all food logistics functions. This allows consumers to connect more directly with both farmers and food producers, in Short Food Chains (SFCs), which help the former to understand more about the source of their food and how it was produced. This paper aims to analyse the current SFCs' challenges, with particular attention paid to fresh products, taking into account the evolution of consumers and market trends as well as the transformation of logistics. The analysis is based on evidence and examples from across Europe. New direct delivery food logistics models could help consumers access supplies of fresh products more easily, improve consumer health and reduce the high waste levels and carbon emissions, which represent key challenges for many European fresh product supply chains. Food suppliers would also benefit by securing more of the final consumer value of the food they produce. [ABSTRACT FROM AUTHOR]

The transport of sensitive products is very important because their deterioration may cause the value lost and even the product rejection by the buyer. In addition, it is important to choose the optimal way to achieve this end. In a data network, the task of calculating the best routes is performed by routers. We can consider the optimal path as the one that provides a shortest route. However, if a real transport network is considered the shortest path can sometimes be affected by incidents and traffic jams that would make it inadvisable. On the other hand, when we need to come back, due to features that symmetry provides, it would be interesting to follow the same path in reverse sense. For this reason, in this paper we present a symmetric routing mechanism for cooperative monitoring system for the delivery of fresh products. The systems is based on a combination of fixed nodes and a mobile node that stores the path followed to be able of coming back following the same route in reverse sense. If this path is no longer available, the system will try to maintain the symmetry principle searching the route that provide the shortest time to the used in the initial trip. The paper shows the algorithm used by the systems to calculate the symmetric routes. Finally, the system is tested in a real scenario which combines different kind of roads. As the results shows, the energy consumption of this kind of nodes is highly influenced by the activity of sensors. [ABSTRACT FROM AUTHOR]

The impact of a coating solution containing chitosan, lignocellulosic nanofibers (LCNF), and raspberry leaf extract on strawberry quality was assessed. Strawberries were coated, inoculated with Aspergillus flavus (a major aflatoxin producer), and stored at 5 ± 1 °C for 16 days. An automated fruit quality evaluation system was developed, and daily images were taken for analysis. ImageJ software was used to evaluate colour, size loss, and fungal damage. Coating solution significantly reduced fruit size loss, with coated strawberries showing 18.4 ± 12.8% size loss after 16 days, compared to 30.2 ± 8.4% in the control group and 25 ± 10.7% in uncoated strawberries. Regarding colour, lower colour changes were observed in coated fruit compared to the control during storage (p ≤ 0.05). Fungal damage did not significantly differ between treatments during storage (p > 0.05). Furthermore, the study demonstrated that image analysis and visual estimation methods for assessing fruit fungal damage yielded similar results, with a strong correlation (R2 = 0.87). This suggests that the proposed image analysis methodology is effective for the assessment of fungal damage in strawberries compared to visual estimation. Chitosan-LCNF-raspberry extract coating solution shows promise in enhancing strawberry quality by reducing size loss during storage and managing fungal development. [Display omitted] • Coating effect was evaluated on strawberries inoculated with Aspergillus flavus. • Image analysis assessed fungal damage, size loss and colour. • Kinetic models were created to assess fruit quality and safety. • Chitosan-LCNF-raspberry extract coating extends strawberry shelf-life. • Coated strawberries showed lower size loss and similar fungal damage. [ABSTRACT FROM AUTHOR]

With the gradual improvement of living standards, people's consumption levels and habits are changing. One notable fact is that the demand for fresh products is growing steadily. Accordingly, fresh-product preservation and logistics distribution also require higher standards. Based on the practice of fresh domestic transport and preservation, for which the producer and the distributor are responsible, this paper discusses their optimal decisions taking into account the freshness-keeping effort of the distributor. Our main contributions include the derivations of the optimal decisions of the order quantity and the freshness-keeping effort in both the pull and push models, which are common in practice but have not been studied in the literature. Our analytical models lead to the result that, all other settings being the same, the distributor always puts a greater effort into preserving the product quality in the pull model than in the push model. This phenomenon results in a greater distributor's order quantity and producer's shipping quantity in the pull model. We also conduct a comprehensive numerical comparison of the effects of different modulating factors, including the price and the proportion and variation of surviving quantity, in these two settings. We find that the profits of the participants and the supply chain are always larger in the pull model, which indicates that the pull model is a better choice for the supply chain. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Changzhou University (Social Science Edition) is the property of Editorial Department of Journal of Changzhou University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Philippine public market sells unrefrigerated imported frozen meat products that may pose a public health concern. This study assessed the quality of thawed and fresh pork using aerobic plate count (APC), pH and sensory parameters (color, odor and presence of slime). Fifteen samples for each type of pork were collected in a public market at Calamba, Laguna, and divided into testing schedules and minced. Serial dilution, pour plating, incubation and APC (cfu/ g) were done; pH was determined; color and odor were ranked; and presence of slime was noted. Significant findings include higher APC for fresh pork, remarkably at 5h; higher increase in pH, mean color rank and mean odor rank for thawed pork; and higher peak percent slime positive for thawed pork. These findings suggest that fresh pork sold in Philippine market settings has a better keeping quality than thawed pork, and that thawed pork showed earlier signs of spoilage. [ABSTRACT FROM AUTHOR]

The present study addresses store-switching behavior regarding shopping for fresh produce. In particular, the study highlights the role played by a consumer way of life associated with fresh food consumption and explains how it has a mediating effect on consumer intention to switch food stores. A sample consisting of 252 respondents was obtained from the Israeli Arab community. The empirical results of the study indicate that way of life factors have a mediating effect on the influence of store outputs on consumer emotions and behavior intention, thus low outputs for fresh produce will lead to store-switching. However, the findings reveal that dissatisfied consumers may not consider switching due to switching barriers which moderate the influence of perceived dissatisfaction on store-switching. Implications for food retail providers are identified, together with a discussion of the study’s limitations and avenues for future research. [ABSTRACT FROM AUTHOR]

This paper studies the location–allocation problem of distribution centers (DCs) for fresh products, in which it requires to determine the number and locations of DCs and design the allocation scheme for fresh products. The main challenge in optimizing this problem is to handle the uncertain demands with only partially known probability distribution information. To address this challenge, this paper first characterizes the ambiguous probability distributions of fresh products demands by an ambiguity set. Then, from different perspectives, this paper proposes two distributionally robust (DR) location–allocation models to find the optimal decisions that perform best in view of the worst-case distribution within the ambiguity set. The first model is with ambiguous expectation, which is transformed into a mixed-integer linear programming (MILP) model via employing the linear decision rule technique. The second model is with ambiguous chance constraints. This model is a semi-infinite chance constraint programming problem, which is transformed into a mixed-integer second-order cone programming (MISOCP) model by deriving safe approximations of the ambiguous chance constraints. To verify the feasibility and effectiveness of the proposed models, a case study about Hema fresh chain stores in Beijing is addressed. The experimental results demonstrate that the optimal decision provided by the proposed optimization methods can immunize against ambiguous probability distributions of demands with a small cost price. • Two DR location–allocation models of DCs for fresh products are raised. • The distribution information of uncertain demands is only partially known. • The DR model with ambiguous expectation is transformed into an MILP model. • The DR model with ambiguous chance constraints is transformed into an MISOCP model. • A case study shows the feasibility and effectiveness of the proposed DR models. [ABSTRACT FROM AUTHOR]

[Display omitted] • Cold plasma is a green technology to improve the quality of vegetables and fruits. • Bubbles enhance the mass transfer of reactive species from gas to liquid phases. • Analytical techniques for PAW and properties of reactive species are highlighted. • Latest progress in PAW technology for the study of food and agriculture is provided. Climate change increases the need for effective and sustainable technologies in food and agriculture. Plasma-activated water (PAW) emerges as a green and sustainable technology in food processing and production. Synergy of a myriad of reactive oxygen and nitrogen species (RNOS) in PAW contributes to desirable properties of PAW. Compared to conventional methods, PAW is fast and effective for various products, not limited by the volume or shape of the treated samples. In this review, we will first introduce the fundamentals on plasma generation, physicochemical properties and characterization of PAW. Among various approaches for activation improvement, we highlight a recent progress in improving the cold plasma activation by microbubbles. Then we critically review the treatment conditions by PAW, and effectiveness of PAW for bio-film removal, food processing, plant growth in agriculture, and environment. As the research output on PAW is expanding exponentially, this review focuses on the work published within the last two years (2020–2021) to summarize the current understanding of the principles of the effects from PAW and to reflect potentials of PAW in applications for the food science and technology. [ABSTRACT FROM AUTHOR]