A risk analysis program can perform a sensitivity analysis to identify the points in the supply chain where remedial action will be most effective. Hazard Analysis Critical Control Point (HACCP) is an internationally recognized, scientific, efficient, simple and reasonable food safety management system. Through scientific and systematic methods, it can analyse and find out the risk of food production process, determine specific preventive and control measures and critical control points, and implement effective monitoring, so as to ensure the safety and hygienic quality of products. Therefore, in order to determine the demand for ICC and refer to the summer and winter temperatures, oyster supply chain research was conducted in Rizhao and Yantai, Shandong Province, China in August 2020 and December 2021, respectively. Based on literature analysis, field tracking and interview investigation, each oyster delivery was mapped, providing insights into how oyster supply chains are structured (Fig. 1) and summarized HACCP and Quality Analysis and Critical Control Point (QACCP) for handling live oysters (Table 1).

Before leaving the factory, oysters usually need to go through processes such as ultrasonic cleaning (Hasan et al., 2023), activated carbon filtration (Vijuksungsith et al., 2023), heavy metal absorption (Emenike et al., 2022), seawater purification, ozone sterilization (Powell & Scolding, 2018), protein removal, and ultraviolet sterilization (Li, Liu, et al., 2023) to make them safe for consumption. However, the oyster supply chain has a very complex multi-disciplinary, multi-link and multi-step system composition, which increases the risk of oysters being infected by bacteria. Tian et al. stated that the commonly used methods of storing and distributing oyster products, either by storing oysters in a cold and humid environment or by storing shucked oyster meat in refrigerated water both methods, do not reduce the presence of pathogens in raw oyster meat (Tian & Liu, 2023). Therefore, it is necessary to add additional sterilization measures. As can be seen in Fig. 1 and Table 1, oysters usually arrive at retailers three days after they are harvested, while the supply chain that wholesalers are involved in takes longer, about 3.5 to 5 days after receipt. The fastest delivery times are to local customers who receive their oysters approximately 24 h after harvest. During this period, current methods for regulating oyster transportation mostly focus on temperature and time control. However, the temperature control method is relatively rough, basically relying on the staff's experience to add ice, so it is difficult to achieve accurate temperature control for different transportation times. And even if the refrigerated truck with better temperature control effect is used, there are problems of large temperature fluctuation and uneven temperature distribution in the space. For the medium and long distance transportation process, there are higher requirements for the sealing of packaging, and the widely used non-unitized packaging cannot prevent the oysters from contacting with the external environment, which ultimately leads to contamination and quality degradation of the oysters. Judith et al. indicated that the growth of microorganisms such as Vibrio parahaemolyticus is influenced by temperature during transportation and processing of oysters after harvest, which can lead to bacterial colonization to potentially dangerous levels if oysters are not refrigerated in a timely manner (Fernandez-Piquer et al., 2011), yet there are instances in some supply chains where oysters have been exposed to temperatures higher than the recommended 10°C (Fernandez-Piquer et al., 2011). In summary, cold chain management in the oyster industry is necessary to ensure product quality and safety throughout the supply chain.

Live edible oysters weighing 90–100 g is about 30 yuan (about $4.15) each in Beijing, China, while oysters that cannot be eaten raw are about 5 yuan (about $0.69) each. Therefore, in order to ensure the quality and safety of oysters and to obtain higher economic benefits, it is essential to keep the transport time as short as possible and to take measures to slow down the decay of the oysters. Generally, temperature, hygiene and delivery times during oyster distribution are critical control points throughout the oyster supply chain, which definitely are the demands that ICC needs to meet.

Figure 2 shows how the ICC achieves the functions of gas microenvironment monitoring, temperature control and sterilization functions during transport by means of temperature numerical simulation techniques, the multi-sensor monitoring network and plasma generators.

Firstly, the quality of the cooling depends heavily on the packaging which directly affects the heat transfer rate and cold source energy utilization of the food in the container (Defraeye et al., 2015). Therefore, the temperature field in the container is analysed by numerical simulation method, and the evaluation methods are proposed, so as to select the best package cooling scheme.

Secondly, integrate multiple sensors in the container. In scenarios such as warehousing, retail, and transportation, real-time monitoring of key environmental parameters enables staff to detect and adjust environmental abnormal values in time, increasing the traceability and food safety of the oyster supply chain. The key environmental parameters include ambient temperature (Love et al., 2019) and humidity, O₂ (Maguire et al., 1999) and CO₂ levels related to oyster and microbial respiration (Pavase et al., 2018; Yamamoto et al., 2022), and NH₃ and H₂S levels related to biological spoilage(Preethichandra et al., 2023; Yamamoto et al., 2022). During transportation of oysters, proteins are hydrolysed to produce a variety of simple compounds, and microorganisms also break down proteins to produce into free amino acids and further reactions such as deamination, decarboxylation, and desulfurization to produce gases such as NH₃, CO₂, and H₂S (Pavase et al., 2018). Among these compounds, the production of CO₂ is usually associated with increased microbial growth in meat and meat products (Gram and Huss, 1996), and food spoilage due to higher levels of volatile alkaline nitrogen represented by NH₃ is considered as one of the indicators of microbial food spoilage in the food industry (Ma et al., 2018). Whereas, specific spoilage organisms (SSOs) produce off-taste and metabolites including H₂S and dominate the microbiota of seafood (Odeyemi et al., 2018; Parlapani et al., 2014).

Thirdly, integrate sterilization equipment in the container. Sterilizing oysters and the environment can delay the spoilage of oysters (Djemaa, 2020). The current measures to reduce spoilage bacteria are mainly concentrated in the pretreatment stage, such as sterilization with plasma activated water, chlorine and chloride sterilization, etc, there are few ways to sterilize live oysters without affecting their viability. Therefore, this experiment set up a miniature plasma sterilization module inside the smart container. Its operation is based on the principle of corona discharge, which causes electrical breakdown of air and generates high-energy electrons and ions by increasing the input voltage to 4.8 ± 0.5 kV, destroying the microbial structure, thus sterilizing the oyster and delaying the decline of its quality.

The transportation container system consists of two parts: one is the container unit's exterior packaging, such as the appearance of the container and the placement of refrigerants, and the other is the development of an information technology-based monitoring equipment system integrated within the container. The design of the transportation container system includes the overall architecture design, the information architecture design, and the software and hardware implementation of the system., like Fig. 3.

Figure 4 shows the system architecture, which includes five parts:

As the most basic layer, the data acquisition layer is designed to get the data and perform the task. This layer obtains the storage and transportation demand of oyster supply chain, including oyster weight and external ambient temperature for numerical simulation of temperature field, integrates a variety of sensors to obtain microenvironment parameters, and receives the upper level command to perform the operation of plasma equipment.

The data transport layer provides an integrated and reliable data access service. After data acquisition, data is transmitted to the mobile phone through the wireless serial port and uploaded to the cloud through the MQTT (Message Queuing Telemetry Transport) which is mainly responsible for connection access, connection management, data forwarding, being equivalent to a connection gateway with unlimited expansion ability.

The database layer mainly includes data warehouse, model base, knowledge base and method base. The data warehouse is responsible for storing real-time sensor data perceived and received from the actual supply chain, as well as for storing basic data and system data. The model base is responsible for storing the governing equations and parameters. The knowledge base is responsible for storing and managing the library and historical cases.

The application layer is the integrated application of sensor data, packaging data, model processing results and knowledge modelling information, and outputs monitoring and control results to the presentation layer.

The presentation layer provides users with a visual environmental data and graphical user interface to display the dynamic change chart, historical data, plasma equipment status, control model processing results of real-time sensor data, so that users can easily obtain real-time data through the Internet and identify key risks in the oyster supply chain.

After completing the design and integration of ICC, it is necessary to evaluate its performance of the intelligent container to ensure its reliability, including the evaluation of the cold source utilization efficiency, sterilization effect, and timely monitoring function of smart containers.

In order to realize the multi-parameter acquisition and plasma sterilization in the micro-environment of oyster storage and transportation, an electronic part was designed and developed. Altium Designer is used to design circuit diagram and make PCB board for intensive design. The modules of the device are shown in Fig. 5.

As shown in Fig. 5, the Arduino Nano acts as a control module that transmits work instructions to other modules. In order to ensure stable and effective data transmission, digital-to- analogue converters and multiplexers are used to match the different output formats of the sensors. The sensing module includes three types of sensors: O₂ sensors, CO₂ sensors, and multi-gas monitoring sensors. The performance indicators of the sensor are shown in Table 2, and the time interval for the sensor to collect data can be set according to user requirements.

The sterilization module is responsible for the disinfection and sterilization of the environment and the surface of the oyster, with wirelessly controlling through the mobile phone, thereby controlling the opening and closing of the plasma generator, the structure of the plasma generator is shown in Fig. 2. The plasma generator boost module boosts the input voltage to 4.8 ± 0.5 kV, causing electrical breakdown of atmospheric air and generating plasma.

The software part of ICC has the ability to collect and integrate multi-parameter information. The platforms used for software development include Arduino IDE 1.8.13 and TLINK cloud platform.

The transportation container system information collection is based on the Arduino IDE platform, and the control program is written and burned into the main control chip to achieve data collection and storage. The entire process of information collection and storage is shown in Fig. 5. The process mainly includes system initialization, sending commands to initialize and start the sensors, verifying whether each sensor can work properly, configuring the timer, data reading, data preprocessing, data storage, data transmission, etc. System initialization includes calling library functions, defining interfaces, setting baud rates, allocating transmission addresses, etc.

The telecontrol function is achieved through the TLINK cloud platform, relying on the Message Queuing Telemetry Transport (MQTT) protocol for data transmission and parsing, and configuring the protocol on the TLINK cloud platform. MQTT protocol is a kind of low-overhead, low-bandwidth-use instant messaging protocol for the Internet of Things, small devices, and mobile applications that enables data transfer with minimal resource consumption (e.g., computation, storage, etc.). The MQTT service quality has 3 levels, and 0 level is selected here. After the initialization of the system is completed, the hardware device is connected to the cloud platform. If the connection is successful, the sensor data collection will begin. If the connection fails, an error will be reported and a reconnection attempt will be made. After collecting the sensor data, the sensor data needs to be pre-processed. The built-in microbial growth model and the gas-based neural network quality prediction model are used to determine whether to activate the plasma sterilization module. If the plasma sterilization module needs to be turned on, the relay controls the plasma equipment to be turned on and reports the device status to the cloud platform, and stores the gas sensor data in the local storage card and reports to the cloud platform, as shown in Fig. 5. During the monitoring process, the plasma equipment can also be remotely commanded to turn on through the cloud platform, and the command is issued.

Using COMSOL Multiphysics to solve the heat transfer process, the temperature of any time and space node can be calculated by the finite element method, which can effectively simulate the heat transfer process, realize the theoretical calculation of the temperature control effect of the transport container and simulate different temperature control methods, and provide reference for the practical application scheme. In order to explore the influence of phase change cold storage container placement on temperature change during transportation, the temperature control performance of the transport container is evaluated from the following three perspectives:

The pre-cooling time: Considering that in actual production, there are few operations to pre-cool oysters before they are put into containers for storage and transportation, and for the oyster supply chain, the core temperature of oysters is required to be lower than 8°C. Therefore, the pre-cooling time of the container is defined as the time it takes for the average temperature in the container drop to 8°C.

Cooling time: After the temperature of the oysters cooled to 8°C, the temperature continued to drop to 8°C again, and the total time when the temperature was lower than 8°C was defined as the cooling time.

Temperature uniformity: In order to ensure that the quality of oysters in the same batch is similar, it is necessary to control the temperature uniformity of oysters during storage and transportation. The temperature uniformity is defined as the difference between the highest and lowest temperature values at each measurement moment and then the average value.

Solid and fluid heat transfer models in COMSOL Multiphysics are used to solve the heat transfer process. Using the finite element method, the temperature in the capacitor at any time and space is calculated, and the following formula (1) to (4) were formulated and used for the modelling. Heat transfer inside solid and liquid phases is dictated by the energy balance equation (Brito et al., 2015):

where is the temperature, is the thermal conductivity, and is the specific heat capacity.

The density and specific enthalpy of the PCM are given as:

where is the percentage of the phase transition, and are respectively the densities of the liquid phase and the solid phase. and are the specific enthalpies of the liquid phase and solid phase proved in the COMSOL Multiphysics environment.

The phase change process is modelled by the equivalent specific heat capacity approach, given as:

where and are respectively the specific heat capacity of the liquid phase and the solid phase, and is the latent enthalpy of the PCMs.

In this case, a vertical natural convective boundary condition is applied to all six faces of the vessel. The initial temperature of the container and the PCMs is -1°C. A summary of the parameters used for the simulation is presented in Table 3.

A three-dimensional and transient numerical simulation was carried out using COMSOL Multiphysics® 6.0. Figure 6 (a) shows the container studied on this work, of which the outer dimensions are 615 mm (Length) × 385 mm (Width) × 425 mm (Height), and the internal dimensions are 535 mm (L) × 305 mm (W) × 335 mm (H). The container wall is made of Polyurethane (PU) insulation for thermal insulation, and the PCM plates are placed inside the container as a cold source, and its shell thickness is 30 µm. As shown in Fig. 6 (c), the influence of four PCM plate placement methods on the temperature field in the container was studied. The light purple part in Fig. 6 (c) is the PCM plate. The thermos-physical properties of the materials used in the container are shown in Table 4. The four ways of placing the PCM boards are named case1, case2, case3 and case4. case1 is two PCMs placed on the left and right sides of the case, and the other two on the top; case2 is three PCMs placed on the left and right sides and in the centre of the case, with the fourth placed in the middle of the two PCMs on the left and centre; case3 is three PCMs placed on the left, right, right, centre and the fourth piece is placed on the top right; case4 is three PCMs placed on the left side, in the middle of the container and between the above two PCMs, and the fourth piece is placed on the top right side of the container.

Meshing. The grid independence verification is carried out firstly. Figure 6 (d) is a comparison of the internal temperature of the container with different grid sizes. When the mesh size is reduced, the temperature difference inside the container is limited to 5%, indicating that the mesh has good independence. Therefore, by considering reliability and computation time, we chose a finer grid size with 36,811 elements for the simulation. These meshes used an unstructured tetrahedral mesh with an average element quality of 0.68. Figure 6 (b) shows the computational grids used for the simulation.

The relative tolerance was set to 0.01 and the absolute tolerance was set to 0.001 in the time solver. The total simulation time is 96 h, and the step size is set to 3 h.

The validity of the simulation model is verified by experiments. The PCM plates was frozen to complete solidification using the thermal control cabinet of Shanghai Yiheng Technology Co., Ltd. After complete freezing, the PCM plates were placed in the container in the manner shown in Fig. 6 (c), and the container was placed the thermal cabinet. The internal temperature of the thermal cabinet was maintained at 25°C. The temperature inside the container was monitored using a temperature logger from Hangzhou Jingxun Instrument Co., Ltd., which was placed at the middle and sides of the bottom of the container. The accuracy of the temperature recorder is ± 0.25%.

The experimental results are shown in the Fig. 6 (e). The numerical simulation results show that the temperature increase rate has a high consistency with the experimental results in the first half, and then is lower than the experimental value in the second half, but the final temperature tends to be consistent. This is because in the experiment, only the temperature near the PCM and the middle of the bottom of the container were measured. The temperature at these two places is different from the average temperature of the container as a whole. However, with the passage of time, the temperature around the container tends to be the same. At this time, the experimental temperature value is consistent with the simulated temperature value, which shows that the simulation of the cooling effect of the PCM is reliable.

Plasma sterilization equipment needs to be turned on to delay the peak of colony proliferation before the total number of oyster colonies changes dramatically to prevent quality degradation.

The plasma automatic sterilization regulation model is based on the quality changes of oysters, which can be achieved through microbial-based modelling of water product quality and environmental-based quality modelling. Microbial-based modelling for water product quality refers to the prediction of changes in water product quality using the growth, metabolism, and death characteristics of microbes by modelling the factors that affect bacterial growth, such as temperature, O₂ content, pH value, etc., to predict the shelf life and quality changes of fresh fish. Environmental quality prediction based on indirect detection principle refers to the connection between environmental factors such as temperature, humidity, and light and changes in water product quality, to establish a mathematical model for predicting the changes in water product quality. Both modelling methods are based on microbial models that focus on analysing the growth and metabolic characteristics of microbes, whereas the environmental model focuses on exploring the impact of environmental factors on water product quality. Therefore, in practical applications, both models can be comprehensively considered.

The modelling of plasma sterilization and automatic regulation is used to obtain the time-series of the key parameters for the micro-environment of transporting aquatic products, such as temperature, O₂ content, etc. Based on the time-series changes of the key parameters and the changes in the quality of aquatic products, a dynamic model of the quality of aquatic products is established, and the quality of aquatic products is predicted, which generates a plasma control decision model. Finally, this decision-making method is applied to the actual transportation process to verify the accuracy of the quality coupling model, and to provide transport recommendations for oysters. Specific decision control can be seen in theFig.7 and as follows:

(1) The growth retardation period, the maximum specific growth rate and the total number of colonies TVC were determined based on the microbial growth dynamics model.

The growth retarded period λ of the total number of colonies under the initial sterilization scheme was determined based on the microbial growth model, and h was recorded as the sterilization time point of the first plasma equipment, and the time was input into the control model.

(2) The concentration values of oxygen, carbon dioxide, hydrogen sulphide and ammonia were collected, and the total number of colonies was calculated by neural network model.

In the actual transportation process, the initial state of oysters is different, so it is necessary to reduce the gas parameter changes in the actual transportation to the gas parameter changes used in the neural network model.

(3) Comparing and , calculating the colony growth rate predicted by the neural network model and comparing with the maximum specific growth rate . If or, the plasma sterilization equipment is turned on for 10 min.

The revised Gompertz model was used to fit the primary growth model of the total number of oyster colonies in Origin (Çelekli et al., 2008). The primary expression form of the revised Gompertz model is as follows:

Where, is the total number of colonies corresponding to time t (lg(CFU/g)), is the initial total colony value, is the maximum number of colonies when the bacteria grow to a stable stage, is the maximum specific growth rate /, is the lag period of the colony/h, t is time/h.

The adjusted coefficient of determination measures the goodness of fit of the results, and the closer its value is to 1, the better the predictive effect of the model. The formula for calculating the adjusted coefficient of determination is as follows. The adjusted coefficients of determination of the model fittings are all close to 1, indicating that the model fitting effect is good and the accuracy is high:

There, is the model prediction value, is the experimental observation value, n is the number of observed values, is the number of parameters in the model.

A second-order quadratic polynomial is used to describe the relationship between the maximum specific growth rate, lag phase, plasma sterilization intensity, and duration.

There, is the sterilizing time (min), W is the level of sterilization intensity,,,is the model coefficient.

In the oyster transportation chain, if gas is expected to characterize the trend of oyster colony changes, it is necessary to first determine whether there is consistency between the analysis of different gas change trends and the total number of colony change trends. The use of a BP neural network-based method for predicting the quality of aquatic products can predict the quality changes of aquatic products without clear quality index change rules and quantify the influence of different factors. Through the establishment of "environment-quality" coupling model, environmental parameters can be used to characterize the quality changes of aquatic products, and accurate prediction of the quality of aquatic products without damaging them, which can provide a method to realize the intelligent detection of aquatic product quality. The growth trend data of gas and colony growth are grey correlated, so that the colony growth trend is indicated by gas concentration, and the regulation rules of plasma equipment are formulated on this basis. The grey correlation coefficient is calculated as formula (11). When calculating the grey correlation, in order to focus on the changes and trends, reducing the difference of the absolute value of the data, the gas should be normalized to unify the monitoring quantity to the approximate range. Normalization is carried out through initialization as shown in formula (10).

There, the comparison sequence (also called subsequence) is . The reference sequence (also called parent sequence) is . is called the resolution coefficient. The smaller the , the greater the resolution. Generally, the value range of is (0,1), and the specific value depends on the situation.

A three-layer neural network can approximate any nonlinear continuous function. Therefore, a three-layer BP neural network was used for modelling and analysis. This model considers a neural network architecture with a single hidden layer. The number of neurons in the hidden layer is determined by an empirical formula, As shown in formula (7). The transfer function of the neural network's hidden layer uses the S-shaped tangent function 'tansig', and the output layer uses the pure linear function 'purelin' as the transfer function. The Levenberg-Marquardt function is used as the training function of the neural network. 70% of the data is used as the training set, 15% as test set, and 15% as validation set (Huang et al., 2023).

The hydroxyl radicals (OH) and ozone contained in the plasma are highly effective in sterilization, but have lifetimes ranging from a few nanoseconds to several seconds. In order to verify the effectiveness of the plasma generator used in this paper, Optical Emission Spectroscopy (OES) was chosen to examine the plasma composition produced by the plasma generator. OES spectra were monitored using a spectroradiometer (USB 2000+; Ocean Optics, FL, USA) in this study, and spectra obtained by the spectroradiometer were analysed using the National Institute of Standards and Technology's (NIST) Atomic Spectroscopy database.

Pre-experiments were conducted on the initial storage conditions of oysters at 20 ℃. The pre-experiments were divided into nine scenarios, exploring colony growth at three sterilization intensities (one, two and three plasma generators in the container) and three sterilization durations (5, 10 and 15 min). Eight parallel groups were set up for the pre-experiment in each case, three oysters were placed in each group. Every 12 h, one of the parallel groups was taken to test the number of colonies, and the average of the number of colonies of the three oysters was recorded. The pre-experiment was carried out for 96 h and the number of colonies in each case where the bacteria grew to a stable stage was recorded and used for the modification of the Gompertz model at a later stage. The colony values were recorded as .

After obtaining the plasma control model, the practical application experiment was carried out. Two groups were set up. Normal group: the storage temperature was 20°C, without plasma sterilization and plasma processing group: the storage temperature was 20°C, plasma sterilization was used as the colony growth inhibition treatment. Considering that the total number of colonies can be used as a means to evaluate the regulation results of oyster supply chain, and the long-term changes of the total number of colonies, 7 parallel groups were set up for each of the usual and plasma groups, and six oysters were placed in each parallel group, the volume ratio of the container to the volume of oysters is 4:3, and placed in a 20°C environment. One group of oysters was taken every 12 h for colony count detection, and the final result recorded was the average of the colony counts of the six oysters, and the change of the gas concentration in the containers was tested through the sensor network every 15 min. The experiment was conducted for a total of 72 h.

Take 25g meat from 3 oysters in the parallel samples, according to the ratio of the weight of oyster meat to the volume of sterile 90% normal saline is 1:9, use a sterile masher to homogenize at a speed of 8000 ~ 10000 r/min for 2 min. Determine total viable bacterial count by plating on plate count agar and incubating at 30°C ± 1°C for 72 h.

The colour of fish blocks was analysed using a CR-400 chroma meter (Konica Minolta, Japan) with a 20 mm detection aperture and a 10° approximate observation angle. L* (lightness), a* (redness-greenness), b* (yellowness-blueness) and ΔE* (overall colour difference) values of oysters were measured, the results were expressed as the mean of three determinations. Whiteness can represent the colour quality of oysters, and its calculation is as follows.

The total colour difference is calculated by the following formula.

The data was pre-processed with Excel 2019 software, then Origin 2022b software was used for drawing and data processing, and MATLAB R2016a was used for greyscale analysis.

There are various reasons for colour changes in oyster meat during frozen storage. Lekjing et al (Lekjing & Venkatachalam, 2018) concluded that oyster meat and endomysium are cream-coloured and turn first yellow, then brown and finally green during storage. These colour changes may be caused by yellow pigments produced by protein denaturation, lipid oxidation, and the Meladic reaction (Rodezno et al., 2023). Since the algae that oysters eat as they grow are rich in red carotene, during storage, oyster meat deteriorates due to bacteria and its own enzymes leading to tissue collapse, and the red carotene in the body leaks out of the protein complexes, which also can lead to yellowing of the meat. In addition, colour parameters are also affected by optical properties, protein denaturation, polymerization crosslinking degree, gel composition and other factors. The changes of oysters in the normal and plasma groups are shown in Fig. 10 (a) and (b). As Fig. 10 (e) shows, with the extension of storage time, the whiteness of oyster meat shows a downward trend, indicating that oyster meat deteriorates, loses juice and becomes darker during storage. With the extension of storage time, the total colour difference between the germicidal group and the non-germicidal group becomes larger, and the whiteness value of the germicidal group is better than that of the non-germicidal group. As can be seen from the figures, the colour of oyster meat in the normal group turned yellow with the prolongation of storage time, indicating that the oyster meat underwent deterioration and loss of juice during storage, which ultimately led to the darkening of the colour. However, the oyster meat in the plasma group could maintain its white colour. It can be initially judged that the quality of oysters in the sterilized group was better than that of the usual group. The whiteness value of oysters was measured as shown in Fig. 10 (e), which indicated that the whiteness value of oysters decreased significantly with the extension of storage time, and the whiteness value of the plasma group was better than that of the normal group, the total colour difference between the two groups also tended to increase. Measurement of the whiteness values of oysters provides preliminary evidence that the use of plasma sterilization has a positive effect on maintaining the freshness of oysters and delaying deterioration. Liu et al (Liu et al., 2024). showed that the freshness of oysters can be characterized by the closed-shell force. It can also be seen through Fig. 10 (c) and (d) that the opening of oysters in the plasma group was smaller than that of the normal group, which can also provide a preliminary basis for proving that the quality of oysters in the sterilized group was higher than that of the normal group.

The acceptable limit for total bacterial count for oyster meat is 7 lg (CFU/g) as specified by Kim et al (Kim et al., 2002). From Fig. 11, it can be seen that when two plasma generators are set up and the sterilization time is 10 minutes, the number of colonies in the container can be controlled below the specified value. And the increase in the number of equipment and sterilization time on the sterilization effect is not obvious. Therefore, the final choice is to set up two plasma generators and sterilize for 10 minutes at a time as the sterilization method for the transportation experiment. The colony growth of the usual and plasma groups is shown in Fig. 10 (f). The plasma generator was first turned on at 32.67 h, and then turned on at 43.25 h, 54.25 h, and 69.25 h, respectively. It can be seen that the total number of colonies in oyster meat decreased at the beginning and then increased with the prolongation of storage time. This is because some strains did not grow or even died in the low temperature environment, causing the total number of colonies to decrease rapidly. With the prolongation of storage time, some strains adapted to the environment. As can be seen from the figure, the total bacterial counts of oyster meat in both groups were less than 4.5 lg (CFU/g) in 72 h, which is lower than the acceptable limit of 7 lg (CFU/g) for total bacterial counts of oyster meat according to Kim et al (Kim et al., 2002), whereas the maximum colony count of the plasma group was only 3.5 lg (CFU/g), which is half of the specified value. It can be seen that the temperature control based on PCM has a positive effect on the preservation of oysters, and it is still possible to maintain the quality of oysters at a high level within 72 h without applying additional sterilization.

During the same storage period, after the 32.67th h, due to the use of the plasma generator, the total number of colonies in the plasma group decreased significantly at the 36th h, which was faster than that of the usual group at the 48th h. The difference in the total number of colonies between the plasma and usual groups reached a maximum of 2 lg (CFU/g) at the 48th h, indicating that the plasma treatment had a significant inhibitory effect on microbial reproduction and metabolism.

Changes in the gaseous environment within the container may serve as important indicators of oyster quality changes and population growth, and a sensor network provides an effective monitoring approach. Xu et al. (Xu et al., 2023) obtained the gas data inside the packages by means of a sensor network and comprehensively analysed the effects of O₂, CO₂, and H₂S on the quality of meat in different packages. The changes of key environmental parameters are shown in Fig. 12 (a) to (d). The rate of O₂ consumption within the container tends to increase continually over time and exhibits a stepwise rise. The concentrations of NH3 and CO₂ rise rapidly within 24 hours, after which a slowdown occurs, followed by a rapid upsurge at around 48 hours. The increase in H₂S concentration is comparatively slow, and its concentration remains below 1 ppm, making it the lowest of the four indicators. From Fig. 11(b), it can be observed that the O₂ consumption in the plasma-treated group is slower than in the conventional group, indicating that plasma treatment can effectively suppress the activity of aerobic microorganisms. Figures 11(c) and 11(d) show that the plasma-treated group maintains clearly lower concentrations of NH₃ and H₂S than the control group, suggesting that plasma treatment can retard decay reactions. Changes in CO₂ concentration can serve as an important indicator of microbial respiration intensity and, in this experiment, reflect the activity level of oxygen-requiring bacteria in the container. As shown in Fig. 11(a), after 48 hours CO₂ concentration increases rapidly in the control group, whereas the rate of increase in the plasma-treated group is comparatively slower and remains markedly lower than in the control group at 48 hours, indicating that the respiration and proliferation of aerobic bacteria within the container are suppressed. Overall, following plasma treatment, the magnitude of microbial responses decreases, and the spoilage of oyster quality is delayed.

Using the modified Gompertz model (Zwietering et al., 1990), the primary growth model of oyster mushroom colony was fitted in Origin. The model results are shown in Fig. 11. The adjusted determination coefficients of the model fittings are close to 1, indicating a good model fitting effect and high accuracy of the model. The results of the fitting analysis of the secondary model are shown in the Table 5.

The correlation degree of the assessment indicators is formed based on the difference in correlation values. The higher the correlation degree of the data results, the higher the consistency of the changes. Figure 12 (e) shows the results of grey correlation analysis. It can be seen that the correlation degree between H₂S concentration and total colony count is the highest, followed by the change trend of NH₃ concentration, and the change trend of O₂ consumption is about 0.5. As time increases, the correlation degree of CO₂ decreases rapidly, reaching only 0.35 in the end. The grey correlation degree between gas concentration and total colony count has a decreasing trend with time, but the decreasing trend is different, which may be due to different physicochemical reactions that occur in different stages of oyster quality deterioration, resulting in different proportions of gas concentration. Overall, there is a high correlation between the trend of gas concentration change and the trend of total colony count change, and the change in gas concentration can reflect the change in total colony count of oyster.

The prediction model of internal gas concentration-colony number during storage of oysters in ICC was developed based on BP neural network. Table 6 shows the training results of the network model with different numbers of hidden layer neurons, from which it can be seen that different numbers of hidden layer neurons in the neural network will lead to different final results of the model training, for the present study, the number of iterations is lower and the mean square error is at the lowest level when the number of hidden layer neurons is 3. The prediction results are shown in Fig. 11 (f). The R² value of the prediction model reaches 0.9983, and there are no outliers in the predicted values that deviate excessively from the fitted curve, so the predictions meet the requirements.

The power consumption of each component in the system is shown in Table 7. The total output power is 570 mW when combined with the 5 V voltage, which means the overall power consumption of the transportation box is 570 mW. In order to ensure the stability of the battery power supply and the normal operation of the monitoring function during the actual transportation process, a 5V/10000mA lithium battery is used for power supply, and when the battery voltage is lower than 3.3 V, the capacity of the battery is calibrated to be 0%. In the demonstration of the transportation box, the remaining capacity of the battery of the transportation box treated with plasma sterilization is 20%, while the remaining capacity of the battery of the group without plasma sterilization is 40%. The overall experiment showed that the transport case was able to achieve 72 h of continuous battery life. The overall experiment shows that the transport box can realize 72 h of continuous monitoring and satisfy the demand for regulating the transportation and control of aquatic products without water preservation. It can be seen that the overall power consumption of the device is low, the energy cost can be negligible.

Container evaluation is determined through discussions with managers and experts from sampling companies and universities, including evaluating whether the performance of the container is satisfactory, whether it can meet the requirements of users and whether the container function can be further improved. Table 8 shows the results of the smart container evaluation.

The existing research on quality control technology for the transportation process of aquatic products lacks multi-parameter monitoring and effective control of the transportation microenvironment. For example, it is difficult to collect and analyse the key parameters of the microenvironment in the transportation process in real time, accurately and continuously by effective means. What’s more, the quality determination of aquatic products in the transportation process is relatively simple, and the parameters are relatively few, in some cases, the prediction of the quality only relying on the ambient temperature to complete, which has a great deal of randomness. As a result, it is hard to effectively achieve environmental control and quality prediction of the fish transportation process.

Transport containers can meet different transportation scenarios in the oyster transportation chain, provide the concept of transport containers and related software and hardware support, as shown in Table 8. In this study, the unitized temperature control requirements for aquatic products transportation were met by integrating phase change energy storage technology, and the transportation microenvironment of aquatic products in ICC was effectively monitored by multi-sensor monitoring technology. On the other hand, based on the neural network quality prediction and microbial growth quality prediction, the plasma regulation model is jointly developed by analysing the total number of colonies and changes in microenvironment gas parameters, and plasma regulation based on dynamic changes in the quality of aquatic products is realized, improving the quality of aquatic products after transportation. In contrast to ICC, the gas-regulated packaging used in the experiments by Schrobback et al. (Schrobback et al., 2021) allowed only a single temperature to be monitored and lacked an effective means of sterilization. While Love et al.'s experiments added monitoring of key gases such as O₂ and CO₂, and added an early warning system with a multi-parameter based temperature regulation system (Love et al., 2019). However, the temperature sensors Love was using were not only expensive ($50 each), but also lacked the ability to record the critical parameter of relative humidity, and product loss rates were only reduced by about 10 percent. The traceability that ICC has can improve the transparency of aquatic product transportation, and efficient temperature control and sterilization regulation can reduce the rate of spoilage during the transportation of aquatic products, improve the market price of aquatic products, obtain more economic benefits, and promote the development of the industry chain.

Table 9 shows the cost of ICC, and it is clear to see that the addition of PCM, intelligent temperature control system and sterilization equipment significantly raises the cost of oyster storage containers, however, the most costly of them all is the container itself, which is essential for oyster storage and transportation even if no other equipment is set up, and the size of the containers with its insulation can be adjusted according to the actual needs, which is not necessary in all cases to be so costly. Moreover, although the addition of temperature control and sterilization equipment significantly increases the cost, the equipment itself can be used for a long time. The container used in this experiment had a volume of 56 L, and field tests indicated that the container could hold at least 15 kg of oysters, excluding the space taken up by the PCM and sanitizing equipment. If this criterion is applied, the use of ICC would result in an additional cost of approximately $48.0823 per 15 kg of oysters, with an average additional cost of $3.2055 per 1 kg of oysters (excluding the price of the container and PCM), and the use of ICC would not reduce the amount of labour required, as the assembly of the oysters and the refrigerant would still need to be done manually. The use of ICC could increase oyster depletion rates by up to 25% and increase the profit per kilogram of oysters by $6.60, which is more than the value of the investment in the equipment. Therefore, the input cost of ICC can be quickly recovered and more quality oysters can be provided to customers. In summary, ICC has a broad application prospect.