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Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

The quality of oysters is reflected by volatile organic components. To rapidly assess the freshness level of oysters and elucidate the changes in flavor substances during storage, the volatile compounds of oysters stored at 4, 12, 20, and 28 °C over varying durations were analyzed using GC-MS and an electronic nose. Data from both GC-MS and electronic nose analyses revealed that alcohols, acids, and aldehydes are the primary contributors to the rancidity of oysters. Notably, the relative and absolute contents of Cis-2-(2-Pentenyl) furan and other heterocyclic compounds exhibited an upward trend. This observation suggests the potential for developing a simpler test for oyster freshness based on these compounds. Linear Discriminant Analysis (LDA) demonstrated superior performance compared to Principal Component Analysis (PCA) in differentiating oyster samples at various storage times. At 4 °C, the classification accuracy of the optimal support vector machine (SVM) and random forest (RF) models exceeded 90%. At 12 °C, 20 °C, and 28 °C, the classification accuracy of the best SVM and RF models surpassed 95%. Pearson correlation analysis of the concentrations of various volatile compounds and characteristic markers with the sensor response values indicated that the selected sensors were more aligned with the volatiles emitted by oysters. Consequently, the volatile compounds in oysters during storage can be predicted based on the response information from the sensors in the detection system. This study also demonstrates that the detection system serves as a viable alternative to GC-MS for evaluating oysters of varying freshness grades.

Environmental and physiological fluctuations in the live oyster cold chain can result in reduced survival and quality. In this study, a flexible wireless sensor network (F-WSN) monitoring system combined with knowledge engineering was designed and developed to monitor environmental information and physiological fluctuations in the live oyster cold chain. Based on the Hazard Analysis and Critical Control Point (HACCP) plan to identify the critical control points (CCPs) in the live oyster cold chain, the F-WSN was utilized to conduct tracking and collection experiments in real scenarios from Yantai, Shandong Province, to Beijing. The knowledge model for shelf-life and quality prediction based on environmental information and physiological fluctuations was established, and the prediction accuracies of TVB-N, TVC, and pH were 96%, 85%, and 97%, respectively, and the prediction accuracy of viability was 96%. Relevant managers, workers, and experts were invited to participate in the efficiency and applicability assessment of the established system. The results indicated that combining F-WSN monitoring with knowledge-based HACCP modeling is an effective approach to improving the transparency of cold chain management, reducing quality and safety risks in the oyster industry, and promoting the sharing and reuse of HACCP knowledge in the oyster cold chain.

Non-invasive blood glucose level (BGL) evaluation technology in skin mucus is a wearable stress-detection means to indicate the health status of live fish for compensating the drawbacks using traditional invasive biochemical inspection. Nevertheless, the commonly used methods cannot accurately obtain the BGL variations owing to the influence of an uncertain glucose exudation rate, ambient effects, and individualized differences. Our study proposes a non-invasive multi-sensor-fusion-based method to evaluate the dynamic BGL variations using the enhanced gray wolf-optimized backpropagation network (EGWO-BP) to continuously acquire more accurate trends. Furthermore, the K-means++ (KMPP) algorithm is utilized to further improve the accuracy of BGL acquisition by clustering fish with full consideration of its size features. In the verification test, turbot (Scophthalmus Maximus) was selected as an experimental subject to perform the continuous BGL monitoring in waterless keep-alive transportation by acquiring comprehensive biomarker information from different parts of fish skin mucus, such as fins, body, and tails. The comparison of results indicates that the KMPP-EGWO-BP can effectively acquire more accurate BGL variation than the traditional gray wolf-optimized backpropagation network (GWO-BP), particle swarm-optimized backpropagation network (PSO-BP), backpropagation network (BP), and support vector regression (SVR) by mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Finally, the proposed BGL fusion evaluation model can precisely acquire the live fish's physiological stress states to substantially reduce the potential mortality for the live fish circulation industry.

(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky–Golay smoothing, SG; Savitzky–Golay 1 derivative, SG-1st; and Savitzky–Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R2p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R2p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.

Fruit quality monitoring has been more and more important. This paper aims to propose and develop a battery-free wireless moisture sensor system for fruit monitoring (BWMS). The BWMS is made up of the battery-free moisture sensor tag and the near filed communication (NFC) reader. The battery-free moisture sensor tag includes the sensor acquisition module and NFC module. The storage temperature and weight sensor data of the fruit could acquire by the battery-free moisture sensor tag and wirelessly transmitted to the NFC reader by 13.56 MHz when receiving the data acquisition command from NFC reader. The BWMS could effectively monitor the MLR by acquiring the temperature and weighing sensor data with NFC, and predict the shelf-life of the fruit with in need by the shelf-life prediction model. All the data obtained by the BWMS was in real time monitored, stored, processed and finally displayed in the app of the NFC reader to improve the temperature, MLR and shelf-life transparency of the fruit, and finally ensure the quality and safety of fruit. The proposed BWMS has a potential application in many kinds of food monitoring by NFC and it could realize the shelf-life prediction for the simple and intuitive quality indicating of food.

Monitoring the storage phase of grapes is a key issue as it is closely related to berry quality, so it is essential to investigate a rapid and comprehensive method of non-destructive wireless inspection applied to the berry storage process. In this study, a cost-effective optical measurement system was designed, built, and tested to determine optimal storage results. NIR spectral measurement system covers 6 separate on-device filters from 610 nm to 860 nm. The experiments were performed on Kyoho grapes. In addition, each sample was analyzed for soluble solids content (SSC) by conventional laboratory methods and used as a reference parameter. The results showed that the use of the artificial neural network (ANN) (RMSEP = 0.62 and Rp2 = 0.69) worked best and could be used for preliminary screening of fruit quality.

Post-ripening fruits need to be ripened to reach edible conditions, as they are not yet mature enough when picked. Ripening technology is based mainly on temperature control and gas regulation, with the proportion of ethylene being one of the key gas regulation parameters. A sensor’s time domain response characteristic curve was obtained through the ethylene monitoring system. The first experiment showed that the sensor has good response speed (maximum of first derivative: 2.01714; minimum of first derivative: −2.01714), stability (xg: 2.42%; trec: 2.05%; Dres: 3.28%), and repeatability (xg: 20.6; trec: 52.4; Dres: 2.31). The second experiment showed that optimal ripening parameters include color, hardness (Change Ⅰ: 88.53%, Change Ⅱ: 75.28%), adhesiveness (Change Ⅰ: 95.29%, Change Ⅱ: 74.72%), and chewiness (Change Ⅰ: 95.18%, Change Ⅱ: 74.25%), verifying the response characteristics of the sensor. This paper proves that the sensor was able to accurately monitor changes in concentration which reflect changes in fruit ripeness, and that the optimal parameters were the ethylene response parameter (Change Ⅰ: 27.78%, Change Ⅱ: 32.53%) and the first derivative parameter (Change Ⅰ: 202.38%, Change Ⅱ: −293.28%). Developing a gas-sensing technology suitable for fruit ripening is of great significance.

Laser processing has proven to be a potential candidate. It provides a fast and non-destructive additive manufacturing method to produce highly conductive patterns on flexible substrates. In this work, we investigate the use of laser sintering of silver nanoparticle inks. The silver nanoparticle ink is inkjet printed on a flexible substrate and laser-sintered to obtain a highly conductive pattern. The results are of great significance to the large-scale development of cost-effective and environmentally friendly production methods for flexible electronic products.

Additive manufactured flexible sensors have prospect applications in wearable devices, healthcare, soft robotics, and metaverse scenarios. However, there is still performance gaps between the emerging additive manufactured flexible sensors and traditional subtractive manufactured rigid sensors. Temporal drift characteristic, as one of the important performance parameters of sensors, was evaluated and analyzed with inkjet printed Ag NPs-PEDOT:PSS flexible temperature sensor, and the potential influence mechanism were explained from the glass transition of the substrate, the swelling effect of water molecules, and melting effect of silver nanoparticles perspective, thereby providing solutions for bridging the performance gaps of additive manufactured flexible sensors.

The effect of CO2 concentrations on the preservation of sweet cherries in modified atmosphere packaging was greatly different. In the present paper, an accurate gas-regulating storage device was used to set the concentrations of CO2 at 0%, 5%, 10%, 15%, 20% and 25%, respectively (O2 was set at 5% and the remaining parts were filled with N2) to store sweet cherries. Then the quality change of sweet cherries was determined aproximately from -1 to +1 °C and from 80 to 85% relative humidity. The results showed that all six air treatments had a certain inhibitory effect on deterioration of sweet cherries. In particular, the 10% CO2 group could reduce rotting rate, maintain firmness, delay the change of soluble solids and vitamin C, reduce the activity of polyphenol oxidase and peroxidase. This group always had good firmness, nutrition and taste after 120 days of storage. So the 10% CO2 concentration provides suitable gas storage conditions of sweet cherries in modified atmosphere packaging.

Live fish waterless transport strategy for the purpose of consumption or ornament is a novel technology, which can implement the larger volume transportation with high survival results as well as less wastewater pollution. The aim of this study was to establish an accurate survival prediction system based on the least physiological stress variations in different temperature ranges, which statistically analyzes the trend of key stress factors under the well life-supported temperatures to improve the final survival result. Furthermore, the accuracy of survival prediction can be adaptively and dynamically improved in consideration of the historical survival series in different seasons. In order to verify the practicability and performance of the system, Paralichthys olivaceus was selected as experimental subjects to carry out dynamic survival prediction experiments for over 30 hours waterless transportation, and the accuracy of the survival prediction system has verified over 97.2% in 0.5-2.5C°, which show that mean absolute error (MAE) and mean squared error (MSE) by less than 0.2 and 0.07 to produce the most accurate estimation in the comparison. The development of this survival prediction system can significantly optimize the current waterless delivery management by reducing the potential mortality and providing the managerial references for industrialization of the live fish waterless logistics.

IoT-enable monitoring can provide valuable information for the shellfish quality evaluation during cold storage condition. However, IoT based information storage relies on the centralized platform, it is possible to tamper. In this paper, we establish blockchain based multi-sensors (WSN) monitoring system to collect quality parameters and verify captured information for improving transparency and trust during cold storage. The implementation of the K-means and SVM algorithms were used in quality evaluation applications to classify and predict the quality loss of frozen shellfish. The results show blockchain based WSN monitoring can achieve the dynamic indicators continuous monitoring and ensures the data security and reliability. The proportion of the training set and the test set in the allowable deviation range is 88.89% and 87.17%. The root mean square error (RMSE) of training set and test set are 0.1502 and 0.1793 by SVM model. The performance of the K-means and SVM model has higher accuracy than BP model. This paper could help to reduce the risk of food losses and improve quality and safety management of frozen shellfish during cold storage.

Green, sustainable and non-stop wireless sensing is not only one of the critical issues in the widespread applications of wireless sensing to guarantee the food quality and safety in cold chain, but also one of the important ways to improve the sustainability of the food cold chain. The aim of this paper is to propose and develop a sustainable solar powered battery-free sensing system for food cold chain management. The system includes the sustainable solar energy harvesting layer, battery-free wireless sensing layer and remote monitoring and management center. The sustainable solar power performance, battery-free wireless sensing performance, energy consumption performance, emergy based sustainability performance and the system evaluation performance of the sustainable solar powered battery-free wireless sensing system were analyzed and evaluated. The proposed and developed sustainable solar powered battery-free sensing system could reduce the average energy consumption by 87.04%, the cost by 15.3% and increase the greening, sustainability and non-stop monitoring of the food cold chain by comparing with the battery powered wireless sensing system. This study could also provide some theoretical and experimental methods for the other food non-stop monitoring in cold chain to improve the sustainable development and ensure the food quality and safety. It is also possible to integrate more different sensors for non-stop cold chain battery-free wireless sensing.

The shell-closing strength (SCS) of oysters is the main parameter for physiological activities. The aim of this study was to evaluate the applicability of SCS as an indicator of live oyster health. This study developed a flexible pressure sensor system with polydimethylsiloxane (PDMS) as the substrate and reduced graphene oxide (rGO) as the sensitive layer to monitor SCS in live oysters (rGO-PDMS). In the experiment, oysters of superior, medium and inferior grades were selected as research objects, and the change characteristics of SCS were monitored at 4 °C and 25 °C. At the same time, the time series model was used to predict the survival rate of live oyster on the basis of changes in their SCS characteristics. The survival times of superior, medium and inferior oysters at 4 °C and 25 °C were 31/25/18 days and 12/10/7 days, respectively, and the best prediction accuracies for survival rate were 89.32%/82.17%/79.19%. The results indicate that SCS is a key physiological indicator of oyster survival. The dynamic monitoring of oyster vitality by means of flexible pressure sensors is an important means of improving oyster survival rate. Superior oysters have a higher survival rate in low-temperature environments, and our method can provide effective and reliable survival prediction and management for the oyster industry.

The quality of Tibetan matsutake drops during cold chain transportation. To extend the shelf life and improve the market value, this study analyzed the matsutake logistics process, and optimized the dynamic monitoring and quality management systems for post-harvest matsutake with different preservation packaging in the cold chain. This system monitored the micro-environmental parameters of the cold chain in real time, and it identified the best preservation method by analyzing the quality change characteristics of the matsutake with different preservation packaging. It was concluded that the matsutake were best preserved under the conditions of modified atmosphere packaging. The data analysis on the collected data verified the performance of the system. Relevant personnel were invited to participate in the system performance analysis and offer optimization suggestions to improve the applicability of the established monitoring system. The optimized model could provide a more effective theoretical reference for the dynamic monitoring and quality management of the system.

Baikouquan oil field is composed of multiple interbedded, thin, low-permeability layers, which are required to vertically fracture multiple layers and to create complex fractures for economic development. However, conventional fracture technologies create a single, simple fracture, having poor feasibility for this field. Therefore, we conducted research on fracturing technology by spontaneously selecting geologic sweet spots based on diversion. This technology can vertically fracture the thin layers one by one and horizontally divert the fracture to non-depleted areas. Firstly, a triaxial diverted fracturing experiment approach was setup, and then diverted fracturing experiments were carried out to verify the feasibility of diverted fracturing and to study the fracture geometry and the law of diversion. Next, experiments were carried out to evaluate the performance of the diversion agents. The valuated properties comprise the diversion pressure, stability time, and degradation based on which to optimize the selection of the diversion agents. Finally, the fracturing technology was applied to well b21004 of Baikouquan oil field, and post-frac performance was evaluated. The experimental results show that multiple and complex fractures are realized through temporary plugging. Diversion performance evaluation tests show that a 4 wt% concentration of 1–5 mm granules + 20/60 mesh powder and a 3 wt% concentration of 1–7 mm granules + 20/60 mesh powder + fiber can hold up enough pressure to force the fracture to divert. The field treating pressure curve shows that there is a 3–10 MPa pressure increase when there are pump diversion agents, which is a clear sign of fracture diversion. Plugging the fracture mouth gives a faster and a higher incremental pressure. Before this fracturing, the well had almost stopped oil production. After the stimulation, the initial oil production rate became 20 + t/d, which shows the effectiveness of this fracturing technology for Baikouquan oil field.

With the enhancement of consumers’ food safety awareness, consumers have become more stringent on meat quality. This study constructs an intelligent dynamic prediction model based on knowledge rules and integrates flexible humidity sensors into the non-destructive monitoring of the Internet of Things to provide real-time feedback and dynamic adjustments for the chilled chicken cold chain. The optimized sensing equipment can be attached to the inside of the packaging to deal with various abnormal situations during the cold chain, effectively improving the packaging effect. Through correlation analysis of collected data and knowledge rule extraction of critical factors in the cold chain, the established quality evaluation and prediction model achieved detailed chilled chicken quality level classification and intelligent quality prediction. The obtained results show that the accuracy of the prediction model is higher than 90.5%, and all the regression coefficients are close to 1.00. The relevant personnel (workers and cold chain managers) were invited to participate in the performance analysis and optimization suggestion to improve the applicability of the established prediction model. The optimized model can provide a more efficient theoretical reference for timely decision-making and further e-commerce management.

A SPEC/AuNPs/PMB modified electrode was prepared by electrodeposition and electro-polymerization. The electrochemical behavior of reduced nicotinamide adenine dinucleotide (NADH) on the surface of the modified electrode was studied by cyclic voltammetry. A certain amount of substrate and glutamate dehydrogenase (GLDH) were coated on the modified electrode to form a functional enzyme membrane. The ammonia nitrogen in the water sample could be calculated indirectly by measuring the consumption of NADH in the reaction. The results showed that the strength of electro-catalytic current signal was increased by two times; the catalytic oxidation potential was shifted to the left by 0.5 V, and the anti-interference ability of the sensor was enhanced. The optimum substrate concentration and enzyme loading were determined as 1.3 mM NADH, 28 mM α-Ketoglutarate and 2.0 U GLDH, respectively. The homemade ceramic heating plate controlled the working electrode to work at 37 °C. A pH compensation algorithm based on piecewise linear interpolation could reduce the measurement error to less than 3.29 μM. The biosensor exhibited good linearity in the range of 0~300 μM with a detection limit of 0.65 μM NH4+. Compared with standard Nessler’s method, the recoveries were 93.71~105.92%. The biosensor was found to be stable for at least 14 days when refrigerated and sealed at 4 °C.

Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.

Due to the presence of bioactive compounds, fruits are an essential part of people’s healthy diet. However, endogenous ethylene produced by climacteric fruits and exogenous ethylene in the microenvironment could play a pivotal role in the physiological and metabolic activities, leading to quality losses during storage or shelf life. Moreover, due to the variety of fruits and complex scenarios, different ethylene control strategies need to be adapted to improve the marketability of fruits and maintain their high quality. Therefore, this study proposed an ethylene dynamic monitoring based on multi-strategies control to reduce the post-harvest quality loss of fruits, which was evaluated here for blueberries, sweet cherries, and apples. The results showed that the ethylene dynamic monitoring had rapid static/dynamic response speed (2 ppm/s) and accurately monitoring of ethylene content (99% accuracy). In addition, the quality parameters evolution (firmness, soluble solids contents, weight loss rate, and chromatic aberration) showed that the ethylene multi-strategies control could effectively reduce the quality loss of fruits studied, which showed great potential in improving the quality management of fruits in the supply chain.

The market demand for fresh sweet cherries in China has experienced continuous growth due to its rich nutritional value and unique taste. Nonetheless, the characteristics of fruits, transportation conditions and uneven distribution pose a huge obstacle in keeping high quality, especially in express logistics. This paper proposes dynamic monitoring and quality assessment system (DMQAS) to reduce the quality loss of sweet cherries in express logistics. The DMQAS was tested and evaluated in three typical express logistics scenarios with “Meizao” sweet cherries. The results showed that DMQAS could monitor the changes of critical micro-environmental parameters (temperature, relative humidity, O2, CO2 and C2H4) during the express logistics, and the freshness prediction model showed high accuracy (the relative error was controlled within 10%). The proposed DMQAS could provide complete and accurate microenvironment data and can be used to further improve the quality and safety management of sweet cherries during express logistics.

Table grapes are very popular for their high nutritional and therapeutic value. The objective of this work was to study the effect of table grapes’ quality property in cold chain logistics for improving the transparency and traceability of table grapes’ cold chain logistics and ensuring the table grapes’ quality and safety. Temperature and relative humidity are monitored by adopting the wireless sensor network (WSN) as the fundamental network infrastructure and adaptive optimal weighted data fusion (AOW) for the adaptive data fusion. The cold chain process, firmness quality and adaptive data fusion of temperature and relative humidity were evaluated in an actual cold chain logistics. The results indicate that the WSN and AOW methods could effectively reflect the real-time temperature and relative humidity information and quality property, improve the transparency and traceability in the cold chain and ensure the preservation of the quality and safety of table grapes. The AOW performance analysis shows that the AOW, whose mean absolute error and mean relative error of the temperature data are 0.06 °C and 8.61% and relative humidity data are 0.12% and 0.23%, respectively, could fuse the sensor data accurately, efficiently and adaptively and meet the actual application requirements.

This paper describes a wireless real-time monitoring system (MS-BWME) to monitor the running state of pumps equipment in brine well mining and prevent potential failures that may produce unexpected interruptions with severe consequences. MS-BWME consists of two units: the ZigBee Wireless Sensors Network (WSN) unit and the real-time remote monitoring unit. MS-BWME was implemented and tested in sampled brine wells mining in Qinghai Province and four kinds of indicators were selected to evaluate the performance of the MS-BWME, i.e., sensor calibration, the system’s real-time data reception, Received Signal Strength Indicator (RSSI) and sensor node lifetime. The results show that MS-BWME can accurately judge the running state of the pump equipment by acquiring and transmitting the real-time voltage and electric current data of the equipment from the spot and provide real-time decision support aid to help workers overhaul the equipment in a timely manner and resolve failures that might produce unexpected production down-time. The MS-BWME can also be extended to a wide range of equipment monitoring applications.

Korla fragrant pear is prestigious for its special texture and unique flavor but suffers storage and supply chain difficulties for its deterioration-prone properties. In order to improve the storage quality of Korla fragrant pears during the whole cold chain from the orchard to the customers, the paper deeply researches multiple influencing factors of cold chain logistics and home storage of Korla fragrant pears with multi-sensor technology (MST), such as the temperature, relative humidity, concentrations of oxygen (O2), carbon dioxide (CO2) and ethylene (C2H4). Cold chain logistics are assessed by sensory evaluation and physiological index measurement, and home storage environments are classified by using back propagation neural network (BPNN) in both refrigerators and ordinary rooms. Experimental results show that the MST-based detectors can improve the accuracy of continuous sensor data acquisition, such that the preservation quality of Korla fragrant pears is effectively enhanced by data analysis on gas contents, firmness, pH, and total soluble solids. These results indicate that Korla fragrant pears stored in refrigerators have a higher acceptance for customers.

Tricholoma matsutake (T. matsutake) growing in Tibet is very popular for its high economic and medicinal value, but fresh T. matsutake has an extremely short shelf life. The shelf life of T. matsutake is complex, influenced by product characteristics, surrounding environmental conditions, and spoilage development. The objective of this work was to study the quality characteristics of fresh T. matsutake during its shelf life period in modified atmosphere packaging (MAP) conditions and establish its remaining shelf life prediction models in a cold chain. In this study, we measured and analyzed quality indicators of fresh T. matsutake, including hardness (cap, stipe), color, odor of sensory characteristics, pH, soluble solids content (SSC), and moisture content (MC) of physical and chemical characteristics under the temperature condition of 4 °C and relative humidity (RH) of 90%. The sensory evaluation results showed that the odor indicator in sensory characteristics was more sensitive to the freshness of T. matsutake. The changes of pH, SSC, and MC were divided into three periods to analyze the physiological changes of T. matsutake. The cap spread process could affect the changes of pH, SSC, and MC in period S1, and they changed gradually in period S2. In the period S3, they changed complicatedly because of deterioration. The remaining shelf life prediction model of T. matsutake was established by the back propagation (BP) neural network method to quantify the relationship between the quality indicators and the remaining shelf life. The shelf life characteristics are complex, which were optimized by correlation analysis. Significant benefits of this work are anticipated on the transportation and preservation of fresh T. matsutake to the market and the reduction of its losses in the postharvest chain.

Low-carbon production is one of the dominating issues in the sustainable development of the food industry with high energy consumption, especially in the table grapes cold chain. The aim of this paper is to propose a profit maximization strategy of table grapes cold chain by integrating the carbon footprint to improve the low-carbon production and sustainability of the cold chain system. The carbon footprint was evaluated by life cycle assessment (LCA) in entire cold chain system of table grapes, and the economic order quantity (EOQ) model was used to develop the profit maximization model with minimal carbon footprint and to maintain the optimal balance between stock and cost. The profit optimization performance, the sensitivity performance and the influencing factors of the decay rate, the carbon emission price, and the distance and carbon emission coefficient in refrigerated transport were analyzed according to the profit maximization model and the inventory data in actual cold chain investigated. The sensitivity performance analysis illustrated that the selling price had the highest sensitivity, and the carbon emission coefficient in storage had the lowest sensitivity. The comprehensive analysis results indicated that there is an optimal combination point between the economy and environment in actual cold chain, which not only reduced the carbon emission, but also had minimal impact on the profit in cold chain. The enterprises should integrate the carbon footprint cost into the profit maximization once the carbon emission tax is levied. The proposed strategy of the profit maximization with carbon footprint constraint is also suitable for improving profit maximization of other low-carbon supply chain applications.

Omega-3 polyunsaturated fatty acids (n-3 PUFAs) enriched eggs are popular for their enrichment of PUFAs, but their freshness and quality are prone to decline with time, particularly if storage conditions are not well maintained. Home storage is the last but a neglected important tach in eggs supply chain, but few studies are available on the egg storage in the home stage. This study aimed to evaluate the internal quality change of both n-3 PUFAs enriched and conventional eggs by simulating eggs stored at consumers’ home in refrigeration and under room temperature. The egg quality indices (Haugh unit, yolk index, weight, and albumen pH) were adopted and analyzed for both storage conditions. Wireless sensor network (WSN) was used for real-time monitoring of the temperature and humidity during storage. The results showed that temperature, humidity and time of storage all have an influence on the freshness of both n-3 PUFAs enriched and conventional eggs and there is no significant difference happened to n-3 PUFAs enriched eggs and conventional eggs. Refrigeration can decelerate the quality deterioration of both n-3 PUFAs enriched and conventional eggs and consumers should be educated about how to maintain the internal quality of eggs during home storage.

Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not fully consider current ambient influence. This is likely lead to a greater deviation in on-line oxygen level forecasting in real situations. Therefore, it is not advisable for the system to perform early warning and on-line air adjustment in delivery. In this paper, we propose a hybrid method and its implementation system that combines a gray model (GM (1, 1)) with least squares support vector machines (LSSVM) that can be used effectively as a forecasting model to perform early warning effectively according to the dynamic changes of oxygen in a closed system. For accurately forecasting of the oxygen level, the fuzzy C-means clustering (FCM) algorithm was utilized for classification according to the flatfish’s physical features—i.e., length and weight—for more pertinent training. The performance of the gray model-particle swarm optimization-least squares support vector machines (GM-PSO-LSSVM) model was compared with the traditional modeling approaches of GM (1, 1) and LSSVM by applying it to predict on-line oxygen level, and the results showed that its predictions were more accurate than those of the LSSVM and grey model. Therefore, it is a suitable and effective method for abnormal condition forecasting and timely control in the waterless live transportation of flatfish.

A non-destructive method to monitor lamb freshness was developed by analysing the impedance spectrum of lamb meat. As a result of lamb storing, its biological tissue structure and intracellular ionic conductivity change, which can be effectively monitored by impedance amplitude and phase angle. Initially, the coefficient of variation method was employed to identify three frequency ranges: low, medium, and high. These ranges were used to establish a predictive model for lamb freshness using Fisher linear discriminant analysis. Independent variables included impedance, phase angle, and measurement quality, while dependent variables encompassed fresh and aged lamb meat states. The subsequent evaluation of lamb meat morphology aimed to uncover how these independent variables impacted lamb freshness. Results highlighted the significance of impedance parameters in fresh lamb meat, particularly in the lowfrequency range. The discriminant analysis model demonstrated a robust predictive capacity, achieving a 94% accuracy rate for the discriminant function. The impedance variable had a significant impact on the model accuracy, and the introduction of independent variables improved the predictive accuracy of fresh and aged lamb meat by 22% and 15% respectively. Moreover, the study observed interconnected relationships among lamb freshness, water status, and impedance state changes. [ABSTRACT FROM AUTHOR]