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2023 Volume 3
Article Contents
Abstract
Introduction
Perennial grasses as natural allies of soil health
Perennial grasses as natural allies of soil biodiversity
Perennial grasses in climate change mitigation
Perennial grasses in combating invasive plants
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
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References
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ARTICLE   Open Access    

Development and design of an intelligent monitoring system for cold chain meat freshness

  • 1.

    College of Food Science and Technology, Henan Agricultural University, Zhengzhou 450002, Henan, China

  • 2.

    Henan International Joint Laboratory for Meat Processing and Safety, Zhengzhou 450002, Henan, China

  • 3.

    Henan Jiuyuquan Food Co., Ltd, Xinxiang 453500, Henan, China

More Information
  • Cold chain meat has become the main force of meat consumption in China due to its unique taste and rich nutrition. However, there are serious regulatory blind spots throughout the cold chain process, making it difficult to monitor the freshness quality and shelf life of cold chain meat in real time. Therefore, in this study, the dominant spoilage microorganism prediction model for cold chain meat was parsed to predict the logarithmic value of the dominant spoilage microorganism in real time based on temperature information, which was compared with the freshness quality threshold to derive real-time quality information. This paper proposes to develop the system with ASP.NET three-tier architecture as the development framework and .NET Framework 4.6.2 as the development environment. Combining MySQL 5.7.31 application database, Vue 2.6 and Apache ECharts front-end technology development a intelligent monitoring system for cold chain meat freshness. The system has five core application modules: multi-role login module, big data visualization and analysis module, product information management module, product freshness quality and shelf life real-time monitoring and warning module, and system setting module, to monitor the freshness quality and shelf life of cold chain meat products at all stages, real time. The development of this system can realize the real-time supervision of the quality of cold chain meat products in the whole industrial chain of processing, storage, transportation and sales, timely detect product abnormalities, provide technical support for food enterprises, and provide food safety assurance for consumers.

  • Introduction

    Perennial grasses [e.g., switchgrass (Panicum virgatum), big bluestem (Andropogon gerardii), indiangrass (Sorghastrum nutans), little bluestem (Schizachyrium scoparium), Maasai love grass (Eragrostis superba), and bush ryegrass (Enteropogon macrostachyus)] are plant species that live for more than two years with deep root systems and the capacity to grow in a variety of climates[1−5]. Although often overlooked, perennial grasses serve an important role in ecosystems, particularly in maintaining soil health and biodiversity, climate change mitigation, and combating alien invasive plants (AIPs)[1,4]. Thus, they are simply natural allies for soil biodiversity conservation, invasive plant management, and climate change mitigation[6,7]. The deep root systems of perennial grasses help soil structure by improving aeration, increasing water infiltration, and lowering soil erosion[1,5]. Also, their extensive root network supports the stability of the soil, making it less susceptible to degradation and encouraging a healthier ecology overall[1,8]. Further, they play a key role in the nutrient cycle by maximizing nutrient utilization and minimizing leaching[9,10]. In addition, perennial grasses contribute organic matter to the soil through biomass, which decomposes over time and enriches the soil with critical nutrients[11−13]. This process improves soil fertility, increasing productivity for other plant species, and agricultural activities[9,12].

    IAPs, also known as non–native or exotic species, are plants introduced to an ecosystem where they do not naturally occur[14−16] and pose a severe ecological, economic, and social impacts[17,18]. Unlike native species, IAPs often lack natural enemies and diseases in their new environments, allowing them to proliferate unrestrictedly[19,20]. Their invasions lead to the displacement of native flora as they outcompete native species for resources i.e., light, water, and nutrients[21,22]. As a result, causing a reduction in biodiversity and the alteration of ecosystem functions, often forming dense monocultures that hinder the growth of other plants and disrupt habitats for native wildlife[23,24]. Moreover, IAPs can alter soil chemistry and hydrology thereby negatively impacting soil biodiversity[6,7,15,25]. IAPs can further impact human health by increasing allergens and providing a habitat for disease vectors[15]. Efforts to manage IAPs typically involve early detection, prevention, and rapid response, such as biological control, mechanical removal, and herbicide treatment[19,25,26]. Although the role of perennial grasses in combating IAPs has been seldom investigated, available studies show that effective management requires integrated eco-friendly management incorporating competitive native perennial grasses to suppress IAPs[6,8,15,27].

    Furthermore, perennial grasses are ecologically significant because they enhance species diversity and soil biodiversity i.e., living forms found in soil, which includes microorganisms (bacteria and fungi), mesofauna (nematodes and mites), and macrofauna, i.e., earthworms and insects[28−32]. This diversity is critical to ecosystem function and plays an important role in nutrient cycling, soil structure maintenance, and plant growth promotion[29,30]. They contribute to nutrient-cycling activities by breaking down organic materials into simpler compounds that perennial grasses and other plants can consume, decomposing dead plants and animals, and releasing nutrients back into the soil, thus increasing soil fertility[32−34]. Further, perennial grasses also promote plant-soil symbiotic relationships such as mycorrhizal associations and rhizobium symbioses, which improves soil health and plant growth[29]. These benefits are enhanced by perennial grasses' root exudates, which support both soil microbial diversity and activity, resulting in a more dynamic and resilient soil environment[1]. However, extreme weather events, such as floods and droughts, as well as IAPs can cause soil organism loss and structural damage, thereby impeding the roles of soil organisms[35−37]. Further, increased temperatures can disrupt microbial activity and nitrogen cycling mechanisms, impacting soil health, and productivity[37,38]. Addressing these challenges needs long-term integrated management approaches that maintain natural ecosystems and increase soil biodiversity, as well as IAP control and climate change mitigation. For instance, promoting the use and maintaining the diversity of perennial grasses in rangelands and agricultural habitats[1,39,40].

    Climate change which is the average change in the earth's temperature and precipitation patterns can also disrupt the delicate balance of soil biodiversity[37,41]. It is driven primarily by human activities i.e., burning fossil fuels, deforestation, and industrial processes which lead to an unprecedented rise in greenhouse gases, such as carbon dioxide and methane in the atmosphere[37,42]. Often the earth's surface temperature increases concomitantly with these greenhouse gasses[41]. Increased temperatures contribute to sea-level rise, more frequent and intense heatwaves, wildfires, and droughts affecting biodiversity, water supply, and human health. Changes in precipitation patterns also lead to extreme weather events i.e., hurricanes, floods, and heavy rainfall, disrupting ecosystems and human societies[37]. It also negatively impacts biodiversity, as species must adapt, migrate, or face extinction due to altered habitats and shifting climate zones[36]. Addressing climate change requires global cooperation and robust policies aimed at reducing greenhouse gas emissions which include the use of eco-friendly approach, for instance, keeping the environment intact with native plants i.e., perennials grasses[43]. Perennial grasses (e.g., turfgrass) are considered potential for mitigating the effects of climate change because they have a high carbon sequestration capacity, storing carbon in both soil and aboveground biomass[44−46]. They can contribute to reducing greenhouse gas levels by absorbing and storing carbon dioxide from the atmosphere in their roots and tissues, thus helping to mitigate climate change[44]. Furthermore, their capacity to minimize greenhouse gas emissions through reduced tillage and increased nitrogen use efficiency makes them an important component of habitat restoration to mitigate climate change impacts[43].

    Consequently, native perennial grasses have been recommended by various previous studies to be used for habitat restoration, including rangelands, because of their physiological and morphological traits, which have shown great potential to improve soil health and biodiversity, mitigate climate change, and combat IAPs[1,5,8,27,40,47]. By their competitive and morphological traits, several perennial native grass species found in African rangelands (e.g., African foxtail grass (Cenchrus ciliaris), horsetail grass (Chloris roxburghiana), rhodes grass (Chloris gayana), E. superba, and E. macrostachyus) and P. virgatum, S. nutans, S. scoparium, and A. gerardii in North America have been tested and recommended for ecological restoration[1−5].

    Perennial grasses as natural allies of soil health

    Preceding studies have demonstrated that perennial grasses have the potential to improve soil health and structure in rangelands and protected habitats[1,48−50]. Unlike annual plants, which have shallow root systems, perennial grasses can penetrate deep into the soil, sometimes reaching depths of several meters as they have deep and extensive root systems[1,7,40]. These deep roots create channels that enhance soil aeration, allowing for better oxygen flow and water infiltration, thereby preventing soil compaction[49]. Perennial grasses contribute to soil stability by binding soil particles together, thereby preventing erosion (Fig. 1), which is important in ecosystems or habitats prone to heavy rainfall or wind[48,49]. This stabilization effect reduces the loss of topsoil, which contains the highest concentration of organic matter and nutrients essential for plant growth[44]. Moreover, perennial grasses have been reported to be efficient in nutrient cycling, a critical process for maintaining soil fertility[49]. For instance, their deep roots access nutrients in deeper soil layers, which might be unavailable to shallow-rooted plants[49,50]. These nutrients are then brought to the surface and incorporated into the plant biomass. When the grasses die back or shed leaves, these nutrients are returned to the soil surface as organic matter, making them accessible to other plants[32,49,51]

    Figure 1.  Diagram illustrating the multifaceted benefits of perennial grasses and their interconnected roles in promoting soil health, biodiversity, IAPs control, climate change mitigation, water retention, erosion control, and habitat provision. The arrows illustrate the complex interactions and synergies among these components, emphasizing the comprehensive ecological contributions of perennial grasses. The central position of perennial grasses highlights their pivotal role in these areas. This visual representation emphasizes how perennial grasses contribute to and enhance various aspects of ecosystem health and stability.
    DownLoad: Full-Size Img PowerPoint

    Furthermore, perennial grasses enhance soil health and structure (Fig. 1), improving the soil's ability to retain water and withstand extreme weather events i.e., heavy rainfall and floods[44,49]. Their extensive root networks stabilize the soil, reducing erosion and runoff (Fig. 1), which are critical for maintaining soil fertility and agricultural productivity under variable climatic conditions[51]. The continuous growth and decay cycle of perennial grasses contributes to the slow but steady release of nutrients[52]. This slow release is beneficial for maintaining a stable nutrient supply, as opposed to the rapid nutrient depletion often seen in soils dominated by annual crops[50]. This process also helps in reducing nutrient leaching, where nutrients are washed away from the soil profile, particularly nitrogen, which is critical for plant growth[49]. Perennial grasses help to reduce N2O emissions; excess nutrients can lead to increased N2O emissions[10,11,53]. They also contribute significantly to the soil organic matter, which is a key component of soil health[52]. Organic matter consists of decomposed plant and animal residues, which improve soil structure, water retention, and nutrient availability[50,52]. The biomass produced by perennial grasses, both above and below ground, adds a substantial amount of organic material to the soil[52]. As the plant material decomposes, it forms humus, a stable form of organic matter that enhances soil structure by increasing its capacity to hold water and nutrients[52,54]. This is particularly important in dry regions e.g. in Africa, where water retention can be a limiting factor for crop growth[49]. The organic matter also provides a habitat and food source for a diverse array of soil organisms, including bacteria, fungi, and earthworms, which further contribute to soil fertility through their biological activities[43,52,54].

    Perennial grasses as natural allies of soil biodiversity

    Perennial grasses play a crucial role in enhancing soil biodiversity (abundance and diversity) and activities within the soil[31,32,51,54]. They provide critical habitats for soil fauna i.e., earthworms, nematodes, and arthropods (Fig. 1)[32,54]. Their complex root systems create a stable environment that supports a wide range of soil organisms[55]. Also, the root systems of perennial grasses exude a variety of organic compounds, including sugars, amino acids, and organic acids, which serve as food sources for soil biodiversity[54]. This continuous supply of root exudates and a stable environment fosters a diverse macro and microbial community, which is essential for maintaining soil health[31,43,54]. For instance, it was reported by Smith et al.[54] that in areas with abundant perennial grasses, a high soil macrofaunal biodiversity (i.e., Lumbricidae, Isopoda, and Staphylinidae) was observed. They further asserted that these grasses were beneficial to soil macrofauna as they increased the abundance and species diversity of staphylinid beetles, woodlice, and earthworms. In addition, Mathieu et al.[56] reported the influence of spatial patterns of perennial grasses on the abundance and diversity of soil macrofauna in Amazonian pastures. These findings suggest that well-managed perennial grasses are vital in enhancing soil macro and microbes in ecosystems[54−56].

    These soil organisms perform various functions, including decomposing organic matter, fixing atmospheric nitrogen, and suppressing soil-borne diseases[29,30,32]. A diverse soil macro and microbial community can enhance nutrient cycling, making nutrients more available to plants[30,56]. Enhanced microbial diversity by perennial grasses contributes to the suppression of pathogens through competition and the production of antimicrobial compounds, thus promoting plant health[32]. They also help in maintaining soil structure, fertility, and overall ecosystem function[32]. For instance, earthworms, often referred to as 'ecosystem engineers', augment soil structure by creating burrows that improve aeration and water infiltration in perennial grass communities[31,51]. Their activity also helps mix organic matter into the soil, promoting nutrient cycling[31,32]. Nematodes and arthropods which feed on perennial grass species contribute to the decomposition process, breaking down organic matter and releasing nutrients that are vital for plant growth[31,54]. The presence of a diverse soil fauna community is indicative of a healthy soil ecosystem, which is more resilient to environmental stresses and disturbances[31].

    Furthermore, perennial grasses are considered as being instrumental in promoting plant-soil symbiotic relationships[43,54], which are crucial for plant health and soil fertility. One of the most well-known symbiotic relationships is between plants and mycorrhizal fungi[29,33]. These fungi colonize plant roots and extend their hyphae into the soil, increasing the root surface area and enhancing the plant's ability to absorb water and nutrients, particularly phosphorus. The relationship between perennial grasses and mycorrhizal fungi is mutually beneficial. The fungi receive carbohydrates produced by the plant through photosynthesis, while the plant gains improved access to soil nutrients and increased resistance to soil-borne pathogens[30]. This symbiotic relationship is particularly important in nutrient-poor soils, where mycorrhizal associations can significantly enhance plant growth and survival. Additionally, perennial grasses promote other beneficial plant-soil interactions, such as those involving nitrogen-fixing bacteria. These bacteria form nodules on the roots of certain perennial grasses, converting atmospheric nitrogen into a form that plants can use[29,30]. This process is essential for maintaining soil fertility, especially in ecosystems where nitrogen is a limiting nutrient.

    Perennial grasses in climate change mitigation

    Perennial grasses are increasingly recognized for their role in climate change mitigation (Fig. 1)[43,44,57]. They can sequester carbon, reduce greenhouse gas emissions, and adaptation to climate variability[58,59]. Their deep root systems and grass-like characteristics make them highly effective in capturing and storing carbon[44]. These roots can penetrate deep into the soil and store carbon for extended periods[59]. Because of this, perennial grasses show potential to enhance the resilience of ecosystems to changing climatic conditions[44]. The roots of perennial grasses are more extensive and persistent compared to annual crops, allowing for greater carbon storage both in the root biomass and the soil[45,46,60]. This process of carbon sequestration involves capturing atmospheric carbon dioxide (CO2) through photosynthesis and storing it in perennial grass tissues (e.g., turfgrasses) and soil organic matter[44−46]. Preceding studies have further shown that perennial grasses can sequester substantial amounts of carbon, contributing to the reduction of atmospheric CO2 levels[45,61]. In addition to carbon sequestration, perennial grasses can reduce greenhouse gas emissions through various mechanisms[43]. One of the primary ways is by reducing the need for frequent soil tillage, which is common in annual cropping systems. Tillage disrupts soil structure, releases stored carbon as CO2, and increases soil erosion[58,61]. Thus, with their long lifespan, perennial grasses can reduce the need for tillage, thereby minimizing CO2 emissions from soil disturbance[43,58].

    Moreover, perennial grasses can improve nitrogen use efficiency, reducing the need for synthetic fertilizers that are a major source of nitrous oxide (N2O) emissions—a potent greenhouse gas[53,62]. Their deep root systems enable them to access nutrients from deeper soil layers, reducing nutrient leaching and the subsequent emissions of N2O[53]. By optimizing nutrient use, perennial grasses contribute to lower greenhouse gas emissions associated with agricultural practices[63]. Also, perennial grasses are crucial for adapting to climate variability[44]. Their deep root systems allow them to access water from deeper soil layers, making them more resilient to drought conditions compared to annual crops[44]. This water use efficiency helps maintain plant growth and productivity even during periods of water scarcity, which are expected to become more frequent with climate change[49]. In general, perennial grasses support soil biodiversity conservation through habitat provision, climate change mitigation, and promoting ecosystem resilience[58]. Besides, these grasses are crucial for ecosystem stability and productivity, particularly in the face of climate change, and ensure the continued provision of ecosystem services (Fig. 1).

    Perennial grasses in combating invasive plants

    Previous studies have shown that IAPs pose significant threats to ecosystems worldwide by displacing native species, altering habitats, and disrupting ecosystem functions and services[15,20,23,64]. Among the integrated management techniques to combat IAPs involves the use of competitive native plants (Fig. 1) such as perennial grasses[6,7,40]. These grasses, which live for more than two years with robust root systems, growth, and resilience to varying environmental conditions, offer several advantages in controlling IAPs[1,48]. Their competitive growth patterns and ability to restore and maintain native plant communities, and establish, and thrive in diverse habitats make them formidable competitors against invasive plants[1]. One of the primary ways perennial grasses combat IAPs is through competition for resources[48]. Their extensive root systems allow them to efficiently absorb water and nutrients, outcompeting IAPs that typically have shallower roots. This competitive edge limits the resources available to IAPs, inhibiting their growth and spread. For instance, species like P. virgatum and big A. gerardii are known for their deep roots, which can reach depths of up to 10 feet (3 m), providing them with a significant advantage over many IAPs[8,48]. They can also outcompete IAPs through their competitive growth patterns including quick establishment and forming dense canopies that shade out AIPs[1,8]. For example, native perennial grasses like S. nutans and S. scoparium have been shown to effectively compete with invasive species i.e., spotted knapweed (Centaurea stoebe) by limiting light availability and space for growth[8,48].

    Moreover, using their extensive root systems that stabilize the soil, perennial grasses can prevent erosion and invasions of IAPs[44]. Invasive plants i.e., carrot weed (Parthenium hysterophorus), cheatgrass (Bromus tectorum), and kudzu (Pueraria montana) can rapidly colonize disturbed soils, leading to severe erosion problems[20,65,66]. However, perennial grasses i.e., P. virgatum and big A. gerardii have been found to reduce erosion and creating an unfavorable environment for IAPs to establish owing to their deep fibrous root systems that hold the soil in place. Perennial grasses can also modify the microenvironment in ways that make it less conducive for IAPs[1,27,66]. They produce dense root mats that strengthen the organic matter content and soil structure, improving the fertility and health of the soil. The diversity and growth of native plant species is aided by improved soil conditions, which further promote biodiversity and inhibit IAPs by strengthening ecosystem resilience[48].

    Additionally, the use of perennial grasses in restoration has shown promising results in reclaiming areas overrun by IAPs and maintaining native plant communities that are disrupted by IAPs[8,66]. By planting a mix of native perennial grasses, land managers can restore ecological balance and prevent the re-establishment of IAPs[26]. These grasses provide long-term ground cover and habitat for wildlife, contributing to the overall health and stability of the ecosystem[1,8,54]. By reintroducing native perennial grasses into areas (e.g., rangelands and protected habitats) dominated by IAPs, ecosystems, and their biodiversity can be restored to their earlier conditions[27,39,67]. For instance, the use of native perennial grasses has been successful in restoring prairie ecosystems that were previously overrun by IAPs i.e., leafy spurge (Euphorbia esula) and purple loosestrife (Lythrum salicaria)[68]. Another important example of using perennial grasses to mitigate IAPs is the restoration of tallgrass prairies in the Midwest United States[8,66]. These prairies were historically dominated by native perennial grasses i.e., S. nutans and S. scoparium, however IAPs i.e., smooth brome (Bromus inermis) and reed canarygrass (Phalaris arundinacea) displaced them, leading to biodiversity loss and altered ecosystem functions[8,66,68]. Studies show that following the restoration of these invaded habitats using perennial grasses, native grasses successfully reestablished and reduced IAPs and promoting native biodiversity[66,67]. In addition, another notable example is the use of perennial grasses to restore riparian areas which were heavily invaded and impacted by IAPs i.e., giant reed (Arundo donax) and saltcedar (Tamarix spp.)[67,69]. Planting native perennial grasses like western wheatgrass (Pascopyrum smithii) and creeping wildrye (Elymus triticoides) in these areas helped to stabilize the soil, reduce erosion, and suppress IAPs, leading to improved riparian habitat quality and ecosystem resilience[18,66,67,69].

    Therefore, competitive suppressive perennial grasses are a crucial tool in the fight against IAPs and other weeds. Their competitive abilities, contributions to soil health, and role in ecosystem restoration makes them invaluable in managing and alleviating the impacts of IAPs. As research continues, the potential for perennial grasses to be integrated into broader IAP strategies remain significant, promising a more sustainable and ecologically sound approach to preserving native biodiversity.

    Conclusions

    Perennial grasses are pivotal in enhancing soil biodiversity, mitigating climate change, and combating IAPs. Their deep root systems stabilize soils, support diverse soil faunal communities, and improve water retention. Besides, they are important grasses in sequestering carbon, reducing greenhouse gas emissions, suppressing IAPs, and supporting the reestablishment of native plant communities. Integrating perennial grasses into protected areas and rangelands management practices could offer a sustainable solution to pressing environmental challenges including invasions of IAPs. Stakeholders i.e., farmers, conservationists, ecologists, and land managers are advised to use perennial grass systems in their restoration practices, crop rotations, and pasturelands to enhance soil health and resilience. They are further commended to use perennial grasses for erosion control and to improve soil structure and fertility. Policymakers could develop and support policies that incentivize the use of perennial grasses in agricultural and restoration projects. Researchers, they are advised to conduct studies to quantify the long-term benefits of perennial grasses on soil biodiversity and climate change mitigation. Additionally, they can develop country or region-specific guidelines for the effective use of perennial grasses in different ecosystems. Hence, by integrating perennial grasses into our environmental stewardship strategies, we can ensure a thriving, balanced ecosystem capable of withstanding the impacts of climate change and IAPs.

    Author contributions

    The author confirms sole responsibility for the following: review conception and design, and manuscript preparation.

    Data availability

    Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

    Acknowledgments

    The author thanks all the colleagues who reviewed and proofread this article. This work was not supported by any funding agency.

  • The author declares that there is no conflict of interest.

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  • Cite this article

    Niu Z, Yang J, Zhu Y, Xu L, Yang S, et al. 2023. Development and design of an intelligent monitoring system for cold chain meat freshness. Food Materials Research 3:2 doi: 10.48130/FMR-2023-0002
    Niu Z, Yang J, Zhu Y, Xu L, Yang S, et al. 2023. Development and design of an intelligent monitoring system for cold chain meat freshness. Food Materials Research 3:2 doi: 10.48130/FMR-2023-0002
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ARTICLE   Open Access    

Development and design of an intelligent monitoring system for cold chain meat freshness

  • 1.

    College of Food Science and Technology, Henan Agricultural University, Zhengzhou 450002, Henan, China

  • 2.

    Henan International Joint Laboratory for Meat Processing and Safety, Zhengzhou 450002, Henan, China

  • 3.

    Henan Jiuyuquan Food Co., Ltd, Xinxiang 453500, Henan, China

  • Received: 14 December 2022
  • Accepted: 31 January 2023
  • Published online: 28 February 2023
Food Materials Research  3,  Article number: 2  (2023)  |  Cite this article

Abstract: Cold chain meat has become the main force of meat consumption in China due to its unique taste and rich nutrition. However, there are serious regulatory blind spots throughout the cold chain process, making it difficult to monitor the freshness quality and shelf life of cold chain meat in real time. Therefore, in this study, the dominant spoilage microorganism prediction model for cold chain meat was parsed to predict the logarithmic value of the dominant spoilage microorganism in real time based on temperature information, which was compared with the freshness quality threshold to derive real-time quality information. This paper proposes to develop the system with ASP.NET three-tier architecture as the development framework and .NET Framework 4.6.2 as the development environment. Combining MySQL 5.7.31 application database, Vue 2.6 and Apache ECharts front-end technology development a intelligent monitoring system for cold chain meat freshness. The system has five core application modules: multi-role login module, big data visualization and analysis module, product information management module, product freshness quality and shelf life real-time monitoring and warning module, and system setting module, to monitor the freshness quality and shelf life of cold chain meat products at all stages, real time. The development of this system can realize the real-time supervision of the quality of cold chain meat products in the whole industrial chain of processing, storage, transportation and sales, timely detect product abnormalities, provide technical support for food enterprises, and provide food safety assurance for consumers.

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    INTRODUCTION

    • With the improvement of residents' living standards, cold chain meat has become the mainstream of people's daily consumption due to its unique taste, rich nutrition, high food safety and other advantages[1]. However, there are some problems in the function of processing, storage, logistics and sale of meat, such as the contamination of spoilage microorganisms and food borne pathogens, which is at present, the biggest risk faced by cold chain meat products[2]. Temperature is the most critical factor affecting the spoilage of meat in the cold chain[3]. At present, the low temperature of the cold chain system can inhibit the growth and reproduction of microorganisms[4], effectively delaying their spoilage and ensuring the safety of consumers[5]. There are some problems in the process of cold chain transportation in China, such as complex environments and lack of supervision[6]. This will cause problems such as frequent temperature fluctuations during transportation and difficulty in effective supervision[7]. Eventually, it will lead to cross-contamination of products in the process of logistics and sales. Temperature fluctuations can accelerate the growth and reproduction of microorganisms, speeding up the process of meat spoilage and seriously threatening the economic income of meat enterprises and consumer health[8]. Therefore, how to monitor the freshness and retain shelf life of cold chain meat real time is one of the key issues that needs to be solved.

      For information systems for cold chain meat supervision, the main focus is traceability system research, such as the study by Eskil Forås et al. based on EPCIS and RFID technology to supervise the real-time temperature information of cold chain meat in the process of supply chain and improved the traceability of cold chain meat[9]; He et al. developed a whole-chain food safety supervision cloud platform based on QoS technology, which enables food safety to form a whole-chain closed loop from the source to the end, realizing process tracking, risk warning and traceability of the whole food chain[10]; Tang et al. built a cold chain food quality and safety supervision platform based on blockchain technology to effectively supervise and trace abnormal monitoring indicators and products[11]. Most information systems focus on the post-facto traceability of abnormal products, which is not timely and lagging behind. Compared to the post-facto traceability of abnormal products, it is more important to monitor and warn the products in real time, which is still lacking, and is not conducive to real-time monitoring of products by enterprises, consumers and regulatory authorities. In terms of predictive model database construction and expert systems, there are already some mature three-tier predictive microbial model expert systems at home and abroad, such as the Pathogen Modeling Program (PMP) developed by the US Department of Agriculture[12]; Food Micromodel (FM) developed by the UK Ministry of Agriculture, Fisheries and Food[13]; ComBase Predict developed by the UK Institute of Food Research, the US Department of Agriculture Research Centre, etc. ComBase Predictor (CP), developed jointly by the UK Food Research Institute and the US Department of Agriculture[14]. However, in terms of industrial food applications, there is an urgent need to solve the problem of how to make better use of expert systems and microbial prediction model databases to achieve practical industrial and intelligent regulatory applications.

      In view of the above problems, this paper developed an intelligent monitoring system for cold chain meat freshness which is effectively combined with industrial application. The system is developed with B/S architecture and divided into five core functional modules, which are multi-role login module, big data visualization display and analysis module, product information management module, product quality real-time supervision module and system user management module. The system is designed to monitor the freshness of cold chain meat in the whole industrial chain in real time, to achieve real-time monitoring of product quality by managers, business users and consumers, and to ensure the safety of cold chain products.

    MATERIALS AND METHODS

      Systems analysis

      Requirement analysis

    • Cold chain meat in the storage, processing, logistics and sales process, due to the implementation of cold chain system standards are not in place, the production and circulation and sales process of the cold chain logistics system is not sound, and the transport environment is complex, involving a wide range of unexpected circumstances and difficult to control, often leading to its cold chain meat spoilage. On the other hand, the freshness and quality of cold chain meat during the logistics and sales process is difficult to obtain directly, resulting in a delay in handling abnormal information, causing waste and even affecting the health of consumers.

      Therefore, it is possible to provide business users with accurate, real-time product quality analysis by developing a cold chain meat wisdom supervision system that predicts cold chain meat freshness indicators through dynamic collection of product-related environmental information and displays freshness and shelf life in real time in the form of statistical charts, to quickly deal with abnormalities, reduce business costs and ensure food safety.

      System development module analysis

    • The overall system development module is divided into seven parts, namely the central control module, information collection module, data storage module, visualization and analysis module, intelligent computing module, quality supervision module and abnormality warning module. The system module diagram is shown in Fig. 1.

      Figure 1. 

      System development module diagram.

      1) The central control module is the core module of the system, sending automatic operation instructions to the system data acquisition, transmission, computing and other modules, completing different tasks by calling different modules, coordinating the various collaborative functions between modules, and at the same time allowing module expansion of the system, leaving the application expansion interface for system perfection.

      2) The information collection module is the data base module of the system, through multi-functional environmental sensors to collect the environmental information of the whole industrial chain of cold chain meat, real-time data transmission, to provide data support for system computing and big data analysis.

      3) The data storage module is an important module of the system, which stores real-time monitoring information from environmental sensors and is called by other computing and supervision modules to complete the functions of cold chain meat wisdom supervision. At the same time, it integrates the multi-source heterogeneous data generated by various links in the whole industry chain to solve the problem of data silos generated by multiple systems. The data storage module stores the cold chain meat quality supervision information generated in the system operation to provide data support for the next big data analysis and wisdom warning.

      4) The visualization and analysis module is the display module of the system, which displays and interacts with the key information collected by the system, the quality information obtained through computing and the abnormal warning information in the form of a visualization user interface, and provides the usual interactive functions of the system, integrating the functions of the underlying data storage and calling and intelligent computing, and finally displaying and interacting with them in the form of statistical charts.

      5) The intelligent computing module is the signal interaction module of the system. Through the design of the computing logic between the system modules, the interaction relationship between different modules is clarified, and the intelligent signal interaction function is completed for the interaction flow of different data streams, so that the system modules can interact efficiently and complete the command computing function within the system.

      6) The quality control module is the core business module of the system. Through information collection, data processing and arithmetic, the system can obtain the input parameters required for quality control in real time, and the input parameters are encapsulated by the underlying arithmetic functions in the quality control module to output quality control indicators, such as cold chain meat freshness level and shelf life information, which provide the basis for user decision making.

      7) Abnormal warning module is the main function module of the system, through where quality supervision can obtain cold chain meat various real-time indicators, through the setting of abnormal warning indicators and thresholds, the quality supervision output parameters can be monitored in real time, monitoring indicators exceed the set threshold, then through a variety of forms of abnormal alarms, assist managers with timely intervention, reduce food safety risks and enterprise losses.

      System design

      The design of multi-tier system architecture for application requirements

    • Through the demand analysis of this system, from the perspective of real-time supervision of cold chain meat quality, this system is developed using B/S structure (Brower/Server, browser/server mode)[15]. The system is divided into a four-layer structure from the perspective of the development technology architecture, which are the interaction layer, user layer, application layer and data layer. The overall system architecture is shown in Fig. 2.

      Figure 2. 

      Cold chain meat intelligent supervision system architecture.

      1) The interaction layer, i.e. the development of the GUI (Graphical User Interface), is the bridge between the application layer and the user layer. Through the B/S structure combined with the visualization of the web front-end, the user logs in to the system from the browser side and carries out the corresponding big data viewing, product information query, product quality monitoring, warning information acceptance and abnormal situation handling operations in the system, and after the front-end interaction page obtains the corresponding form data, it is transmitted to the server for processing and adds, deletes, changes and checks the underlying database, and then outputs the operation results to the front-end page in the form of visualization. The operation results are then output to the front-end page in visual form to complete the interactive function.

      2) The user level is the actual user of the system. Through the analysis of the system requirements, it is determined that the users of the system are divided into three roles, namely administrator, business user and consumer. Different roles have different requirements for the use of the system, where the administrator can view all users of the system, edit the use rights of each role, set the relevant parameters of the system, and is the highest level user of the system. Enterprise users can view the big data statistics of their products in the system, product information and product quality dynamics monitoring, etc. Consumers can log in to the system and scan the QR code of purchased products to view the current quality and shelf life of the products, assisting consumers in their product purchases.

      3) The application layer is the core layer of the system, which can realize the overall business logic of the cold chain meat wisdom supervision system and achieve the overall operation of the required functions of the system, including big data statistics, real-time freshness status calculation, product shelf life calculation and user interaction logic during operation, and is the core layer of the system operation, with multi-functional interfaces, multi-source heterogeneous data processing and other important functions.

      4) The data layer provides data services for the application layer, and the back-end database technology uses MySQL database management system for data management. The data layer uses ADO.NET, data API information bi-directional transmission technology for data addition, deletion and checking operations, and the database for operations is divided into system database, model algorithm database, enterprise user database and other required databases. This data layer data source is multi-source heterogeneous data, the main data come from sources such as meat enterprises, cold chain systems, shop environments and sensor systems. The multi-level data sources help the system to obtain comprehensive and accurate information.

      System core business process design

    • The core business process of the system is designed so that the system can complete the intelligent supervision function of cold chain meat. The system firstly goes through information collection, collecting multiple sources of heterogeneous data such as order information, factory environment information, cold chain logistics information and shop environment information. The collected data is categorized and stored in a database and awaits operations such as retrieval, recall and editing. The intelligent algorithm automatically acquires the real-time collected data, selects different models for different products, inputs the corresponding parameters for automatic operation and outputs the results in a classified manner. After the output results are passed through the product monitoring module, the current product quality situation is resolved and the real-time supervision function is completed. The abnormal warning module monitors the product quality in real time and provides an abnormal warning if the monitoring threshold is exceeded. Ultimately, the information and data analysis statistics are displayed and interacted with on the visualization interface, making it easy for managers to operate the system. The core business process of the system is shown in Fig. 3.

      Figure 3. 

      System core business processes.

      System database design

    • The system database is designed according to the SQL (Structured Query Language) standard based on the overall business process and the data flow generated by the system. The development process includes defining the relational schema, creating data tables, and adding, querying, modifying, and deleting data tables, etc. The database should have efficient and flexible data manipulation functions. In the database design, in order to facilitate the use of functional modifications, there is also a need for good secondary development and extension functions.

      Based on the actual needs of this system, the core data tables created are: big data statistics table, product category table, product information table, shop information table, shop product monitoring table, user table, system operation log table, and system general data table. These core data tables complete the standardization of heterogeneous data from multiple sources for this system's products, storing detailed information on product freshness and remaining shelf life during storage, processing, logistics and sales.

      System function design

    • According to the system requirements, the core application functions of the system are set up in five modules, namely: multi-role login module, data visualization module, product management module, product quality supervision module and system setting module, and the system function design diagram is shown in Fig. 4.

      Figure 4. 

      System core function diagram.

      1) Multi-role login module. There are three roles: Administrator, Enterprise User and Consumer. Each role has different requirements for the use of the system, different setting permissions and different interaction pages. The Administrator role is the highest level role in the system. This role allows you to view all registered users of the current system, view information and permission settings for each enterprise user, set global settings for common functions of the system, and update and maintain the system. Enterprise users can view the product information and real-time status of all cold chain meat products of their enterprises. By viewing the real-time monitoring information of products, we can handle abnormalities and reduce losses in time. The consumer role can scan the product QR code information to view the quality history of the product and provide scientific advice on purchasing behavior.

      2) Data visualization module. It is divided into product shipment, product qualification rate, microbial real-time monitoring chart, abnormal data warning and other functions, the most concerned product data for enterprise users to carry out data statistics, and presented in bar graphs, line graphs, pie charts and other visual statistical charts, so that enterprise managers can understand the product situation of this enterprise in a timely manner, to assist enterprises in decision-making and abnormal situation emergency and processing.

      3) Product management module. It was divided into product category management, product production information management, product status management and other functions. It can complete the batch import of enterprise products, product category editing operation, product information retrieval and management, cold chain logistics product freshness quality monitoring, product status real-time view and other functions, so that the enterprise management personnel of this enterprise product specific information for unified management.

      4) Product quality monitoring module. By collecting information on the environment in which the cold chain meat products are stored, processed, logistically and sold, we can obtain key environmental information. After analyzing the environmental information, real-time spoilage microbial logarithm values are calculated by freshness and remaining shelf life algorithms. The calculated results are compared with the freshness threshold to derive real-time product freshness and remaining shelf life. It also provides alarms for abnormal situations in the monitoring process, so that enterprise managers can deal with abnormal situations in a timely manner.

      5) System settings module. This is divided into monitoring settings, system user rights settings, system user settings and operation log viewing. Monitoring settings can adjust the monitoring indicators in the system to make it more in line with the application requirements. The system user rights setting can set up different functions for different roles of the system, so that the system functions can meet the needs of different roles. The system user settings function allows you to view the information of all current system users and system usage, and to improve and upgrade the system functions according to the system suggestions submitted by users.

      System implementation

      System database development
      System basic database development

    • This system is developed by using the relational database management system MySQL 5.7.31. MySQL is one of the best RDBMS (Relational Database Management System) applications for web applications[16]. By storing data in different tables and linking tables to each other, the speed of data storage and recall can be increased and the flexibility of the database greatly improved[17]. The development process was carried out in conjunction with Navicat for MySQL 15.0.22 visualization software, a fully featured graphical database development software that provides a powerful graphical interface for MySQL database management, development and maintenance.

      Using MySQL to create a cold chain meat system database, as the general database of the cold chain meat wisdom supervision system, according to the system requirements, through the E-R diagram (entity-relationship diagram), to build the data relationship of different tables in the database, the cold chain meat wisdom supervision system database to build 8 data tables, respectively, are big data statistics table, product type table, product information table, shop information table, shop product monitoring table, user table, system operation log table, system general data table, the above data tables store all the interaction data of the system for transmission through the data transmission interface API, so that the system computing module and the front-end visualization page can quickly call and edit the data. The database structure is shown in Fig. 5.

      Figure 5. 

      System database structure.

      Database development of prediction model for freshness and shelf life of cold chain meat

    • The prediction model database is an important part of the system, providing data and algorithm support for the inter-system model operation, and the intelligent operation module of the system.

      The prediction model database should contain two main functions. Firstly, it needs to contain a wide range of realistic operational models required for cold chain meat quality prediction and contain the growth parameters of different cold chain meat spoilage microorganisms for the algorithm to call. Secondly, model parameters for other cold chain meat spoilage microorganisms can be uploaded at a later stage to continuously expand the product envelope included in the database for different cold chain meat industry informatics applications. The structure of the predictive model algorithm database is shown in Fig. 6.

      Figure 6. 

      Database structure of prediction model algorithm.

      Front end page development

    • The front-end visual interaction page of the system is developed using the B/S structure (Browser/Server mode). The B/S structure uses the working mode of browser request and server response. Users can quickly request and visit the distributed servers on the network through the browser[18]. The browser receives the request, which is processed by the server and transmitted to the front-end page for visualization.

      The front-end development technology is developed using ASP.NET and combined with Vue.js 2.6 MVVM bi-directional data binding framework to achieve front-end page development[19], the statistical charts in the page are designed and developed using open source ECharts line charts, bar charts, pie charts and so on[20]. The front-end pages are divided into system introduction page, login page, console page, product management page, monitoring management page and system settings page.

      Multi-system integration interface development

    • In order to better integrate this system with practical industrial applications, it is necessary to develop multi-system integration interfaces, and data communication between different systems to collect the required data for real-time supervision of the freshness and shelf life of cold chain meat products.

      The system is interfaced with the cold chain logistics management system of 'Henan Jiuyuquan Food Co., Ltd', a sauce and marinated meat products company, with the initial colony count of spoilage microorganisms, real-time temperature and humidity of logistics trucks and other environmental information, key workshop points and terminal shop environmental information as data sources input and stored in the system database, different functional modules for data call.

      As shown in Fig. 7, the data sources of the system are the initial colony count of product spoilage microorganisms, cold chain logistics management system, key production link points in the workshop and environmental information of the terminal shops, etc. The system collects data from the data sources and stores them in the system base database, the model database matches different products according to different product types, calls the relevant dynamic data in the base database, predicts the freshness and shelf life of the products in real time, and this is displayed on the front-end page of the B/S system to facilitate timely and effective management by the management staff. The output results and related data can also be exported to different systems through the data interface for statistical analysis.

      Figure 7. 

      Software interfacing and data flow structure.

    RESULTS

    • The system tested the model algorithm using a shelf-life prediction model algorithm constructed by Sun for vacuum-packed raw beef, which identified Lactobacillus and Pseudomonas as specific spoilage bacteria for vacuum-packed raw beef products and constructed a specific spoilage bacteria growth model and a residual shelf-life model[21].

      Y(t)=Y0+(Ymax−Y0)exp{−exp[μmaxeYmax−Y0(λ−t)+1]} (1)

      Eq. (1) is a modified Gompertz model, the first level of the microbial prediction model, used to describe the process of change in spoilage microorganisms over time, and is the basis of the freshness and shelf-life prediction model.

      √μmax=b(T−Tmin) (2)
      √1λ=bλ(T−Tmin) (3)

      Eq. (2) and Eq. (3) are square root models, which are secondary models of the microbial prediction model, used to describe the effect of temperature on the primary model and to determine the parameter values for specific growth rate and lag time in the primary model.

      SL=λ−Nmax−N0μmaxe{ln[−ln(Ns−N0Nmax−Ns)]−1} (4)

      Eq. (4) is a shelf-life prediction model, constructed based on a growth model of specific spoilage bacteria, which predicts the shelf-life of a product by calculating the time from initial colony count (N0) to minimum spoilage (Ns) of a specific spoilage bacteria.

      MSE=RSSn−p (5)
      RMSE=√RSSn−p (6)
      AIC=nln(RSSn)+2(m+1)+2(m+1)(m+2)n−m−2 (7)

      Eqs (5), (6) and (7) are the assessed values of the model fit effect, where MSE is the mean square error, RMSE is the root mean square error and AIC is the Akaike information criterion. The above three equations are used to assess the prediction model non-linear fit effect.

      The system was tested under the conditions shown in Table 1.

      Table 1.  System test conditions.

      Test categoryTest condition
      Software EnvironmentNet Framework4.6.2 + IIS7.5 + MySQL 5.7.31
      Hardware environmentCPU: 2×Intel Xoen quad-core X5530 2.40GHz or more
      RAM8GB (4G*2) DDR3-1333 with fault-tolerant repair
      Hard DiskSAS10K 320GB × 4 (RAID5)
      Network Card10/100/1000 MB Ethernet port × 2

      A flow chart of the system in use is shown in Fig. 8.

      Figure 8. 

      Flow chart of system usage.

      Through system testing, the development of intelligent supervision system for cold chain meat was finally realized. The functional modules of the system run well and meet the trial needs of different roles, part of the system running effect diagram is shown in Fig. 9.

      Figure 9. 

      Partial system pages.

      The system has been tested and used, you can login to the system through different roles. Display and analyze big data on the console page. Unified management and retrieval of enterprise products through tables to view product information. In the product quality supervision module, real-time supervision of cold chain meat freshness quality information and abnormal warning using different colors. In the user management module, you can view all user information of the current system and set user rights. After testing, the overall system works well and can complete the design functions of the intelligent supervision system for cold chain meat.

    CONCLUSIONS

    • The proposed system combines technologies such as Web Internet and visualization, so that different users can use this system smoothly to complete the required functions. By constructing a cold chain meat freshness prediction model database to monitor the real-time freshness of cold chain meat in the whole industry chain process in real time. It also combines technologies such as Web Internet and visualization, so that different users can use this system smoothly to complete the required functions. The system enables different business users to monitor the quality of their products in real time without contact, and provide real-time warning of abnormalities, to assist business managers in making decisions, and to greatly reduce the losses caused by failure to obtain cold chain meat quality information in a timely manner. Consumers can also use the system to scan QR codes to obtain information on the quality of the cold chain meat products they buy and provide scientific buying advice.

      Acknowledgments

      • This research was financially supported by the Major science and technology projects in Henan province (221100110500), the Science Foundation for Outstanding Youth of Henan Province (212300410008),and the Science and Technology Innovation Team of Henan Universities (22IRTSTHN021).

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Rights and permissions
      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press on behalf of Nanjing Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (9)  Table (1) References (21)
  • About this article
    Cite this article
    Niu Z, Yang J, Zhu Y, Xu L, Yang S, et al. 2023. Development and design of an intelligent monitoring system for cold chain meat freshness. Food Materials Research 3:2 doi: 10.48130/FMR-2023-0002
    Niu Z, Yang J, Zhu Y, Xu L, Yang S, et al. 2023. Development and design of an intelligent monitoring system for cold chain meat freshness. Food Materials Research 3:2 doi: 10.48130/FMR-2023-0002
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  • Introduction
  • Materials and methods

  • Systems analysis

  • Requirement analysis
  • System development module analysis

  • System design

  • The design of multi-tier system architecture for application requirements
  • System core business process design
  • System database design
  • System function design

  • System implementation

  • System database development
  • System basic database development
  • Database development of prediction model for freshness and shelf life of cold chain meat
  • Front end page development
  • Multi-system integration interface development
  • Results
  • Conclusions
  • Acknowledgments
  • Conflict of interest
  • Rights and permissions
  • About this article
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