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Article

Real-Time Temperature and Humidity Measurements during the Short-Range Distribution of Perishable Food Products as a Tool for Supply-Chain Energy Improvements

by
Martim L. Aguiar
1,2,
Pedro D. Gaspar
1,2,*,
Pedro D. Silva
1,2,
Luísa C. Domingues
1 and
David M. Silva
1
1
Department of Electromechanical Engineering, University of Beira Interior, Rua Marquês D’Ávila e Bolama, 6201-001 Covilha, Portugal
2
C-MAST, Center for Mechanical and Aerospace Science and Technologies, Faculty of Engineering, University of Beira Interior, 6201-001 Covilha, Portugal
*
Author to whom correspondence should be addressed.
Processes 2022, 10(11), 2286; https://doi.org/10.3390/pr10112286
Submission received: 27 September 2022 / Revised: 21 October 2022 / Accepted: 24 October 2022 / Published: 4 November 2022
(This article belongs to the Special Issue Application of Data-Driven Method for HVAC System)
Browse Figures
Versions Notes

Abstract

:
Food waste results in an increased need for production to compensate for losses. Increased production is directly related to an increase in the environmental impact of agriculture and in the energy needs associated with it. To reduce food waste, the supply chain should maintain ideal preservation conditions. In horticultural products, temperature, and relative humidity are two of the main parameters to be controlled. Monitoring these parameters can help decision-making in logistics and routes management, as well as to diagnose and timely prevent food losses. In the present work, eighteen wireless traceability devices with temperature and relative humidity sensors monitored crates with horticultural products along a short-range distribution route with five stops (4 h 30 m). Sensor data and a location tag were sent via GSM for real-time monitoring. The results showed fluctuations in temperature and relative humidity that reached up to 7.4 °C and 35.3%, respectively. These fluctuations happened mostly due to frequent door opening, operational procedures, and irregular refrigeration conditions. Furthermore, the results brought attention to a procedure that creates unnecessary temperature fluctuations and energy losses. This study highlights the importance of individual monitorization of goods, for quality control and optimization of energy efficiency along the supply chain.

1. Introduction

The degradation of food quality is caused by microbial growth [1]. This degradation can lead to health hazards, food waste, and profit losses. The factors that affect microbial growth can be intrinsic (food pH, nutrients, moisture, etc.) or extrinsic (relative humidity, temperature, and atmosphere composition). Extrinsic factors are easier to control to increase food preservation [2,3,4,5]. Different food products require different ideal extrinsic conditions for optimum preservation [6,7]. Temperature is the extrinsic factor that affects food preservation the most, allied with the fact that inefficiencies leading to thermal losses are still a problem in the food industry [8,9,10,11]; this means that it is crucial to monitor the temperature of perishable foods along their life cycle to diagnose faults and cut losses. The thermal losses can be caused not only by inefficiencies such as bad insulation, system design and frost formation [12], but also by common routines such as doors opening and closing [13] which are unavoidable events during the distribution of refrigerated goods. The impacts of these events should be thoroughly monitored.
Refrigeration is essential for the preservation of horticultural products, but it requires significant amounts of energy [14]. In retail, the energy spent during the commercialization of refrigerated food products is about 50% of the supermarket’s total energy consumption [15]. Along with the energy used for storage and transport, refrigeration is one of the main causes of energy consumption along the life cycle of perishable food products [16]. To make matters worse, the amount of food products that are wasted before reaching the final consumer is approximately one-third of the total production; in horticultural products, this waste reaches 45% [17]. This leaves plenty of room for the development of technologies that monitor and help maintain food quality to reduce waste, energy consumption, and associated environmental impact [12,18,19].
Packaging has the objective of facilitating transportation while maintaining the quality of food products. Nowadays, new technologies are being developed to make packaging more efficient and sustainable [20,21]. Packages can incorporate materials to enhance food preservation [22], such as phase change materials (PCM) that help maintain a stable temperature when conditions outside the package vary [23,24,25]. Traceability is also increasingly being incorporated into packaging along the supply chain [26,27]. The use of sensors for agricultural applications is not a new subject [28] but it is a field that has been through accelerated growth in both scientific and industrial sectors [13]. In the packaging sector, intelligent packaging technologies use sensors to measure and transmit the packaging conditions, with the aim of reducing food waste and meeting new food safety regulations, which are frequently updated to be stricter [26].
Intelligent packaging is the result of technologies that allow for the incorporation of communication and sensing tools in a food packaging system to measure the conditions of the environment in which the system is deployed [13]. Measurement of parameters such as temperature and relative humidity or vibrations, and light can be useful to perform quality control, objectively, which would be otherwise impossible [26]. Of these parameters, temperature is the most associated with energy consumption, and food quality degradation is the most important to monitor [29].
Traceability of food quality leads to increased sustainability and profit [30] while decreasing health hazards [31,32]. This can be magnified if traceability systems are integrated into decision support systems for perishable food products [33,34]. This way strategies such as price rebates can be applied to sell products whose storing and transport conditions monitored can be associated with quality degradation [35], resulting in dynamic pricing according to the quality of the food products [1]. Furthermore, with the emergence of artificial intelligence (AI), the data gathered by intelligent packaging can be fed to AI algorithms for the best decisions to be made to increase sustainability and profitability [36].
In previous works, Morais et al. [37,38] developed a monitoring device to read temperature and relative humidity during transportation. The measurements were transmitted via GSM, along with GPS coordinates. Subsequently, the iTrace platform was developed, allowing real time access to measurements made with an array of temperature and relative humidity sensors [6]. Testing this system in a real case scenario was the aim of the present study. This way, extrinsic factors for food preservation can be monitored along the cargo volume and as close to the food product as possible. This is important as the arrangement in which the products are conditioned can have an impact on temperature and relative humidity distribution and fluctuations, which ultimately, if inadequate, results in quality degradation [39]. Temperature fluctuations among different products have already been studied in supermarkets, within the display cabinets [40,41]. The heterogeneous cargo and produce crate distribution of a real case scenario only accentuate the differences in the fluctuations of temperature and relative humidity. Ultimately, the aim of the study is to highlight the importance of monitoring produce by crate, to track and diagnose flaws in the conservation of extrinsic conditions for food preservation, and energy losses associated with it, that otherwise would go undetected.

2. Materials and Methods

The present work aimed to study the temperature and relative humidity distribution close to the different horticultural products during a distribution route. For this, a system to read and transmit the readings of temperature and relative humidity is needed. Moreover, the cargo must be organized and characterized to facilitate the interpretation of large volumes of data.

2.1. Temperature and Relative Humidity Measurement and Communication System

The sensing module was composed of several parts, shown in Figure 1. The measurements of temperature and relative humidity were taken using an SHT30 sensor. This sensor had an accuracy of ±0.3 °C with a repeatability of 0.06 °C for temperatures between 10 °C and 55 °C, and accuracy of ±3% with a repeatability of 0.13% for relative humidity values between 10% and 90%. The sensor was connected to a LOLIN D1 mini pro V2.0 microcontroller using an LOLIN SHT30 v2.1.0 shield. The sensor, microcontroller, and a 3.7 V 500 mAh lithium battery were conditioned within a 3D printed box for protection. This box had a mesh, visible in Figure 1e, so that the sensor could make direct contact with the air surrounding the food product.
It is worth noting that the measurements of relative humidity have significant errors which required a calibration of the sensors to be made before experimental testing. After calibration, a repeatability of 0.13% relative humidity ensured accurate data, considering these are low-cost sensors. More precise and expensive sensing methods would render the application unfeasible for real case applications, due to higher costs associated.
An array of these sensing modules communicated with the gateway composed of an industruino microcontroller, with a GSM/GPRS module to allow for real time data transmission with GPS location pinpointing. The values were measured and transmitted every five minutes. These data could be accessed at the iTrace platform, developed for this purpose [8]. The gateway and system schematics can be seen in Figure 2.
The system was developed so that the sensing modules could be placed in the crates within the horticultural products. This proximity to the produce allowed measurements of temperature and relative humidity to be taken as close to the food product as possible.

2.2. Distribution Vehicle and Refrigeration System

The fleet of distribution vehicles was composed by IVECO Daily vans, as shown in Figure 3.
The cargo area and loading doors of these vans can be characterized by the dimensions shown in Table 1.
The van had a hwasung HT-250RT-ESC refrigeration system installed with a controller in the driver’s compartment that displayed the instantaneous temperature value, as shown in Figure 4a. These displayed temperatures were measured at the outlet of the heat exchanger, as shown in Figure 4b, and were compared to the measurements taken by the sensors within the produce crates. As this temperature was only displayed while the van was working, only measurements before and after stops were taken. Between stops, temperature values were manually registered in intervals of 15 min. The temperature setpoint was 3 °C. No relative humidity values are monitored by the refrigeration system; therefore, no van relative humidity measurements were taken for later comparison.

2.3. Cargo and Sensor Distribution

The route consisted of the distribution of an assortment of horticultural products along five stops. The diversity of horticultural products made it necessary to characterize the distribution and content of the crates along the cargo area to facilitate the interpretation of results later. Table 2 displays the abbreviations used to represent the different horticultural products.
The distribution of the products was arranged by the drivers, to facilitate deliveries; therefore, the arrangement order was diverse and complex. In the example shown in Figure 5, there were 108 crates of diverse produce and 26 bags of 20 kg of potatoes and onions.
The sensors were distributed along the piles (columns) of crates/bags, in the top, middle and bottom. Figure 6 shows a column of crates with a sensor (sensor 3) placed within the tomatoes, in the middle crate.
To explain the sensor distribution across such a heterogeneous cargo, the top and section views were schematized as shown in Figure 7. In this representation, the cargo area is divided into groups, each group is divided into columns and each column is layered into lines. As an example, sensor 19 is placed in group 4, column 3, line 3, so G4C3L3 is a short reference that gives exactly the position of the crate the sensor is in. This representation also allows for a sectioned view, that can be helpful to interpret results, considering how the surroundings can influence the environment.

2.4. Route and Stops

The route consisted of the distribution of an assortment of horticultural products, along with five different local retail stores within the northern region of Castelo Branco, Portugal. The measurements were taken during the month of June, in the beginning of the Portuguese summer season, with the temperatures within the minimum average of 15.1 °C and maximum average of 29.4 °C, reaching a maximum absolute value of 38.5 °C [42]. Figure 8 displays a map of the route along with the stops and a graphical representation of the duration of those stops. This graphical representation was overlayed with the sensor data in the results section of the present work, so that the context of the stops could be taken into consideration while analyzing the results.
The route started at a point Ⓞ. At 8:40 h, the van started, and the van’s refrigeration system was turned on. After leaving the warehouse, five more stops were made for unloading, before returning. Table 3 displays the details about all the unloading stops made.
It is important to note that some of the produce (leaf vegetables such as cabbages and lettuce) were watered before transportation to decrease the degradation associated with lower relative humidity. This was analyzed further as sensor measurements varied for this produce. An example of a watered crate of cabbages with sensor 12 (position G1C1L4) is shown in Figure 9.

3. Results and Discussion

Data were compiled to better display and compare the results. Averages of different sensors are shown, and the comparison is analyzed below. These averages were made from the readings taken in five-minute periods.

3.1. Average Values of All Sensors

The average of all the sensor readings was the first step to understanding the overall fluctuations in temperature and relative humidity along the route. In Figure 10 the averages for all the readings are presented.
Fluctuations were easily observed, especially during stops in the periods that cargo was unloaded. The graph that shows the average temperature of all the sensors also plots the measurements displayed by the refrigeration system installed in the van. This display shows measurements taken right at the outlet of the refrigeration system. It is notable how the temperature readings in the sensors differed from those displayed in the driver’s cabinet, only matching when the refrigeration system was turned off. This happened because this sensor measured the outlet of the refrigeration system and not the temperature of the air close to produce. The refrigeration system only measured temperature; therefore, no relative humidity values are presented for comparison. Overall, temperature and relative humidity values decreased during trips, and increased during stops. Although it is known that relative humidity is inversely proportional to temperature, in this case, the refrigeration system while lowering the air temperature also acted as a dehumidifier due to the surface temperature of the heat exchanger being below the dew point temperature. This resulted in measurements showing a decrease in temperature along with a decrease in humidity during trips, while both temperature and relative humidity increased during stops.

3.2. Sensors in the Top Versus Sensors in Lower Crates

Top crates were directly exposed to the refrigerated air during transportation and warm air during unloads, while crates in lower layers should have been more protected against fluctuations. In Figure 11 the average temperatures and relative humidity for sensors in the top (13, 14, 15, 16, 17) and inner layers (3, 6, 8, 7, 5) are shown.
It is possible to observe that as was expected, sensors in the lower layers registered fewer aggressive fluctuations in temperature and relative humidity. The temperature was lower in the top sensors due to them being exposed directly to the outlet of the refrigeration system while traveling, but the values quickly rose to those of the inner sensors while unloading. Relative humidity was also higher for the crates positioned in lower levels, especially between unloads, but the fluctuations were comparable to those in the upper levels, except during unloads.

3.3. Average of All Sensors versus Sensors Placed near the Side Door

It was expected that the sensors placed near the doors would suffer higher fluctuations in temperature and relative humidity when unloading due to the proximity to external environment and larger thermal interaction. In Figure 12, readings from the sensors near the side door (14, 2, 4) are compared to the average. It is worth noting that the side door was only opened from the stop onwards.
As expected, near the unloading door, temperatures were a lot less stable and particularly higher than the average after this door started being used. Furthermore, fluctuations in relative humidity were comparable to the fluctuations in the average relative humidity, but the readings showed consistently lower values after the side door started being used for unloading.

3.4. Sensors Placed in Wet Produce versus Sensors Placed in Dry Produce

To increase the shelf life of some horticultural products, such as cabbage, cauliflower, and lettuce, the procedure established in the warehouse is to hose this produce down with tap water before loading it for distribution. Produce such as potatoes, melons, and onions do not go through this procedure, so the relative humidity measured near them was expected to be lower. In Figure 13, the average of measurements in crates with watered produce (5, 6, 12, 13) is compared to the average of measurements in drier produce (2, 7, 9). It is worth noting that from 11:30 h onwards, all the dry readings shown were from sensor 2, as crates from sensors 7 and 9 were unloaded in stop Ⓒ.
The temperature difference was mostly seen after sensors 7 and 9 are disabled, and the side door started being used. Sensor 2, being placed among potato bags, near the side door measured higher values from stop Ⓒ onward as shown in Section 3.3. For relative humidity, fluctuations were of similar amplitude along the route, but a higher relative humidity was measured near produce watered prior to loading.

3.5. Individual Sensor Measurements with Different Readings

Although the averages help to achieve an overall understanding of how the events during transportation affect the extrinsic conditions along the distribution route, the point of distributing an array of sensors along the cargo volume was to realize how the conditions of individual crates of produce vary independently from each other, and if those variations were relevant enough for individual crate sensitization to be considered. In Figure 14, two different sensors with quite different readings are compared. Sensor 2, in position G1C4L2 was placed among potato bags, while sensor 6, in position G1C1L1 was placed among cabbages. This comparison aimed to display the importance of measuring crates individually, instead of single-point measurements during the transportation of horticultural products.
The differences between readings were quite surprising, considering the produce spent the night in the same climatized pavilion and were transported in a van adapted for refrigerated transportation of goods. At 12:30 h, for example, the temperature differences were approximately 4 °C, and the relative humidity had a difference of approximately 25%. In this case, the different nature of the horticultural products, their positioning and the procedures undergone before loading were the principal causes for these discrepancies. It is also worth noting that not even the fluctuations were similar.

3.6. Maximum, Minimum and Average Values per Sensor

To better characterize the temperature and relative humidity distribution along the cargo volume, Table 4 shows the absolute maximum shows the highest value measured, the absolute minimum shows the lowest value measured, the average shows the average of all the readings from that sensor, and the range was calculated by subtracting the values of absolute minimum to the values of absolute maximum, to obtain the range within each sensor fluctuates for temperature and relative humidity. The values were measured during the distribution route, while the sensor was active, i.e., before turning off the sensor and unloading the monitored crate. The times when the absolute maximum and absolute minimum values were recorded are also shown.
When analyzing Table 4, we can observe that for sensor 2 and sensor 9, the difference between absolute maximum temperatures recorded was 5.3 °C, the difference between absolute minimum temperatures recorded for these same sensors was 5.6 °C and the difference between average recorded temperatures was 4.8 °C. For sensor 10 and sensor 6, the difference between the ranges of recorded temperatures was 6.1 °C.
For relative humidity, sensor 6 and sensor 14 showed a difference between the absolute maximum relative humidity recorded of 14%, while the difference between the absolute minimum relative humidity recorded in sensor 7 and sensor 10 was 32.5%. The difference in average recorded relative humidity for sensor 6 and sensor 7 was 19.7% while the difference in the range of recorded relative humidity for sensor 4 and sensor 10 was 31.9%. These were significant fluctuations among different sensors, which highlights the importance of distributing the measurements between produce crates, instead of single point measurements.
Furthermore, absolute maximum and minimum values for temperature and relative humidity, allowed us to inspect when and where the worse conditions for loss of quality were met, while average values indicated an overall state of preservation, and range highlights fluctuations along the trip. Together, this can be a powerful tool to diagnose faults that otherwise would go undetected.
The best demonstration of the application of this system as a tool for the detection of faults was finding a flaw concerning the procedures taken by the distribution company. After completing the measurements and analyzing the data, the authors were intrigued by the disparities measured in some sensors, namely between sensor 2 and sensor 6, whose values were already compared in Section 3.5. Sensor 2, in position G1C4L2 was placed among potato bags, while sensor 6, in position G1C1L1 was placed among cabbages. What intrigued the authors was how sensor 6, placed in the lower levels, and far from the door, could have a higher range of temperature measurements than sensor 2, placed near the door. Upon close inspection, and as shown in Figure 14a, temperature fluctuations during stops were measured in both sensors, but during the overall trip, the temperature in sensor 6 dropped significantly more than the temperature in sensor 2. In addition, sensor 6 had the second highest value for absolute maximum temperature, which is also unusual, as cabbages were stored at lower temperatures than other products such as potatoes and onions. It was only when looking at the relative humidity which is significantly higher for sensor 6 than for sensor 2 that the following hypothesis arose: cabbages, and other leaved produce, went through the procedure of being watered prior to loading, receiving heat from the tap water, which was aggravated in the warm days of summer the data was collected. This hypothesis would be impossible to achieve if it were not for the array of sensors, together with a diagram of their position and type of horticultural product.
After sensor 6, sensors 15, 14 and 16 registered higher ranges, which was expected, as these were all on the top crates near the side door, and as shown in Figure 11a and Figure 12a the top and door side sensors should have measured higher fluctuations.

4. Conclusions

The impact of inadequate temperature and relative humidity in the preservation of horticultural products is known, but it is not easy to predict if sensors are not placed near the produce. This study presented evidence that reinforces the importance of parameter monitoring within the packages for the precise control of food quality preservation along the supply chain. The disparity between the crate temperature measurements and the readings displayed from the van refrigeration system further reinforces these claims.
The results also showed that the order in which products are stored can have an impact on the temperature and relative humidity fluctuations during distribution. This information can be used to rearrange the order in which products are conditioned to decrease the impact of temperature fluctuations, especially in produce most prone to degradation. For instance, produce that requires higher relative humidity, or is more sensible to temperature and relative humidity fluctuations should be stored in the bottom. This suggestion does not interfere with the duration or unload procedure, as the produce are unloaded in columns, and only the column order is altered, not the position of the column within the cargo area, which is left for the driver to decide how to best accommodate for faster distribution.
The unpredictability of the type and order of transported goods, especially in short-range distribution, does not allow for any assumption of cargo homogeneity, which further reinforces the need for distributed monitoring.
Measurements such as those presented in this work can be used to diagnose and fix flaws along the supply chain. In the case of this study, it was found that a procedure was introducing undesired thermal loads by watering produce with tap water prior to loading it into the distribution vehicle. This may result in lower shelf life for the affected produce due to increased fluctuations in conservation temperature, higher energy consumption associated with refrigeration, to compensate for the thermal load, and energy loss associated with food waste. The negative effects of this procedure would otherwise go undetected if the monitoring of temperature and relative humidity was not spread across the cargo volume. To fix this flaw, chilled water could be sprayed on the produce that require watering, or misting could be applied instead of hosing the produce, to reduce the volume of warm water and associated thermal load. Obviously, the impact of the procedure on the degradation of quality should be studied, to access if changes would result in improvements significant enough to modify it.
The application of the studied sensing system will be specific to every case. The number of variables such as refrigerated or non-refrigerated transportation system, produce variety, season, weather, trip length, number and duration of unloads, produce, and so on will influence results, and that is why this is a good tool to measure what can be almost impossible to predict, helping diagnose flaws that affect the shelf life of produce. This complexity will increase with the number of sensors, as recording positions, and sensed produce is crucial to a complete analysis, which will become a harder task, without a tool specifically designed for the purpose.
The cross analysis of sensor readings is useful to understand the variations during transportation, diagnose flaws and develop improved procedures, although to predict the shelf life of produce in a single crate, data measured close to the specific produce is the most important.
This system has some limitations. The measurement and analysis of temperature and relative humidity fluctuations along an uneven cargo volume was complex enough, given different sides of the van being exposed to the sun, different doors being opened in different phases of the distribution route, and the position of the outlet of the refrigeration system. Added to this was an uneven cargo, with different produce organized not by type, but by client, with different storage temperatures across them and some of them being watered before loading will add to the complexity of the analysis. Furthermore, the fact that some produce is unloaded right at the beginning of the route resulted in a small window of time for these sensors to gather data.
The need for a wireless sensing system means that a battery must be charged from time to time, and although this is a low-power sensor, the logistics associated with charging several dozens of sensors, even if optimized to be a weekly charge, must be addressed and simplified, to make this a viable option.
The robustness of the sensor is also a limitation that must be addressed. The crates of produce endure though mechanical stresses during transportation, being dragged and dropped, or even thrown when empty. If the sensor is attached to the crate, it will need to endure these conditions while remaining undamaged and reliable for the next usage.
A high sample rate is crucial to understanding abrupt changes in temperature and relative humidity, but a low sample rate should be enough to measure slower changes between unloads. The problem with high sample rates is higher power consumption, and therefore lower battery life. This can be solved by implementing a dynamic sample rate that increases sample rate during unloads, by either measuring the state of movement with an accelerometer, or by measuring luminosity within the cargo area, which increases when the doors are open.
Further studies should be made with a larger array of sensors, and a higher frequency of measurements to better understand the fluctuations of temperature and relative humidity along with the horticultural goods during transportation. This will also require a system for data analysis and comparison; as mentioned above, a larger array of sensors will result in a higher complexity of data that in turn will be more difficult to process and understand, find flaws, and monitor quality.

Author Contributions

Conceptualization, P.D.G., M.L.A., L.C.D. and P.D.S.; methodology, P.D.G., M.L.A., L.C.D. and P.D.S.; validation, P.D.G. and P.D.S.; formal analysis, P.D.G. and P.D.S.; investigation, M.L.A., L.C.D., D.M.S.; resources, P.D.G. and M.L.A.; data curation, M.L.A.; writing—original draft preparation, M.L.A.; writing—review and editing, P.D.G., P.D.S. and M.L.A.; supervision, P.D.G. and P.D.S.; project administration, P.D.G.; funding acquisition, P.D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded within the activities of project “PrunusPós–Optimization of processes for the storage, cold conservation, active and/or intelligent packaging and food quality traceability in post-harvested fruit products”, project n. º PDR2020-101-031695, Partnership nº 87, initiative n. º175, promoted by PDR 2020 and co-funded by FEADER within Portugal 2020. This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) and C-MAST (Centre for Mechanical and Aerospace Science and Technologies), under project UIDB/00151/2020.

Data Availability Statement

More information on the project PrunusPós and the iTrace platform can be found at https://www.prunuspos.pt/ (accessed on 26 September 2022).

Acknowledgments

The authors are also thankful to the company Albifrutas–Produtos Horticolas, Lda., in the name of José António for allowing us to perform the measurements in a real case scenario, and to the van driver, António, for helping along and being patient with the necessary procedures of the experiment during the route.

Conflicts of Interest

The authors declare no conflict of interest.

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  13. Carneiro, R.; Gaspar, P.D.; Silva, P.D. 3D and transient numerical modeling of door opening and closing processes and its influence on thermal performance of cold rooms. Appl. Therm. Eng. 2017, 113, 585–600. [Google Scholar] [CrossRef]
  14. Gaspar, P.D.; Gonçalves, L.C.; Pitarma, R.A. Experimental analysis of the thermal entrainment factor of air curtains in vertical open display cabinets for different ambient air conditions. Appl. Therm. Eng. 2011, 31, 961–969. [Google Scholar] [CrossRef]
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  16. Gaspar, P.D.; Godina, R.; Barrau, R. Influence of orchard cultural practices during the productive process of cherries through Life Cycle Assessment. Processes 2021, 4, 1065. [Google Scholar] [CrossRef]
  17. Gustavsson, J.; Cederberg, C.; Sonesson, U.; van Otterdijk, R.; Meybeck, A. Global Food Losses and Food Waste—Extent, causes and prevention. Study conducted for the International Congress SAVE FOOD. In Proceedings of the Interpack 2011, Düsseldorf, Germany, 12–18 May 2011; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2011. [Google Scholar]
  18. Gaspar, J.P.; Gaspar, P.D.; Silva, P.D.; Simões, M.P.; Santo, C.E. Energy life-cycle assessment of fruit products—Case study of Beira Interior’s peach (Portugal). Sustainability 2019, 10, 3530. [Google Scholar] [CrossRef] [Green Version]
  19. Aguiar, M.L.; Gaspar, P.D. Refrigerantes naturais: Tendências do mercado, políticas e tecnologias na indústria agroalimentar portuguesa. In Proceedings of the X Iberian Congress and VIII Ibero-American Congress of Cold Sciences and Techniques (CYTEF 2020), Navarra, Spain, 11–13 November 2020; pp. 323–330. [Google Scholar]
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  21. Leitão, F.; Madhan, S.K.; Silva, P.D.; Gaspar, P.D. Experimental Testing of the Thermal Response of Different Food Alveoli Solutions for Packaging Boxes. Lecture Notes in Engineering and Computer Science. In Proceedings of The World Congress on Engineering 2021 (WCE 2021), London, UK, 7–9 July 2021; pp. 268–272. Available online: http://www.iaeng.org/publication/WCE2021/WCE2021_pp268-272.pdf (accessed on 26 September 2022).
  22. Leitão, F.; Madhan, S.K.; Gaspar, P.D.; Silva, P.D. Experimental Study of the Thermal Response of Fruits Alveoli with Different Materials, Structure and Energy Storage. In Proceedings of the 15th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics (ATE-HEFAT 2021), Online, 25–28 July 2021; Available online: https://hefat2021.org/proceedings/PROCEEDINGS.rar (accessed on 26 September 2022).
  23. Madhan, S.K.; Espírito Santo, C.; Andrade, L.P.; Silva, P.D.; Gaspar, P.D. Active and intelligent packaging with phase change materials to promote the shelflife extension of food products. KnE Eng. 2020, 4, 232–241. [Google Scholar] [CrossRef]
  24. Leitão, F.; Silva, P.D.; Gaspar, P.D.; Pires, L.C. Experimental study on the thermal response of PCM alveoli for fruit packaging boxes. In Proceedings of the III Environmental Innovations: Advances in Engineering, Technology and Management, EIAETM Conference, Online, 27 September–1 October 2021. [Google Scholar]
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  26. Morais, R.; Cunha, J.B.; Cordeiro, M.; Serodio, C.; Salgado, P.; Couto, C. Solar data acquisition wireless network for agricultural applications. In Proceedings of the 19th Convention of Electrical and Electronics Engineers in Israel, Jerusalem, Israel, 5–6 November 1996; pp. 527–530. [Google Scholar] [CrossRef]
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  28. Zöller, S.; Wachtel, M.; Knapp, F.; Steinmetz, R. Going all the way—Detecting and transmitting events with wireless sensor networks in logistics. In Proceedings of the 38th Annual IEEE Conference on Local Computer Networks—Workshops, Sydney, Australia, 21–24 October 2013; pp. 39–47. [Google Scholar] [CrossRef]
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Processes 10 02286 g001 550
Figure 1. Sensing module components: LOLIN SHT30 v2.1.0 shield (a), LOLIN D1 mini pro V2.0 microcontroller (b), 3.7 V 500 mAh lithium battery (c), assembled sensing module (d) and 3D-printed case (e).
Figure 1. Sensing module components: LOLIN SHT30 v2.1.0 shield (a), LOLIN D1 mini pro V2.0 microcontroller (b), 3.7 V 500 mAh lithium battery (c), assembled sensing module (d) and 3D-printed case (e).
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Figure 2. Gateway (a) and system schematics (b).
Figure 2. Gateway (a) and system schematics (b).
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Figure 3. Distribution vehicle example.
Figure 3. Distribution vehicle example.
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Figure 4. Controller (a) and heat exchanger with temperature sensor (b) of the trucks refrigeration system.
Figure 4. Controller (a) and heat exchanger with temperature sensor (b) of the trucks refrigeration system.
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Figure 5. Final produce arrangement within the truck (a), and sample of the diversity of produce transported (b).
Figure 5. Final produce arrangement within the truck (a), and sample of the diversity of produce transported (b).
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Figure 6. Placement of sensor 3.
Figure 6. Placement of sensor 3.
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Figure 7. Organization of crates among the cargo area and sensor distribution.
Figure 7. Organization of crates among the cargo area and sensor distribution.
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Figure 8. Route stops (a) and graphical representation of the duration of those stops (b).
Figure 8. Route stops (a) and graphical representation of the duration of those stops (b).
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Figure 9. Crate with cabbages that were watered prior to loading in the truck.
Figure 9. Crate with cabbages that were watered prior to loading in the truck.
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Figure 10. Average measured temperature, and refrigeration system displayed temperature (a) and average measured relative humidity (b).
Figure 10. Average measured temperature, and refrigeration system displayed temperature (a) and average measured relative humidity (b).
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Figure 11. Average of top and inner sensor readings for temperature (a) and relative humidity (b).
Figure 11. Average of top and inner sensor readings for temperature (a) and relative humidity (b).
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Figure 12. Average of all the sensor readings versus the average of the readings from sensors near the side door for temperature (a) and relative humidity (b).
Figure 12. Average of all the sensor readings versus the average of the readings from sensors near the side door for temperature (a) and relative humidity (b).
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Figure 13. Average of the readings of sensors placed in crates with produce watered previously to being loaded, versus the readings of dry crates for temperature (a) and relative humidity (b).
Figure 13. Average of the readings of sensors placed in crates with produce watered previously to being loaded, versus the readings of dry crates for temperature (a) and relative humidity (b).
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Figure 14. Readings of sensor 2 and sensor 6 for temperature (a) and relative humidity (b).
Figure 14. Readings of sensor 2 and sensor 6 for temperature (a) and relative humidity (b).
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Table
Table 1. Van dimensions.
Table 1. Van dimensions.
DescriptionDimension (mm)
Load compartmentLength4680
Width1800
Height1800
Sliding side doorWidth1200
Height1800
Rear doorsWidth1530
Height1800
Table
Table 2. References for the horticultural products in the crates.
Table 2. References for the horticultural products in the crates.
Ref.ProductRef.ProductRef.ProductRef.Product
APAppleEPEggplantMGMangoPOPotato
ARApricotGAGarlicNENectarinePPPapaya
BCBaggedGRGrapeONOnionPRPear
BNBananaKWKiwiOROrangePSPackaged spinach
CACarrotLELettucePACauliflowerTOTomato
CBCabbageLKLeekPEPeachUMMushroom
CHCherryLNLemonPIPineappleWMWatermelon
CTCantaloupeMEMelonPLParsleyZUZucchini
Table
Table 3. Stops and relevant information.
Table 3. Stops and relevant information.
StopEvents
Stop TimeStop DurationUnloading DoorSensors Turned Off
Ⓐ9:20 h27 minBack door10, 11, 19
Ⓑ10:35 h7 minBack doorNone
Ⓒ11:17 h9 minSide door4, 7, 9, 16
Ⓓ11:53 h12 minSide door3, 5, 17, 15
Ⓔ12:47 h18 minSide door2, 6, 8, 12, 13, 14, 18
Table
Table 4. Absolute maximum, absolute minimum, range, average temperature (T) and relative humidity (RH) per sensor, with time of measurement for absolute maximum and absolute minimum values.
Table 4. Absolute maximum, absolute minimum, range, average temperature (T) and relative humidity (RH) per sensor, with time of measurement for absolute maximum and absolute minimum values.
SensorAbsolute MaximumAbsolute MinimumRangeAverage
T (°C)Time (h)RH (%)Time (h)T (°C)Time (h)RH (%)Time (h)T (°C)RH (%)T (°C)RH (%)
218.610:3080.19:5015.612:3055.012:553.025.216.667.1
316.79:1588.39:5012.211:5054.811:454.433.514.672.9
413.49:1591.29:5010.110:3556.011:153.335.311.878.4
516.69:1584.89:4012.011:5050.911:204.634.014.267.5
617.99:4091.310:3010.512:5575.911:207.415.414.485.3
715.79:0577.49:5012.511:2550.111:203.227.314.165.6
816.19:4587.19:3511.412:4559.012:454.828.213.572.0
913.39:4087.09:4010.010:3555.711:203.331.411.775.5
1014.89:2086.09:3013.59:3082.69:201.23.414.184.5
1115.49:1582.99:2512.99:2571.89:152.511.214.476.9
1216.79:4085.59:0512.712:5566.812:454.118.814.275.2
1315.49:3085.812:5511.111:5561.612:254.324.313.171.5
1416.89:4077.39:3011.311:5055.411:205.521.913.767.3
1516.69:1581.39:3010.811:5551.211:205.830.213.867.7
1615.29:0584.311:2510.311:2056.411:205.027.912.974.1
1715.29:4080.19:3011.211:5057.411:204.022.713.569.6
1816.510:3587.19:5011.612:5562.312:554.924.814.078.6
1914.09:1586.89:4512.09:3079.89:151.97.013.482.9
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Aguiar, M.L.; Gaspar, P.D.; Silva, P.D.; Domingues, L.C.; Silva, D.M. Real-Time Temperature and Humidity Measurements during the Short-Range Distribution of Perishable Food Products as a Tool for Supply-Chain Energy Improvements. Processes 2022, 10, 2286. https://doi.org/10.3390/pr10112286

AMA Style

Aguiar ML, Gaspar PD, Silva PD, Domingues LC, Silva DM. Real-Time Temperature and Humidity Measurements during the Short-Range Distribution of Perishable Food Products as a Tool for Supply-Chain Energy Improvements. Processes. 2022; 10(11):2286. https://doi.org/10.3390/pr10112286

Chicago/Turabian Style

Aguiar, Martim L., Pedro D. Gaspar, Pedro D. Silva, Luísa C. Domingues, and David M. Silva. 2022. "Real-Time Temperature and Humidity Measurements during the Short-Range Distribution of Perishable Food Products as a Tool for Supply-Chain Energy Improvements" Processes 10, no. 11: 2286. https://doi.org/10.3390/pr10112286

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