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Digital twins enable the quantification of the trade-offs in maintaining citrus quality and marketability in the refrigerated supply chain

Nature Food volume 3, pages 413–427 (2022)Cite this article

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Abstract

Supply chains of fresh fruit must maintain a very narrow window of hygrothermal conditions after harvest. Any excursions outside this range can markedly lower the consumer acceptability of the fruit. However, the loss in fruit quality and marketability largely remains invisible to stakeholders throughout the supply chain. Here we developed a physics-based digital twin of citrus fruit to pinpoint when, why and to what extent fruit quality and marketability are reduced. Sensor data on 47 commercial shipments are thereby translated into actionable metrics for supply chain stakeholders by mapping the variability using principal component analysis. We unveiled a large spread (between 3% and 60%) in the shipments for different metrics of quality and marketability. Half of the shipments currently lie outside the ideal trade-off range between maintaining quality, killing fruit fly larvae and avoiding chilling injury. The digital twin technology opens the possibility to obtain the real-time coupling with sensor data to monitor food quality and marketability.

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Fig. 1: Overview figure for citrus fruit in a supply chain.
Fig. 2: Descriptive statistics of the input dataset for delivery air temperature.
Fig. 3: Inter-shipment variability in metrics.
Fig. 4: Mapping the correlation landscape for the metrics.
Fig. 5: The invisible quality loss for every shipment.
Fig. 6: Trade-off in maintaining citrus fruit quality.

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Data availability

The authors confirm that all relevant data supporting the findings of this study are available within the paper and its Supplementary Information. The source data for the delivery air temperature of the 47 commercially measured shipments and the fruit quality are available as source data files. Source data are provided with this paper.

References

  1. Defraeye, T. et al. Digital twins are coming: will we need them in supply chains of fresh horticultural produce? Trends Food Sci. Technol. https://doi.org/10.1016/j.tifs.2021.01.025 (2021).

  2. Mercier, S., Villeneuve, S., Mondor, M. & Uysal, I. Time-temperature management along the food cold chain: a review of recent developments. Compr. Rev. Food Sci. Food Saf. 16, 647–667 (2017).

    Article  Google Scholar 

  3. Khumalo, G., Goedhals-Gerber, L. L., Cronje, P. & Berry, T. The non-conformance of in-transit citrus container shipments to cold protocol markets: a systematic literature review. Food Control 125, 107947 (2021).

    Article  Google Scholar 

  4. Treatment Manual (USDA-APHIS, 2016); https://www.aphis.usda.gov/import_export/plants/manuals/ports/downloads/treatment.pdf

  5. Zacarias, L., Cronje, P. J. R. & Palou, L. in The Genus Citrus (eds Talon, M. et al.) 421–446 (Elsevier, 2020).

  6. Verdouw, C. N., Wolfert, J., Beulens, A. J. M. & Rialland, A. Virtualization of food supply chains with the Internet of things. J. Food Eng. 176, 128–136 (2016).

    Article  Google Scholar 

  7. Campos, Y. & Villa, J. L. Technologies applied in the monitoring and control of the temperature in the Cold Chain. In 2018 IEEE 2nd Colombian Conference on Robotics and Automation, CCRA 2018 (Ed. Carrillo, H. et al.) 1-6 (Institute of Electrical and Electronics Engineers, 2018).

  8. White, C. C. & Cheong, T. In-transit perishable product inspection. Transp. Res. E 48, 310–330 (2012).

    Article  Google Scholar 

  9. Goedhals-Gerber, L. L. & Khumalo, G. Identifying temperature breaks in the export cold chain of navel oranges: a Western Cape case. Food Control 110, 107013 (2020).

    Article  Google Scholar 

  10. Verboven, P., Defraeye, T., Datta, A. K. & Nicolai, B. Digital twins of food process operations: the next step for food process models? Curr. Opin. Food Sci. 35, 79–87 (2020).

    Article  Google Scholar 

  11. Defraeye, T. et al. Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains. Resour. Conserv. Recycl. 149, 778–794 (2019).

    Article  Google Scholar 

  12. Wu, W., Beretta, C., Cronje, P., Hellweg, S. & Defraeye, T. Environmental trade-offs in fresh-fruit cold chains by combining virtual cold chains with life cycle assessment. Appl. Energy 113586, 1–11 (2019).

    Google Scholar 

  13. Ayala-Zavala, J. F., Del-Toro-Sánchez, L., Alvarez-Parrilla, E. & González-Aguilar, G. A. High relative humidity in-package of fresh-cut fruits and vegetables: advantage or disadvantage considering microbiological problems and antimicrobial delivering systems? J. Food Sci. 73, R41–R47 (2008).

    Article  CAS  Google Scholar 

  14. Defraeye, T. et al. Integral performance evaluation of the fresh-produce cold chain: a case study for ambient loading of citrus in refrigerated containers. Postharvest Biol. Technol. 112, 1–13 (2016).

    Article  Google Scholar 

  15. Ladaniya, M. S. Citrus Fruit: Biology, Technology, and Evaluation (Academic Press, 2008).

  16. Adaskaveg, J. E., Hao, W. & Förster, H. Postharvest strategies for managing phytophthora brown rot of citrus using potassium phosphite in combination with heat treatments. Plant Dis. 99, 1477–1482 (2015).

    Article  CAS  Google Scholar 

  17. Iwanami, H., Moriya, S., Kotoda, N. & Abe, K. Turgor closely relates to postharvest fruit softening and can be a useful index to select a parent for producing cultivars with good storage potential in apple. HortScience 43, 1377–1381 (2008).

    Article  Google Scholar 

  18. Palou, L. in Postharvest Decay: Control Strategies (ed Bautista-Baños, S.) Ch. 2 (Elsevier, 2014).

  19. Ragaert, P., Devlieghere, F. & Debevere, J. Role of microbiological and physiological spoilage mechanisms during storage of minimally processed vegetables. Postharvest Biol. Technol. 44, 185–194 (2007).

    Article  Google Scholar 

  20. do Nascimento Nunes, M. C. Color Atlas of Postharvest Quality of Fruits and Vegetables (John Wiley & Sons, 2009).

  21. Campuzano, J., Ammon, C., Buzby, J., Bentley, J. & Padera, B. Updated Supermarket Shrink Estimates for Fresh Foods and Their Implications for ERS Loss-Adjusted Food Availability Data. Report No. 1476-2017-3908 (United States Department of Agriculture, Economic Research Service 2016).

  22. Buzby, J. C., Bentley, J. T., Padera Beth, Campuzano, J. & Ammon, C. Updated Supermarket Shrink Estimates for Fresh Foods and Their Implications for ERS Loss-Adjusted Food Availability Data (USDA, 2016); https://www.ers.usda.gov/publications/pub-details/?pubid=44103

  23. Cottier, W. The cold sterilization of oranges from South Africa. N.Z. Sci. Rev. 10, 99 (1952).

    Google Scholar 

  24. Pundir, A. K., Jagannath, J. D. & Ganapathy, L. Improving supply chain visibility using IoT—Internet of things. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 156–162 (Institute of Electrical and Electronics Engineers, 2019).

  25. Kamolov & Park, S. An IoT-based ship berthing method using a set of ultrasonic sensors. Sensors 19, 5181 (2019).

    Article  ADS  Google Scholar 

  26. Jedermann, R., Praeger, U. & Lang, W. Challenges and opportunities in remote monitoring of perishable products. Food Packag. Shelf Life 14, 18–25 (2017).

    Article  Google Scholar 

  27. Verzelen, F. Are ‘Virtual Twins’ Key to Achieving Sustainability Goals? (World Economic Forum, 2021); https://www.weforum.org/agenda/2021/01/virtual-twin-technology-sustainability-goals/

  28. Digitalization in Maritime Transport: Ensuring Opportunities for Development (United Nations Conference on Trade and Development, 2019); https://unctad.org/system/files/official-document/presspb2019d4_en.pdf

  29. Lind, M. et al. Digital data sharing: The ignored opportunity for making global maritime transport chains more efficient. UNCTAD Transp. Trade Facil. Newsl. 79, 22 (2018).

    Google Scholar 

  30. FAO Digital Services Portfolio (Food and Agriculture Organization of the United Nations, 2019); http://www.fao.org/digital-agriculture/digital-portfolio/en/

  31. Prause, L., Hackfort, S. & Lindgren, M. Digitalization and the third food regime. Agric. Human Values 1, 3 (2020).

    Google Scholar 

  32. Guidance on the Implementation of Articles 11, 12, 14, 17, 18, 19 and 20 of Regulation (EC) Number 178/2002 on General Food Law (European Commission Health and Consumers, 2010); https://ec.europa.eu/food/safety/general_food_law_en

  33. UNECE Standard FFV-14 cCncerning the Marketing and Commercial Quality Control of Citrus Fruit (United Nations Economic Commission for Europe, 2009); https://unece.org/fileadmin/DAM/trade/agr/meetings/ge.01/2010/INF2e_CitrusFruit.pdf

  34. Getahun, S., Ambaw, A., Delele, M., Meyer, C. J. & Opara, U. L. Analysis of airflow and heat transfer inside fruit packed refrigerated shipping container: Part I—model development and validation. J. Food Eng. 203, 58–68 (2017).

    Article  Google Scholar 

  35. Senguttuvan, S., Youn, J. S., Park, J., Lee, J. & Kim, S. M. Enhanced airflow in a refrigerated container by improving the refrigeration unit design. Int. J. Refrig. 120, 460–473 (2020).

    Article  Google Scholar 

  36. Jiang, T. et al. Analysis of an internal structure for refrigerated container: improving distribution of cooling capacity. Int. J. Refrig. 113, 228–238 (2020).

    Article  Google Scholar 

  37. van Boekel, M. A. J. S. Kinetic modeling of food quality: a critical review. Compr. Rev. Food Sci. Food Saf. 7, 144–158 (2008).

    Article  Google Scholar 

  38. Nunes, R. V., Swartzel, K. R. & Ollis, D. F. Thermal evaluation of food processes: the role of a reference temperature. J. Food Eng. 20, 1–15 (1993).

    Article  Google Scholar 

  39. Cold Treatment Container Protocols (Perishable Products Exports Control Board, 2019); https://ppecb.com/wp-content/uploads/2019/04/new-Q25-Cold-Treatment-Protocols-Yellow-Card-bh-08042019.pdf

  40. Procedure for In-Transit Cold Treatment of Specified Quarantine Pests in Citrus Shipped from South African Ports to the Republic of China (Perishable Products Exports Control Board, 2007).

  41. van Boekel, M. A. J. S. On the pros and cons of Bayesian kinetic modeling in food science. Trends Food Sci. Technol. 99, 181–193 (2020).

    Article  Google Scholar 

  42. Krauss, M., Tappe, K., Schuppert, A., Kuepfer, L. & Goerlitz, L. Bayesian population physiologically-based pharmacokinetic (PBPK) approach for a physiologically realistic characterization of interindividual variability in clinically relevant populations. PLoS ONE 10, e0139423 (2015).

    Article  Google Scholar 

  43. Heap, R. & Marshall, R. in International Congress of Refrigeration (International Institute of Refrigeration, 2003).

  44. Paull, R. E. Effect of temperature and relative humidity on fresh commodity quality. Postharvest Biol. Technol. 15, 263–277 (1999).

    Article  Google Scholar 

  45. Aglave, B. in Handbook of Plant Disease Identification and Management 129–176 (CRC Press, 2018).

  46. Adaskaveg, J. E., Förster, H. & Mauk, P. A. in Citrus Production Manual (eds. Ferguson, L. & Grafton-Cardwell, E. E.) 307–315 (UCANR Publications, 2014).

  47. Dalla Pria, M., Christiano, R. C. S., Furtado, E. L., Amorim, L. & Bergamin Filho, A. Effect of temperature and leaf wetness duration on infection of sweet oranges by Asiatic citrus canker. Plant Pathol. 55, 657–663 (2006).

    Article  Google Scholar 

  48. Corradini, M. G. Shelf life of food products: from open labeling to real-time measurements. Annu. Rev. Food Sci. Technol. 9, 251–269 (2018).

    Article  Google Scholar 

  49. Tijskens, L. M. M. & Polderdijk, J. J. A generic model for keeping quality of vegetable produce during storage and distribution. Agric. Syst. 51, 431–452 (1996).

    Article  Google Scholar 

  50. PT 24: Cold Treatment for Ceratitis capitata on Citrus sinensis—International Plant Protection Convention (FAO-IPCC, 2018); https://www.ippc.int/en/publications/84350/

  51. Lado, J., Cronje, P. J., Rodrigo, M. J. & Zacarías, L. in Postharvest Physiological Disorders in Fruits and Vegetables (ed de Freitas, S. T., Pareek, S.) 377–398 (CRC Press, 2019).

  52. Wills, R. B. H. in Postharvest: An Introduction to the Physiology and Handling of Fruit and Vegetables (Ed. Wills, R. B. H. et al.)130–143 (Van Nostrand Reinhold, 1989).

  53. Martin, N. A. & Falder, S. A review of the evidence for threshold of burn injury. Burns 43, 1624–1639 (2017).

    Article  CAS  Google Scholar 

  54. Yuen, C. M. C., Tridjaja, N. O., Wills, R. B. H. & Wild, B. L. Chilling injury development of ‘Tahitian’ lime, ‘Emperor’ mandarin, ‘Marsh’ grapefruit and ‘Valencia’ orange. J. Sci. Food Agric. 67, 335–339 (1995).

    Article  CAS  Google Scholar 

  55. 2013 ASHRAE Handbook: Fundamentals (ASHRAE, 2013).

  56. Peterson, B. G. et al. PerformanceAnalytics: Econometric tools for performance and risk analysis. R version 2.0.4 https://cran.r-project.org/web/packages/PerformanceAnalytics/index.html (2020).

  57. Petroni, A. & Braglia, M. Vendor selection using principal component analysis. J. Supply Chain Manag. 36, 63–69 (2000).

    Article  Google Scholar 

  58. Genovese, A., Morris, J., Piccolo, C. & Koh, S. C. L. Assessing redundancies in environmental performance measures for supply chains. J. Clean. Prod. 167, 1290–1302 (2017).

    Article  Google Scholar 

  59. Le, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).

    Article  Google Scholar 

  60. Alboukadel, K. Factoextra: Extract and visualize the results of multivariate data analyse. R version 1.0.7 https://cran.r-project.org/package=factoextra (2020).

  61. Kuznar, L. A. & Frederick, W. G. Environmental constraints and sigmoid utility: implications for value, risk sensitivity, and social status. Ecol. Econ. 46, 293–306 (2003).

    Article  Google Scholar 

  62. Díaz-Pérez, M., Carreño-Ortega, Á., Gómez-Galán, M. & Callejón-Ferre, Á.-J. Marketability probability study of cherry tomato cultivars based on logistic regression models. Agronomy 8, 176 (2018).

    Article  Google Scholar 

  63. Hattingh, M. & Uys, D. In-season retail sales forecasting using survival models. ORiON 30, 59-71 (2014).

  64. Silapeux, A. G. K., Ponka, R., Frazzoli, C. & Fokou, E. Waste of fresh fruits in Yaoundé, Cameroon: challenges for retailers and impacts on consumer health. Agriculture 11(2) (2021).

  65. Berry, T. M. Optimisation of Multi-Scale Ventilated Package Design for Next-Generation Cold Chain Strategies of Horticulture Produce (Stellenbosch Univ., 2017).

Download references

Acknowledgements

We thank T. Grout and V. Hattingh for their helpful suggestions. We also thank F. Bahrami for her editorial inputs to this manuscript. We acknowledge the support of K. Shoji in the exploratory work in data processing. The contribution of C.S. was funded by the Swiss National Science Foundation SNSF (project 200021_169372).

Author information

Authors and Affiliations

  1. Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Biomimetic Membranes and Textiles, St. Gallen, Switzerland

    Chandrima Shrivastava, Seraina Schudel & Thijs Defraeye

  2. University of Bern, ARTORG Center for Biomedical Engineering Research, Bern, Switzerland

    Chandrima Shrivastava

  3. Citrus Research International, Department of Horticultural Science, University of Stellenbosch, Stellenbosch, South Africa

    Tarl Berry & Paul Cronje

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  1. Chandrima Shrivastava
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Contributions

T.D. conceptualized this study and performed project administration. T.D. wrote the proposal and secured funding for this project. T.D. and C.S. developed the methodology for this study. T.B. and P.C. collected data for citrus cold chains. C.S. performed the simulations, data analysis, interpretation and visualization of results. T.D. supervised the work of C.S. C.S. wrote the original draft of the manuscript with key inputs from T.D. S.S., T.B., P.C. and T.D. critically reviewed and edited the manuscript, and C.S. revised the manuscript based on their suggestions.

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Correspondence to Thijs Defraeye.

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Nature Food thanks Ettore Settanni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Variability in input dataset.

Variability in temporal and hygrothermal history of the shipments: (a) Shipment length (days), (b) Average temperature of delivery air (°C), (c) Deviation in delivery air temperature (°C), (d) Average relative humidity of delivery air (%), (e) Deviation in relative humidity of delivery air for the 47 shipments analyzed in this study. Here, the color scale varies from favorable (green) to unfavorable conditions (red). The number over the bar indicates the number of shipments falling into a particular histogram bin. The spread is expressed as a box plot, where the area within the box indicates the interquartile range (IQR = Q3-Q1). The whiskers indicate the minima and maxima of the spread, computed as (Q1 – 1.5xIQR and Q3+1.5xIQR). Outliers are plotted as individual points.

Extended Data Fig. 2 Quantile table for the input parameters and metrics of quality and marketability.

Table presenting the quantiles for the input parameters and metrics of quality and marketability estimated by the digital twin for the 47 shipments.

Extended Data Fig. 3 Mean, standard deviation, and coefficient of variation for the input parameters and metrics of quality and marketability.

Table presenting the mean, standard deviation, and coefficient of variation for the input parameters and metrics of quality and marketability estimated by the digital twin for the 47 shipments.

Extended Data Fig. 4 Goodness-of-fit test, skewness, and kurtosis for the fitted distribution for the input parameters and metrics of quality and marketability.

Table presenting the goodness-of-fit test, skewness, and kurtosis for the fitted distribution for the input parameters and metrics of quality and marketability. Here * indicates that the Chi-squared test is significant (p<0.05), so here the fitted distribution differs significantly from the population. # indicates that the distribution is significantly skewed. A skewness between -0.5 and 0.5 indicates a fairly symmetric distribution, skewness in the range (-1, -0.5) and (0.5, 1) indicates moderately skewed data, and skewness <-1 or >+1 indicates a highly skewed distribution. $ indicates kurtosis is significant. A large, positive kurtosis indicates a leptokurtic distribution (high peak and skinny tails), whereas a small kurtosis (<0) indicates a platykurtic distribution (low peak and heavy tail). Values of skewness and kurtosis close to zero indicate a normal distribution.

Extended Data Fig. 5 Model parameters, constants and variables of interest for the physics-driven model of citrus fruit in a refrigerated container.

Table listing the model parameters, constants and variables of interest for the physics-driven model of citrus fruit in a refrigerated container.

Extended Data Fig. 6 Death kinetics of Mediterranean fruit fly (MFF) larvae.

Death kinetics of Mediterranean fruit fly (MFF) larvae: (a) Survivor plot of MFF larvae on a logarithmic scale versus the time of exposure (days) at three temperatures (1, 2 and 3 °C). Here, the dotted red line indicates the Probit 9 requirement (99.99683% mortality at 95% confidence interval); (b) Corresponding decimal reduction times or D-value (DT, day) for estimating the temperature dependence as z-value. The z-value is the temperature difference required to change the D-value by a factor of 10.

Extended Data Fig. 7 Comparison between the predicted values and the observed values from experiments and literature for chilling injury in lemon.

Comparison between the predicted values and the observed values from experiments and literature for chilling injury in lemons: (a) Chilling injury index (CI index) takes values between 0 and 3, and is representative of the surface area of the rind that is inflicted with chilling injury. The experiments were conducted for 10 replicates. Experimental data points are plotted as blue dots and the error bars represent the standard deviation. Experiments were conducted on ‘Eureka’ lemon fruit stored at -0.6, 2, and 7 °C for 0, 12, 22, 32, and 42 days. (b) Probability of incidence of chilling injury, expressed as percentage. Error bars are not relevant here for the experimental data, as it was computed as a percentage out of ten replicates. The figure is discussed in the Supplementary information (Section S8). RMSE is the root mean squared error. R2 is the coefficient of determination ranging from 0 to 1 that measures how well the model predicts new observations.

Extended Data Fig. 8 Calibrated thermal damage model for ‘Valencia’ orange.

Calibrated thermal damage model for ‘Valencia’ orange: (a) Iso-effect lines for various combinations of time and temperature exposure, for the severity of chilling injury symptoms, expressed as chilling injury index {0, 1, 2, 3}. (b) Comparison of the predicted values for chilling injury index with other values reported in literature. Iso-effect contours for (c) damage integral (Ω), (d) chilling injury index, and (e) percentage of chilling injury inflicted fruits (%) from the thermal damage model calibrated for ‘Valencia’ orange. The figure is discussed in the Supplementary information (Section S8). RMSE is the root mean squared error. R2 is the coefficient of determination ranging from 0 to 1 that measures how well the model predicts new observations.

Extended Data Fig. 9 Validation of the digital twin for the boxes at the bottom-most pallet layers of the container, near the container floor.

Validation of the digital twin for the boxes at the bottom-most pallet layers of the container, near the container floor: A comparison between measured values for a fruit box in the bottom layer of pallets 1, 5, 9, 14, 17, and 20 and the values predicted by the digital twin based on two sensors measuring delivery air data: (a) Fruit core temperature (°C) - the dotted red lines represent the fruit core temperature predicted by the digital twin based on data from two sensors measuring delivery air temperature and humidity, while the yellow lines correspond to measured fruit core temperature; (b) Moisture loss (%), and the dotted line corresponds to the typically acceptable limit for moisture loss, which is 5%; (c) Box plot for moisture loss (%); (d) Fruit with symptoms of chilling injury (%), here the dotted lines correspond to every additional fruit showing chilling injury; (e) Box plot for fruit with chilling injury (%); (f) Pest mortality (%), and the dotted line corresponds to Probit 9 pest mortality, which is 99.99683% mortality at 95% confidence interval; (g) Box plot for pest mortality. Statistically significant differences were evaluated at a significance level of 0.05 (n.s.: not significant and p>0.05; *: p<0.05; ***: p<0.01). For (c), (e), and (g), the spread is expressed as a box plot, where the area within the box indicates the interquartile range (IQR = Q3-Q1). The whiskers indicate the minima and maxima of the spread, computed as (Q1 – 1.5xIQR and Q3+1.5xIQR). The data are also plotted as individual points.

Extended Data Fig. 10 Validation of the digital twin for the boxes in the middle layer of pallets in the container.

Validation of the digital twin for the boxes in the middle layer of pallets in the container: A comparison between measured values and the values predicted by the digital twin for fruit next to a sensor in the middle layer of pallets 1, 5, 9, 14, 17, and 20: (a) Moisture loss (%), (b) Fruit with symptoms of chilling injury (%); (c) Pest mortality (%); (d) Microbiological risk due to condensation.

Supplementary information

Supplementary Information

Supplementary sections S1–S14, Figs. S1–S6 and Tables S1–S2.

Reporting Summary

Source data

Source Data Fig. 2

Delivery air temperature data for 47 shipments.

Source Data Fig. 4

Fruit quality data for 47 shipments computed by the digital twin.

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Shrivastava, C., Berry, T., Cronje, P. et al. Digital twins enable the quantification of the trade-offs in maintaining citrus quality and marketability in the refrigerated supply chain. Nat Food 3, 413–427 (2022). https://doi.org/10.1038/s43016-022-00497-9

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