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Włodarska Katarzyna (Institute of Quality Science, Poznań University of Economics and Business, Poland), Pawlak-Lemańska Katarzyna (Institute of Quality Science, Poznań University of Economics and Business, Poland), Sikorska Ewa (Institute of Quality Science, Poznań University of Economics and Business, Poland)
NIR Technology for Non-destructive Monitoring of aApple Quality During Storage
LogForum, 2024, vol. 20, nr 1, s. 11-21, tab.,bibliogr. 29 poz.
Łańcuch dostaw, Rozwój zrównoważony, Żywność, Jakość, Straty gospodarcze
Supply chain, Sustainable development, Food, Quality, Economic losses
. Background: Post-harvest losses are a major obstacle in achieving sustainable fresh produce chains. The causes of high waste are various and include inadequate logistics and quality control. Therefore, the research and development of rapid and accurate tools for fruit quality control is crucial for food quality assurance. Near-infrared (NIR) spectroscopy has become remarkably valuable in the agri-food sector. The aim of this study was to test NIR coupled with multivariate data analysis as a non-destructive tool to monitor apple quality during short-term storage. Methods: NIR was used to test apples (n=171) from four varieties and with varying levels of freshness. Each sample was measured immediately after harvest (at time T0) and after 14 days of storage at 10 ˚C (T14) in a non-destructive manner. Pattern recognition techniques including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify the apples. Variable importance in projection (VIP) was used to identify NIR spectra ranges that contributed significantly to the discrimination between fresh and stored apples. Results: The classification model distinguishing apples according to variety was characterized by high classification performance reflected in a misclassification error below 1%. The model for apple freshness discrimination also showed good classification performance with errors at the level of 7.9% and 5.8% for validation and prediction, respectively. The global model of eight classes including both apple variety and freshness was characterized by misclassification errors in the range of 1.2-6.3% for validation and 2.0-3.9% for prediction. The VIP method revealed that the spectral ranges contributing significantly to the freshness classification mainly corresponded to the absorption of water. Conclusions: NIR technology coupled with pattern recognition methods was found to be a promising tool for monitoring the overall loss of quality in fresh apples during short-term storage. The results may contribute to the development of a system supporting apple quality control and logistics during storage, e.g., while awaiting sale, transport or further processing. The findings are intended to guide various supply chain members and decision-makers so they can reduce post****harvest losses and improve the performance of the short fruit supply chain.(original abstract)
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  1. Anand S., Barua M. K., 2022, Modeling the key factors leading to post-harvest loss and waste of fruits and vegetables in the agrifresh produce supply chain, Computers and Electronics in Agriculture, 198, 106936, 2.106936
  2. Beghi R., Giovanelli G., Malegori C., Giovenzana V., Guidetti R., 2014, Testing of a VIS-NIR system for the monitoring of long-term apple storage, Food and Bioprocess Technology, 7(7), 2134-2143, 1294-x
  3. Cédric C., Pascale G., Mauget J., Bertrand D., 2007, Discrimination of storage duration of apples stored in a cooled room and shelf by visible-near infrared spectroscopy, Journal of Near Infrared Spectroscopy, 15, 169-177,
  4. Chong I.-G., Jun C.-H., 2005, Performance of some variable selection methods when multicollinearity is present, Chemometrics and Intelligent Laboratory Systems, 78(1), 103-112, 04.12.011
  5. Cozzolino D., 2022, Advantages, opportunities, and challenges of vibrational spectroscopy as tool to monitor sustainable food systems, Food Analytical Methods, 15(5), 1390- 1396, 021-02207-w
  6. Cyplik P., Zwolak M., 2022, Industry 4.0 and 3D print: a new heuristic approach for decoupling point in future supply chain management, LogForum, 18 (2), 161-171. 733
  7. Enthoven L., Skambracks M., Van den Broeck G., 2023, Improving the design of local short food supply chains: Farmers' views in Wallonia, Belgium, Journal of Rural Studies, 97, 573-582, 3.01.016
  8. Evola R. S., Peira G., Varese E., Bonadonna A., Vesce E., 2022, Short food supply chains in Europe: scientific research directions, Sustainability, 14, 3602,
  9. Euromonitor International. (2023). Production of apples in Poland. Data set, Passport. Available on the Internet: csevolution/index (25/10/2023).
  10. Gardas B. B., Raut R. D., Narkhede B., 2018, Evaluating critical causal factors for post harvest losses (PHL) in the fruit and vegetables supply chain in India using the DEMATEL approach, Journal of Cleaner Production, 199, 47-61, ro.2018.07.153
  11. Hassoun A., Jagtap S., Garcia-Garcia G., Trollman H., Pateiro M., Lorenzo J. M., Trif M., Rusu A. V., Aadil R. M., Šimat V., Cropotova J., Câmara J. S., 2023, Food quality 4.0: From traditional approaches to digitalized automated analysis, Journal of Food Engineering, 337, 111216, 2.111216
  12. Ignat T., Lurie S., Nyasordzi J., Ostrovsky V., Egozi H., Hoffman A., Friedman H., Weksler A., Schmilovitch Z., 2014, Forecast of apple internal quality indices at harvest and during storage by VIS-NIR spectroscopy, Food and Bioprocess Technology, 7, 2951-2961, 1297-7
  13. Jagtap S., Bader F., Garcia-Garcia G., Trollman H., Fadiji T., Salonitis K., 2021, Food Logistics 4.0: Opportunities and challenges, Logistics, 5, 2, 02
  14. Kaipia R., Dukovska-Popovska I., Loikkanen L., 2013, Creating sustainable fresh food supply chains through waste reduction, International Journal of Physical Distribution & Logistics Management, 43, 262-276, 2011-0200
  15. Kapoor R., Malvandi A., Feng H., Kamruzzaman M., 2022, Real-time moisture monitoring of edible coated apple chips during hot air drying using miniature NIR spectroscopy and chemometrics, LWT - Food Science and Technology, 154, 112602, 602
  16. Lahane S., Paliwal V., Kant R., 2023, Evaluation and ranking of solutions to overcome the barriers of Industry 4.0 enabled sustainable food supply chain adoption, Cleaner Logistics and Supply Chain, 8, 100116, 00116
  17. Loiseau E., Colin M., Alaphilippe A., Coste G., Roux P., 2020, To what extent are short food supply chains (SFSCs) environmentally friendly? Application to French apple distribution using Life Cycle Assessment, Journal of Cleaner Production, 276, 124166, 124166
  18. Macheka L., Spelt E., van der Vorst J. G. A. J., Luning, P. A., 2017, Exploration of logistics and quality control activities in view of context characteristics and postharvest losses in fresh produce chains: A case study for tomatoes, Food Control, 77, 221-234, 7.02.037
  19. Michel-Villarreal R., Vilalta-Perdomo E. L., Canavari M., Hingley M., 2021, Resilience and digitalization in short food supply chains: A case study approach, Sustainability, 13, 5913,
  20. Negi S., Trivedi S., 2021, Factors impacting the quality of fresh produce in transportation and their mitigation strategies: empirical evidence from a developing economy, Journal of Agribusiness in Developing and Emerging Economies, 11, 121-139, 2020-0154
  21. Pissard A., Marques E. J. N., Dardenne P., Lateur M., Pasquini C., Pimentel M. F., Fernández Pierna J. A., Baeten V., 2021, Evaluation of a handheld ultra-compact NIR spectrometer for rapid and non-destructive determination of apple fruit quality, Postharvest Biology and Technology, 172, 111375, 2020.111375
  22. Sottocornola G., Baric S., Nocker M., Stella F., Zanker M., 2023, DSSApple: A hybrid expert system for the diagnosis of postharvest diseases of apple, Smart Agricultural Technology, 3, 100070, 00070
  23. Tiganis A., Grigoroudis E., Chrysochou P., 2023, Customer satisfaction in short food supply chains: A multiple criteria decision analysis approach, Food Quality and Preference, 104, 104750, 2.104750
  24. Vincent J., Wang H., Nibouche O., Maguire P., 2018, Differentiation of apple varieties and investigation of organic status using portable visible range reflectance spectroscopy, Sensors, 18, 1708,
  25. Włodarska K., Piasecki P., Lobo-Prieto A., Pawlak-Lemańska K., Górecki T., Sikorska E., 2021, Rapid screening of apple juice quality using ultraviolet, visible, and near infrared spectroscopy and chemometrics: A comparative study, Microchemical Journal, 164, 106051, 106051
  26. Yao Y., Ma K., Zhu J., Huang F., Kuang L., Wang X., Li S., 2023, Non-destructive determination of soluble solids content in intact apples using a self-made portable NIR diffuse reflectance instrument, Infrared Physics & Technology, 132, 104714, 3.104714
  27. Zhang B., Zhang M., Shen M., Li H., Zhang Z., Zhang H., Zhou Z., Ren X., Ding Y., Xing L., Zhao J., 2021, Quality monitoring method for apples of different maturity under long-term cold storage, Infrared Physics & Technology, 112, 103580, 0.103580
  28. Zhang M., Shen M., Li H., Zhang B., Zhang Z., Quan P., Ren X., Xing L., Zhao J., 2022, Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 267, 120598, 598
  29. Zhu G., Tian C., 2018, Determining sugar content and firmness of 'Fuji' apples by using portable near-infrared spectrometer and diffuse transmittance spectroscopy, Journal of Food Process Engineering, 41, 12810,
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