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Efficient post-harvest sorting of kiwiberry (Actinidia arguta) is essential for maintaining fruit quality and prolonging the viability of storage and transport. Because kiwiberry is climacteric, mixing ripe and unripe fruits may accelerate over-ripening and shorten shelf life, creating challenges for commercial distribution. This study investigates the integration of hyperspectral imaging and predictive modelling to automate the sorting of ripeness in a prototype device designed for handling kiwiberry. Hyperspectral data from 1,770 fruits were processed using transformation techniques, including Standard Normal Variate, Multiplicative Scatter Correction, Savitzky-Golay filtering, and spectral derivatives. Three regression models - Multivariate Adaptive Regression Splines, Partial Least Squares Regression, and Principal Component Regression - were evaluated to predict soluble solids content as an indicator of ripeness. Based on raw spectra, the Partial Least Squares Regression model obtained the highest accuracy, achieving a determination coefficient of 0.95, root mean squared error for prediction of 0.6778, and residual prediction deviation of 4.18 on the test set. The developed system provides a foundation for real-time, non-invasive sorting, enhancing post-harvest management, reducing waste, and advancing automated fruit sorting technologies as a practical solution for optimizing supply chain logistics in kiwiberry production.
Wydawca
Rocznik
Tom
Strony
50--64
Opis fizyczny
Bibliogr. 38 poz., fig., tab.
Twórcy
- Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska 164, 02-787 Warsaw, Poland
autor
- Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska 164, 02-787 Warsaw, Poland
Bibliografia
- 1. Latocha P. The nutritional and health benefits of kiwiberry (Actinidia arguta) - a Review. Plant Foods for Human Nutrition, 2017; 72(4): 325–334. https://www.doi.org/10.1007/s11130-017-0637-y.
- 2. Latocha P. Some morphological and biological features of ‘Bingo’ – a new hardy kiwifruit cultivar from Warsaw University of Life Sciences (WULS) in Poland. Yearbook of the Polish Dendrological Society, 2012; 60: 61–67.
- 3. Latocha P., Łata B., Stasiak A. Phenolics, ascorbate, and the antioxidant potential of kiwiberry vs. common kiwifruit: The effect of cultivar and tissue type. Journal of Functional Foods, 2015; 19: 155–163. https://www.doi.org/10.1016/j.jff.2015.09.024.
- 4. Leontowicz H., Leontowicz M., Latocha P., Jesion I., Park Y.S., Katrich E., Barasch D., Nemiroski A., Gorinstein S. Bioactivity and nutritional properties of hardy kiwi fruit Actinidia arguta in comparison with Actinidia deliciosa ‘Hayward’ and Actinidia eriantha ‘Bidan’. Food Chemistry, 2016; 196: 281–291. https://www.doi.org/10.1016/j.foodchem.2015.08.127.
- 5. Latocha P., Debersaques F., Decorte, J. Varietal differences in the mineral composition of kiwiberry - Actinidia arguta (Siebold et Zucc.) Planch. ex Miq. Acta Horticulturae, 2015; 1096: 479–486. https://www.doi.org/10.17660/ActaHortic.2015.1096.59.
- 6. Pinto D., Delerue-Matos C., Rodrigues F. Bioactivity, phytochemical profile, and pro-healthy properties of Actinidia arguta: A review. Food Research International, 2020; 136: 109449. https://www.doi.org/10.1016/j.foodres.2020.109449.
- 7. Wojdyło A., Nowicka P., Oszmiański J., Golis T. Phytochemical compounds and biological effects of Actinidia fruits. Journal of Functional Foods, 2017; 30: 194–202. https://www.doi.org/10.1016/j.jff.2017.01.018.
- 8. Fisk C.L., McDaniel M.R., Strik B.C., Zhao Y. Physicochemical, sensory, and nutritive qualities of hardy kiwifruit (Actinidia arguta ‘Ananasnaya’) as affected by harvest maturity and storage. Journal of Food Science, 2006; 71(3): S204–S210. https://www.doi.org/10.1111/j.1365-2621.2006.tb15642.x.
- 9. Latocha P., Krupa T., Jankowski P., Radzanowska J. Changes in postharvest physicochemical and sensory characteristics of hardy kiwifruit (Actinidia arguta and its hybrid) after cold storage under normal versus controlled atmosphere. Postharvest Biology and Technology, 2014; 88: 21–33. https://www.doi.org/10.1016/j.postharvbio.2013.09.005.
- 10. Boyes S., Strübi P., Marsh H. Sugar and organic acid analysis of Actinidia arguta and rootstock-scion combinations of Actinidia arguta. Lebensmittel-Wissenschaft und -Technologie, 1996; 30(4): 390–397. https://www.doi.org/10.1006/fstl.1996.0201.
- 11. Nishiyama I., Fukuda T., Shimohashi A., Oota T. Sugar and organic acid composition in the fruit juice of different Actinidia varieties. Food Science and Technology Research, 2008; 14(1): 67–73. https://www.doi.org/10.3136/fstr.14.67.
- 12. Stefaniak J., Przybył J.L., Latocha P., Łata B. Bioactive compounds, total antioxidant capacity and yield of kiwiberry fruit under different nitrogen regimes in field conditions. Journal of the Science of Food and Agriculture, 2020; 100: 3832–3840. https://www.doi.org/10.1002/jsfa.10420.
- 13. Xiong Z., Xie A., Sun D.-W., Zeng X.-A., Liu D. Applications of hyperspectral imaging in chicken meat safety and quality detection and evaluation: A review. Critical Reviews in Food Science and Nutrition, 2015; 55: 1287–1301. https://www.doi.org/10.1080/10408398.2013.834875.
- 14. Geladi P., MacDougall D., Martens H. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Applied Spectroscopy, 1985; 39(3): 491–500. https://www.doi.org/10.1366/0003702854248656.
- 15. Barnes R.J., Dhanoa M.S., Lister S.J. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Applied Spectroscopy, 1989; 43(5): 772–777. https://www.doi.org/10.1366/0003702894202201.
- 16. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 1964; 36(8): 1627–1639. https://www.doi.org/10.1021/ac60214a047.
- 17. Maleki M.R., Mouazen A.M., Ramon H., De Baerdemaeker J. Multiplicative scatter correction during on-line measurement with near infrared spectroscopy. Biosystems Engineering, 2007; 96(3): 427–433. https://www.doi.org/10.1016/j.biosystemseng.2006.11.014.
- 18. Witteveen M., Sterenborg H.J.C.M., van Leeuwen T.G., Aalders M.C.G., Ruers T.J.M., Post A.L. Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging. Journal of Biomedical Optics, 2022; 27(10): 106003. https://www.doi.org/10.1117/1.JBO.27.10.106003.
- 19. Kucheryavskiy S. mdatools – R package for chemometrics. Chemometrics and Intelligent Laboratory Systems, 2020; 198: 103937. https://www.doi.org/10.1016/j.chemolab.2020.103937.
- 20. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2023. https://www.R-project.org.
- 21. Milborrow S. Derived from mda:mars by T. Hastie and R. Tibshirani. earth: Multivariate Adaptive Regression Splines, 2011. R package. http://CRAN.R-project.org/package=earth.
- 22. Liland K., Mevik B., Wehrens R. pls: Partial Least Squares and Principal Component Regression. R package version 2.8–3. 2023. https://CRAN.R-project.org/package=pls.
- 23. Hastie T., Tibshirani R., Friedman J. H. The elements of statistical learning: Data mining, inference, and prediction, Second Edition. Springer-Verlag New York Inc., 2009.
- 24. Lee S., Sarkar S., Park Y., Yang J., Kweon G. Feasibility study for an optical sensing system for hardy kiwi (Actinidia arguta) sugar content estimation. Journal of Agriculture & Life Science, 2019; 53(3): 147–157. https://www.doi.org/10.14397/jals.2019.53.3.147.
- 25. Mumford A., Abrahamsson Z., Hale I. Predicting soluble solids concentration of ‘Geneva 3’ kiwiberries using near infrared spectroscopy. HortTechnology, 2024; 34(2): 172–180. https://www.doi.org/10.21273/HORTTECH05316-23.
- 26. Galindo-Prieto B., Eriksson L., Trygg J. Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS). Journal of Chemometrics, 2014; 28(8): 623–632. https://www.doi.org/10.1002/cem.2627.
- 27. Eriksson L., Johansson E., Kettaneh-Wold N., Wold S. Multi- and Megavariate Data Analysis. Principles and Applications. Umetrics Academy, Umeå, Sweden, 2002.
- 28. Xu L., Chen Y., Wang X., Chen H., Tang Z., Shi X., Chen X., Wang Y., Kang Z., Zou Z., Huang P., He Y., Yang N., Zhao Y. Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging. Frontiers in Plant Science, 2023; 13: 1075929. https://www.doi.org/10.3389/fpls.2022.1075929.
- 29. Kim J.G., Park Y., Shin M.H., Muneer S., Lerud R., Michelson C., Il Kang D., Min J.H., Chamidha Kumarihami H.M.P. Application of NIR-Spectroscopy to predict the harvesting maturity, fruit ripening and storage ability of Ca-chitosan treated baby kiwifruit. Journal of Stored Products and Postharvest Research, 2018; 9(4): 44–53. https://www.doi.org/10.5897/JSPPR2018.0257.
- 30. Sarkar S., Basak J.K., Moon B.E., Kim H.T. A comparative study of PLSR and SVM-R with various preprocessing techniques for the quantitative determination of soluble solids content of hardy kiwi fruit by a portable Vis/NIR spectrometer. Foods, 2020; 9(8): 1078. https://www.doi.org/10.3390/foods9081078.
- 31. Wang H., Peng J., Xie C., Bao Y., He Y. Fruit quality evaluation using spectroscopy technology: A review. Sensors, 2015; 15: 11889–11927. https://www.doi.org/10.3390/s150511889.
- 32. Benelli A., Cevoli C., Fabbri A., Ragni L. Ripeness evaluation of kiwifruit by hyperspectral imaging. Biosystems Engineering, 2022; 223(B): 42–52. https://www.doi.org/10.1016/j.biosystemseng.2021.08.009.
- 33. Sharma S., K.C. Sumesh, Sirisomboon P. Rapid ripening stage classification and dry matter prediction of durian pulp using a pushbroom near infrared hyperspectral imaging system. Measurement, 2022; 189: 110464. https://www.doi.org/10.1016/j.measurement.2021.110464.
- 34. Tian P., Meng Q., Wu Z., Lin J., Huang X., Zhu H., Zhou X., Qiu Z., Huang Y., Li Y. Detection of mango soluble solid content using hyperspectral imaging technology. Infrared Physics & Technology, 2023; 129: 104576. https://www.doi.org/10.1016/j.infrared.2023.104576.
- 35. Moghimi A., Aghkhani M.H., Sazgarnia A., Sarmad M. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosystems Engineering, 2010; 106(3): 295–302. https://www.doi.org/10.1016/j.biosystemseng.2010.04.002.
- 36. Lee J.S., Kim S.-C., Seong K.C., Kim C.-H., Um Y.C., Lee S.-K. Quality prediction of kiwifruit based on near infrared spectroscopy. Korean Journal of Horticultural Science and Technology, 2012; 30(6): 709–717. https://www.doi.org/10.7235/hort.2012.12139.
- 37. Zhu H., Chu B., Fan Y., Tao X., Yin W., He Y. Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models. Scientific Reports, 2017; 7: 7845. https://www.doi.org/10.1038/s41598-017-08509-6.
- 38. Simpson E. H. The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B, 1951; 13: 238–241.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-caa8e9e3-96fe-44e2-b39d-aaafed128621
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