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Investigating the Effect of Ozonation on Mechanical Parameters of Lonicera caerulea L. Fruits Using Machine Learning

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09 juin 2025
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Aliasgarian, S., Ghassemzadeh, H.R., Moghaddam, M., & Ghaffari, H. (2015). Mechanical damage of strawberry during harvest and postharvest operations. Acta Technologica Agriculturae, 18, 1-5. https://doi.org/10.1515/ata-2015-0001 Search in Google Scholar

Auzanneau, N., Weber, P., Kosińska-Cagnazzo, A., & Andlauer, W. (2018). Bioactive compounds and antioxidant capacity of Lonicera caerulea berries: Comparison of seven cultivars over three harvesting years. Journal of Food Composition and Analysis, 66, 81-89. https://doi.org/10.1016/j.jfca.2017.12.006 Search in Google Scholar

Basara, O., & Gorzelany, J. (2024). Assessment of Selected Chemical and Morphological Properties of Lonicera var. kamtschatica and Lonicera var. emphyllocalyx Treated with Gaseous Ozone. Molecules, 29, 3616. https://doi.org/10.3390/molecules29153616 Search in Google Scholar

Becker, R., & Szakiel, A. (2019). Phytochemical characteristics and potential therapeutic properties of blue honeysuckle Lonicera caerulea L.(Caprifoliaceae). Journal of Herbal Medicine, 16, 100237. Search in Google Scholar

Bolandnazar, E., Rohani, A., & Taki, M. (2019). Energy consumption forecasting in agriculture by artificial intelligence and mathematical models. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(13), 1618–1632. https://doi.org/10.1080/15567036.2019.1604872 Search in Google Scholar

Bożek, M. (2012). The Effect of Pollinating Insects on Fruiting of Two Cultivars of Lonicera caerulea L. Journal of Apicultural Science, 56(2), 5-11. https://doi.org/10.2478/v10289-012-0018-6 Search in Google Scholar

Česonienė, L., Labokas, J., Jasutienė, I., Šarkinas, A., Kaškonienė, V., Kaškonas, P., Kazernavičiūtė, R., Pažereckaitė, A., & Daubaras, R. (2021). Bioactive Compounds, Antioxidant, and Antibacterial Properties of Lonicera caerulea Berries: Evaluation of 11 Cultivars. Plants, 10, 624. https://doi.org/10.3390/plants10040624 Search in Google Scholar

Cevher, E.Y., & Yildirim, D. (2022). Using Artificial Neural Network Application in Modeling the Mechanical Properties of Loading Position and Storage Duration of Pear Fruit. Processes, 10, 2245. Search in Google Scholar

Clay, D. E., Brugler, S., & Joshi, B. (2024). Will artificial intelligence and machine learning change agriculture: A special issue. Agronomy Journal, 116, 791–794. https://doi.org/10.1002/agj2.21555 Search in Google Scholar

Contigiani, E. V., Jaramillo-Sánchez, G., Castro, M. A., Gomez, P. L., & Alzamora, S. M. (2018). Postharvest quality of strawberry fruit (Fragaria x Ananassa Duch cv. Albion) as affected by ozone washing: Fungal spoilage, mechanical properties, and structure. Food and Bioprocess Technology, 11, 1639-1650. Search in Google Scholar

Duarte-Molina, F., Gómez, P.L., Castro, M.A., & Alzamora, S.M. (2016). Storage quality of strawberry fruit treated by pulsed light: Fungal decay, water loss and mechanical properties. Innovative Food Science & Emerging Technologies, 34, 267-274. https://doi.org/10.1016/j.ifset.2016.01.019. Search in Google Scholar

Dziedzic, E., Błaszczyk, J., Bieniasz, M., Dziadek, K., & Kopeć, A. (2020). Effect of modified (MAP) and controlled atmosphere (CA) storage on the quality and bioactive compounds of blue honeysuckle fruits (Lonicera caerulea L.). Scientia Horticulturae, 265, p.109226. https://doi.org/10.1016/j.scienta.2020.109226. Search in Google Scholar

El Bilali, A., Moukhliss, M., Taleb, A., Nafii, A., Alabjah, B., Brouziyne, Y., Mazigh, N., Teznine, K., & Mhamed, M. (2022). Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling. Environmental Science and Pollution Research, 29, 47382–47398. https://doi.org/10.1007/s11356-022-18559-7. Search in Google Scholar

Elibox, W., Meynard, C., & Umaharan, P. (2017). Fruit volume and width at harvest can be used to predict shelf life in pepper (Capsicum chinense Jacq.). Tropical Agriculture, 94(2), 122-131. https://doi.org/0041-3216/2017/020122-10. Search in Google Scholar

Gawroński, J., Hortynski, J., Kaczmarska, E., Dyduch-Sieminska, M., Marecki, W., & Witorozec, A. (2014). Evaluation of phenotypic and genotypic diversity of some Polish and Russian blue honeysuckle (Lonicera caerulea L.) cultivars and clones. Acta Scientiarum Polonorum Hortorum Cultus, 13, 157-169. Search in Google Scholar

Geasa, M. M. M. (2022). Effect of mechanical damage on tomato fruits under storage conditions. Journal of Soil Sciences and Agricultural Engineering, 13(3), 93-98. https://doi.org/10.21608/jssae.2022.124035.1053. Search in Google Scholar

Gorzelany, J., Belcar, J., Kuźniar, P., Niedbała G., & Pentoś K. (2022). Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning. Agriculture, 12, 200. https://doi.org/10.3390/agriculture12020200. Search in Google Scholar

Hosseini Monjezi, P., Taki, M., Abdanan Mehdizadeh, S., Rohani, A., & Ahamed, M.S. (2023). Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis. Horticulturae, 9, 853. https://doi.org/10.3390/horticulturae9080853. Search in Google Scholar

Hu, M., Dong, Q., Liu, B., & Opara, U.L. (2016). Prediction of mechanical properties of blueberry using hyperspectral interactance imaging. Postharvest Biology and Technology, 115, 122-131, https://doi.org/10.1016/j.postharvbio.2015.11.021. Search in Google Scholar

Huang, W., Wang, X., Zhang, J., Xia, J., & Zhang, X. (2023). Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics. Food Control, 145, 109496. https://doi.org/10.1016/j.foodcont.2022.109496. Search in Google Scholar

Kula, M., & Krauze-Baranowska, M. (2016). Blue Honeysuckle (Lonicera caerulea L.)-The current state of phytochemical research and biological activity. Post Fitoter, 17, 111-118. Search in Google Scholar

Kuźniar, P., Belcar, J., Zardzewiały, M., Basara, O., & Gorzelany, J. (2022). Effect of Ozonation on the Mechanical, Chemical, and Microbiological Properties of Organically Grown Red Currant (Ribes rubrum L.) Fruit. Molecules, 27, 8231. https://doi.org/10.3390/molecules27238231. Search in Google Scholar

Leisso, R., Jarrett, B, & Miller, Z. (2021b). Haskap Preharvest Fruit Drop and Stop-drop Treatment Testing. HortTechnology, 31(6), 820-827. https://doi.org/10.1139/cjps-2021-0138. Search in Google Scholar

Leisso, R., Jarrett, B., Richter, R., & Miller, Z. (2021a). Fresh haskap berry postharvest quality characteristics and storage life. Canadian Journal of Plant Science, 101(6), 1051-63. Search in Google Scholar

Lufu, R., Ambaw, A., & Opara, U. L. (2020). Water loss of fresh fruit: Influencing pre-harvest, harvest and postharvest factors. Scientia Horticulturae, 272, 109519. https://doi.org/10.1016/j.scienta.2020.109519. Search in Google Scholar

Lv, Y., Tahir, I.I., & Olsson, M.E. (2019). Effect of ozone application on bioactive compounds of apple fruit during short-term cold storage. Scientia Horticulturae, 253, 49–60. https://doi.org/10.1016/j.scienta.2019.04.021. Search in Google Scholar

Martinez Romero, D., Bailén, G., Serrano, M., Guillen, F., Valverde, J.M., Zapata, P., Castillo, S., & Valero, D. (2007). Tool to maintain postharvest fruit and vegetable quality through the inhibition of ethylene action: a review. Critical reviews in food science and nutrition, 47 (6), 543-560. https://doi.org/10.1080/10408390600846390. Search in Google Scholar

Miller, F.A., Silva, C.L., & Brandao, T.R. (2013). A review on ozone-based treatments for fruit and vegetables preservation. Food Engineering Reviews, 5(2), 77-106. Search in Google Scholar

Moggia, C., Beaudry, R.M., Retamales, J.B., & Lobos, G.A. (2017). Variation in the impact of stem scar and cuticle on water loss in highbush blueberry fruit argue for the use of water permeance as a selection criterion in breeding. Postharvest Biology and Technology, 132, 88-96. https://doi.org/10.1016/j.postharvbio.2017.05.019. Search in Google Scholar

Mohsen, A., Shabankareh, S.H., Kiapey, A., Rezaeiasl, A., Mahmoodi, M.J., & Torshizi, M.V. (2024). Assessing kiwifruit quality in storage through machine learning. Journal of Food Process Engineering, 47. https://doi.org/10.1111/jfpe.14681. Search in Google Scholar

Nawaz, M., & Babar, M.I.K. (2024). IoT and AI: a panacea for climate change-resilient smart agriculture. Discover Applied Sciences, 6, 517. https://doi.org/10.1007/s42452-024-06228-y. Search in Google Scholar

Ngcobo, M.E., Delele, M.A., Opara, U.L., Zietsman, C.J., & Meyer C.J. (2012). Resistance to airflow and cooling patterns through multi-scale packaging of table grapes. International Journal of Refrigeration, 35(2), 445-452. https://doi.org/10.1016/j.ijrefrig.2011.11.008. Search in Google Scholar

Niedbała, G., Kurek, J., Świderski, B., Wojciechowski, T., Antoniuk, I., & Bobran K. (2022). Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods. Agriculture, 12, 2089. https://doi.org/10.3390/agriculture12122089. Search in Google Scholar

Ochmian, I., Skupien, K. Grajkowski, J., Smolik, M., & Ostrowska, K. (2012). Chemical composition and physical characteristics of fruits of two cultivars of blue honeysuckle (Lonicera caerulea L.) in relation to their degree of maturity and harvest date. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 40, 155-162. https://doi.org/10.15835/nbha4017314. Search in Google Scholar

Pan, H., Yang, J., Shi, Y., & Li, T. (2015). BP neural network application model of predicting the apple hardness. Journal of Computational and Theoretical Nanoscience, 12(9), 2802–2807. https://doi.org/10.1166/jctn.2015.4180. Search in Google Scholar

Peipei, Z., Bingbing, S., Hongwei, Ji., Wang, H., Yuexuan, L., Zhang, X., & Chunhua, R. (2022). Nondestructive Prediction of Mechanical Parameters to Apple Using Hyperspectral Imaging by Support Vector Machine. Food Analytical Methods, 1, 1397-1406. Search in Google Scholar

PN-90/A-75101-03:1990. Fruit and vegetable preserves. Preparation of samples for physicochemical tests. Determination of dry matter content by the gravimetric method. Polski Komitet Normalizacyjny. Search in Google Scholar

Ranieri, A., Petacco, F., Castagna, A., & Soldatini, G.F. (2000). Redox state and peroxidase system in sunflower plants exposed to ozone. Plant Science, 159(1), 159-167. Search in Google Scholar

Ren, B., Zhang, L., Chen, J., Wang, H., Bian, C., Shi, Y., Qin, D., Huo, J., & Gang, H. (2023). Response of Abscission Zone of Blue Honeysuckle (Lonicera caerulea L.) Fruit to GA3 and 2,4-D Spray Application. Agronomy, 13, 2937. https://doi.org/10.3390/agronomy13122937. Search in Google Scholar

Ropelewska, E. (2022). Assessment of the Influence of Storage Conditions and Time on Red Currants (Ribes rubrum L.) Using Image Processing and Traditional Machine Learning. Agriculture, 12, 1730. https://doi.org/10.3390/agriculture12101730. Search in Google Scholar

Saeidirad, M. H., Rohani, A., & Zarifneshat, S. (2013). Predictions of viscoelastic behavior of pomegranate using artificial neural network and Maxwell model. Computers and Electronics in Agriculture, 98, 1-7. https://doi.org/10.1016/j.compag.2013.07.009 Search in Google Scholar

Satitmunnaithum, J., Kitazawa, H., Arofatullah, N.A., Widiastuti, A.,.Kharisma, A.D., Yamane, K., Tanabata, S., & Sato, T. (2022). Microbial population size and strawberry fruit firmness after drop shock-induced mechanical damage. Postharvest Biology and Technology, 192, p.112008. https://doi.org/10.1016/j.postharvbio.2022.112008 Search in Google Scholar

Senica, M., Stampar, F., & Mikulic-Petkovsek, M. (2018). Blue honeysuckle (Lonicera cearulea L. subs. edulis) berry; a rich source of some nutrients and their differences among four different cultivars. Scientia Horticulturae, 238, 215–221. https://doi.org/10.1016/j.scienta.2018.04.056 Search in Google Scholar

Son, N., Chen, C., Cheng, Y., Toscano, P., Chen, C., Chen, S., Tseng, K., Syu, C., Guo, H., & Zhang, Y. (2022). Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms. Ecological Informatics, 69, 101618. .https://doi.org/10.1016/j.ecoinf.2022.101618. Search in Google Scholar

Tappi, S., Ragni, L., Tylewicz, U., Romani, S. Ramazzina, I., & Rocculi, P. (2017). Browning response of fresh-cut apples of different cultivars to cold gas plasma treatment. Innovative Food Science & Emerging Technologies, 53, 56-62. https://doi.org/10.1016/j.ifset.2017.08.005. Search in Google Scholar

Yu M., Li S., Zhan Y., Huang Z., Lv J., Liu Y., Quan X., Xiong J., Qin D., & Huo J. (2023). Evaluation of the Harvest Dates for Three Major Cultivars of Blue Honeysuckle (Lonicera caerulea L.) in China. Plants, 12, 3758. https://doi.org/10.3390/plants12213758. Search in Google Scholar

Zapałowska, A., Matłok, N., Zardzewiały, M. Piechowiak, T., & Balawejder, M. (2021). Effect of Ozone Treatment on the Quality of Sea Buckthorn (Hippophae rhamnoides L.). Plants, 10, 847. https://doi.org/10.3390/plants10050847. Search in Google Scholar

Zhu, C., Zhang, L., Gao, Y., Qin, D., & Huo, J. (2022). Two novel blue honeysuckle (Lonicera caerulea L.) cultivars: Lanjingling and Wulan. HortScience, 57(9), 1145-1147. https://doi.org/10.21273/HORTSCI16674-22. Search in Google Scholar

Zhu, S., Liu, J., Yang, Q., Jin, Y., Zhao, S., Tan, Z., ... & Zhang, H. (2023). The impact of mechanical compression on the postharvest quality of ‘Shine Muscat’grapes during short-term storage. Agronomy, 13(11), 2836. Search in Google Scholar

Ziaratban, A., Azadbakht, M., Ghasemnezhad, A. (2016). Modeling of volume and surface area of apple from their geometric characteristics and artificial neural network. International Journal of Food Properties, 20(4), 762–768. https://doi.org/10.1080/10942912.2016.1180533 Search in Google Scholar