Investigating the effect of ozonation on mechanical parameters of Lonicera caerulea L. fruits using machine learning

• Autorzy: Basara Oskar , Pentoś Katarzyna , Gorzelany Józef

Identyfikatory

DOI

10.2478/agriceng-2025-0007

Warianty tytułu

PL

Badanie wpływu ozonowania na parametry mechaniczne owoców Lonicera caerulea L. za pomocą uczenia maszynowego

• Języki publikacji: EN

Abstrakty

EN

Lonicera caerulea L. - well - known in Poland as Kamchatka berry, has been gaining increasing popularity in recent years. The tests carried out on newly established Japanese haskap clones aimed at demonstrating the suitability of the fruits for mechanical harvesting and storage. This study focused on evaluation of mechanical properties and assessment of three distinct machine learning techniques to create predictive models that elucidate the connection between key mechanical attributes of the fruit and storage conditions of L. caerulea. The average force needed to puncture the fruits skin and flesh of L. caerulea var. emphyllocalyx varieties is 16.91% higher than that required for the tested L. caerulea var. kamtschatica varieties. L. caerulea var. emphyllocalyx fruits exhibited a significantly higher respiration rate, with C2H4 and CO2 levels during storage being 25.5% and 10.5% higher, respectively, compared to L. caerulea var. kamtschatica varieties. The machine learning algorithms tested yielded accurate models for deformation and energy prediction. The mean absolute percentage error (MAPE) of these models was determined to be between 14.87 and 20.65%. Models with significantly lower accuracy were obtained for force prediction, with the MAPE reaching 28.95% for L. var. kamtschatica fruit and 42.33% for L. emphyllocalyx fruit. The cultivation and improvement of Lonicera caerulea L. varieties is of great importance for the advancement of mechanized harvesting methods and the development of improved storage technologies for this species. The creation of machine learning methods will facilitate the development of predictive models that can serve as a predictive tool for the relationship between selected mechanical properties.

PL

Lonicera caerulea L. - dobrze znana w Polsce pod nazwą jagody kamczackiej w ostatnich latach stała się popularna. Badania przeprowadzone na niedawno wyhodowanych japońskich klonach haskap miały wykazać, że owoce nadają się do mechanicznego zbioru i przechowywania. Niniejsza praca ma na celu ocenę właściwości mechanicznych i trzech odrębnych metod uczenia maszynowego, aby stworzyć modele predykcyjne, które wyjaśniają związek między kluczowymi atrybutami mechanicznymi owoców i warunkami przechowywania L. caerulea. Średnia siła potrzebna do przekłucia skóry i miąższu owoca L. caerulea var. emphyllocalyx wynosi o 16,91% więcej niż ta wymagana dla badanych odmian L. caerulea var. kamtschatica. Owoce L. caerulea odmiany emphyllocalyx wykazały istotnie wyższy poziom tempa oddychania, przy poziomach C2H4 i CO2 podczas przechowywania na poziomie 25,5% i 10,5% odpowiednio wyższym, w porównaniu do odmian L. caerulea var. kamtschatica. Algorytm uczenia maszynowego zbadał wygenerowane dokładne modele do przewidywania deformacji i energii. Średni bezwzględny błąd procentowy (MAPE) badanych modeli wynosił pomiędzy 14,87 a 20,65%. Modele o dużo niższej dokładności uzyskano do przewidywania siły przy średnim bezwzględnym błędzie procentowym wynoszącym 28,95% dla owoców L. odmiany kamtschatica oraz 42,33% dla owoców L. emphyllocalyx. Uprawa i ulepszenie odmian Lonicera caerulea L. ma wielkie znaczenie dla rozwoju zmechanizowanych metod uprawy i rozwoju ulepszonych technologii przechowywania tego gatunku. Stworzenie metod uczenia maszynowego poprawi rozwój modeli predykcyjnych, które mogą służyć jako narzędzie predykcyjne dla zależności wybranych cech mechanicznych.

Słowa kluczowe

EN

Lonicera caerulea L.; morphological properties; mechanical properties; gas exchange; machine learning

PL

Lonicera caerulea L.; cechy morfologiczne; wymiana gazowa; uczenie maszynowe

• Wydawca: Polskie Towarzystwo Inżynierii Rolniczej
• Czasopismo: Agricultural Engineering
• Rocznik: 2025
• Tom: Vol. 29, No. 1
• Strony: 97--114
• Opis fizyczny: Bibliogr. 50 poz., rys., tab.

Twórcy

autor

Basara Oskar

• Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów, Poland

• https://orcid.org/0000-0001-7672-5209

autor

Pentoś Katarzyna

• Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, 37b Chelmonskiego Street, 51-630 Wroclaw, Poland

• https://orcid.org/0000-0002-0666-1948

autor

Gorzelany Józef

• Department of Food and Agriculture Production Engineering, University of Rzeszow, 4 Zelwerowicza Street, 35-601 Rzeszów

• https://orcid.org/0000-0002-1028-097X

Bibliografia

• 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.
• 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.
• 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.
• Becker, R., & Szakiel, A. (2019). Phytochemical characteristics and potential therapeutic properties of blue honeysuckle Lonicera caerulea L.(Caprifoliaceae). Journal of Herbal Medicine, 16, 100237.
• 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.
• 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.
• 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/ plants 10040624.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 ClujNapoca, 40, 155-162. https://doi.org/10.15835/nbha4017314.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.