In vitro surface sterilization optimization in two high-potential Pistacia rootstock genotypes using statistical and machine learning approaches

İbrahim Halil Hatipoğlu; Burak Akyüz; Bekir Erol Ak

doi:10.24925/turjaf.v14i8.2238-2245.8872

Authors

İbrahim Halil Hatipoğlu Recep Tayyip Erdoğan University, Faculty of Agriculture, Department of Horticulture, 53340, Rize, Türkiye https://orcid.org/0000-0002-7236-4976
Burak Akyüz Ondokuz Mayıs University, Faculty of Agriculture, Department of Horticulture, 55270, Samsun, Türkiye https://orcid.org/0000-0001-7356-776X
Bekir Erol Ak Harran University, Faculty of Agriculture, Department of Horticulture, 63290, Şanlıurfa, Türkiye https://orcid.org/0000-0001-6938-942X

DOI:

https://doi.org/10.24925/turjaf.v14i8.2238-2245.8872

Keywords:

Pistacia spp , In vitro , Machine Learning , Micropropagation , Phytotoxicity

Abstract

Surface sterilization of woody species, such as Pistacia, is often inefficient in vitro. This is largely due to endogenous contamination and phenolic exudation, which frequently lead to lower culture success rates. The objective of this study was to compare the effectiveness of different chemical sterilization agents and concentrations on contamination control and early shoot development in two promising Pistacia rootstock genotypes [Genotype-I (Pistacia palaestina) and Genotype-II (Pistacia terebinthus]. A series of eight sterilization treatments was subjected to rigorous evaluation under controlled culture conditions. The treatments included hydrogen peroxide (15% and 30%), silver nitrate (0.1% and 0.5%), mercuric chloride (0.1% and 0.5%), and sodium hypochlorite (1% and 2%). The contamination ratio, survival ratio, shoot diameter, and shoot length were measured. Specifically, the interaction between genotype and concentration was found to be the most influential factor in determining shoot regeneration rates. Genotype I demonstrated lower contamination levels and higher survival and shoot growth than Genotype II. Among the tested sterilants, sodium hypochlorite at concentrations ranging from 1% to 2% showed the most balanced performance, reducing contamination while maintaining higher survival rates and superior shoot growth. In contrast, higher concentrations of mercuric chloride, despite relatively low contamination levels, inhibited the growth. Several machine learning models were utilized to examine relationships within the data. The results showed high predictive accuracy, particularly for survival rates and shoot diameter outcomes. The findings indicate that the efficiency of sterilization in Pistacia is strongly genotype-dependent and that optimized sodium hypochlorite treatments offer a practical and less phytotoxic alternative for the successful in vitro establishment of selected rootstock genotypes.

References

Açar, İ., Doran, İ., Aslan, N., & Kalkancı, N. (2016). Boron affects the yield and quality of nonirrigated pistachio (Pistacia vera L.) trees. Turkish Journal of Agriculture and Forestry, 40(5), 664–670. https://doi.org/10.3906/tar-1511-80

Akbari, M., Karbor, S., Afshar, A., Ferguson, L., Farajpour, M., Dillard, T., & Katam, R. (2024). The role of rootstock selection in pistachio cultivation. Horticulturae, 10(12), 1278. https://doi.org/10.3390/horticulturae10121278

Akyüz, A., & Cemek, B. (2025). Artificial intelligence models for predicting root traits of chokeberry under salt stress. Black Sea Journal of Agriculture, 8(5), 713–724. https://doi.org/10.47115/bsagriculture.1761077

Al-Saghir, M., & Porter, D. (2012). Taxonomic revision of the genus Pistacia L. (Anacardiaceae). American Journal of Plant Sciences, 3, 12–32. https://doi.org/10.4236/ajps.2012.31002

Azghandi, A., Habashi, A., Pur, A., Mojtahedi, N., Parizi, R., Rostami, A., Rafiei, A., & Shakib, A. (2008). Developing protocols for mass propagation of important pistachio rootstocks and commercial cultivars using tissue culture techniques. Agricultural Biotechnology Research Institute of Iran, FAO AGRIS - International System for Agricultural Science and Technology, 90p.

Bacchetta, L., Aramini, M., Bernardini, C., & Rugini, E. (2008). In vitro propagation of traditional Italian hazelnut cultivars as a tool for the valorization and conservation of local genetic resources. HortScience, 43, 562–566.

Benmahioul, B., 2017. Factors affecting in vitro micropropagation of Pistachio. (Pistacia vera L.). Agriculture and Forestry Journal, 1(1), 56-61.

Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758

Ekinci, H., & Ak, B. (2025). In vitro regeneration potential of Pistacia integerrima and Pistacia khinjuk rootstocks: A modeling approach for optimal medium formulation. Turkish Journal of Agriculture and Forestry, 49(3), 624–636. https://doi.org/10.55730/1300-011X.3291

Gammoudi, N., Nagaz, K., & Ferchichi, A. (2022) Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm. BMC Plant Biology, 22, 324. https://doi.org/10.1186/s12870-022-03674-x

Hameg, R., Arteta, T. A., Landin, M., Gallego, P. P., & Barreal, M. E. (2020). Modeling and optimizing culture medium mineral composition for in vitro propagation of Actinidia arguta. Frontiers in Plant Science, 11, 554905. https://doi.org/10.3389/fpls.2020.554905

Hesami, M., Naderi, R., & Tohidfar, M. (2019). Modeling and optimizing in vitro sterilization of Chrysanthemum via Multilayer Perceptron-Non-dominated Sorting Genetic Algorithm-II (MLP-NSGAII). Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.00282

Ibrahim, N., Adedamola, A. M., Ibrahim, B., Ahmed, R. T., Raji, I. D., & Bello-Salau, H. (2023). Survey of machine learning and optimization algorithms in plant tissue culture. Engineering Proceedings, 56(1), 122. https://doi.org/10.3390/ASEC2023-15259

Kaska, N. (1990). Pistachio research and development in the Near East, North Africa and Southern Europe. In Nut production industry in Europe, Near East and North Africa (Reur Technical Series No. 13, pp. 133–160). FAO.

Korkmaz, E., Yaşar, R., Soydan, B., Aasım, M., Sarpkaya, K., & Açar, İ. (2022). Optimization of in vitro sterilization for pistachio (Pistacia vera L.) rootstocks. Anatolian Journal of Botany, 6(1), 1-6. https://doi.org/10.30616/ajb.999973

Kovalchuk, I. Y., Mukhitdinova, Z., Turdiyev, T., Madiyeva, G., Akin, M., & others. (2018). Nitrogen ions and nitrogen ion proportions impact the growth of apricot (Prunus armeniaca) shoot cultures. Plant Cell, Tissue and Organ Culture, 133(2), 263–273. https://doi.org/10.1007/s11240-018-1379-8

Kumari, K., Gaur, R. K., Gupta, R., Yadav, D., & Albrectsen, B. R. (2026). Advancements in artificial intelligence and machine learning to optimize plant cell and tissue culture. In R. K. Gaur, R. Gupta, D. Yadav, & B. R. Albrectsen (Eds.), Plant molecular farming. Springer. https://doi.org/10.1007/978-981-95-3823-2_15

Leifert, C., Cassells, A. C. (2001). Microbial hazards in plant tissue and cell cultures. In Vitro Cellular & Developmental Biology – Plant, 37, 133–138. https://doi.org/10.1007/s11627-001-0025-y

Leifert, C., Morris, C. E., & Waites, W. M. (1994). Ecology of microbial saprophytes and pathogens in tissue culture and field-grown plants: Reasons for contamination problems in vitro. Critical Reviews in Plant Sciences, 13(2), 139–183. https://doi.org/10.1080/07352689409701912

Mahmoud, S., & Al-Ani, N. (2016). Effect of different sterilization methods on contamination and viability of nodal segments of Cestrum nocturnum L. International Journal of Research, 4, 4-9. https://doi.org/10.20431/2349- 0365.0401002

Murashige, T., & Skoog, F. (1962). A revised medium for rapid growth and bio assays with tobacco tissue cultures. Plant Physiology, 15, 473-497.

Pakyürek, M., Ak, B. E., & Kireç, A. (2022). Antepfıstığında önemli anaçlık türler ve çeşitler. In M. Pakyürek & B. E. Ak (Eds.), Antepfıstığı yetiştiriciliği (pp. 71–98). İksad Yayınevi.

Parfitt, D. E., & Badenes, M. L. (1997). Phylogeny of the genus Pistacia as determined from analysis of the chloroplast genome. Proceedings of the National Academy of Sciences, 94(15), 7987–7992. https://doi.org/10.1073/pnas.94.15.7987

Read, P. E., & Preece, J. E. (2014). Cloning: Plants – Micropropagation/tissue culture. In Encyclopedia of Agriculture and Food Systems (pp. 317–336). Academic Press.

Şekerli, M. (2024). Season, thermotherapy and surface sterilization play important roles in microbial contamination of hazelnut in vitro cultures. Plant Cell, Tissue and Organ Culture, 157, Article 70. https://doi.org/10.1007/s11240-024-02799-1

Talebi, M., Akbari, M., Zamani, M., & Sayed-Tabatabaei, B. E. (2016). Molecular polymorphism in Pistacia vera L. using non-coding regions of chloroplast DNA. Journal of Genetic Engineering and Biotechnology, 14, 31–37. https://doi.org/10.1016/j.jgeb.2016.02.001

Temunović, M., Šola, Z., Jakšić, V., Vidaković, A., Liber, Z., Poljak, I., & Bogdanović, S. (2024). Clarifying genetic and taxonomic relationships among Pistacia taxa (Anacardiaceae) in Croatia (eastern Adriatic). Acta Botanica Croatica, 83(1), 1–13. https://doi.org/10.37427/botcro-2024-009

Tambarussi, E. V., Rogalski, M., Nogueira, F. T. S., Brondani, G. E., De Martin, V. D. F., & Carrer, H. (2015). Influence of antibiotics on indirect organogenesis of teak. Annals of Forest Research, 58(1), 177–183. https://doi.org/10.15287/afr.2015.345

Tilkat, E., Onay, A., Yıldırım, H., & Ayaz, E. (2009). Direct plant regeneration from mature leaf explants of pistachio, Pistacia vera L. Scientia Horticulturae 121(3): 361-365.

Zohary, M. (1952). A monographical study of the genus Pistacia. Palestine Journal of Botany (Jerusalem Series), 5, 187–228.