Determination of pomological, morphological, antioxidant activity, biochemical content and nutritional content values of kumquat (Citrus japonica) accessions using multivariate analysis methods

Yıl 2025, Cilt: 18 Sayı: 2, 549 - 569, 31.08.2025

Yazgan Tunç

https://doi.org/10.18185/erzifbed.1630129

Öz

This study aimed to determine the pomological, morphological, biochemical, antioxidant, and nutritional properties of 33 naturally grown kumquat (Citrus japonica) accessions using multivariate statistical methods. The analysis revealed significant variation among the accessions.
Tukey’s multiple comparison test (p<0.05) showed that fruit weight ranged from 1.85 (‘K2’) to 13.23 g (‘K1’), while fruit width varied between 10.43 (‘K2’) and 25.01 mm (‘K1’). Pearson’s correlation analysis demonstrated a strong positive correlation between fruit weight and fruit width (r = 0.89**), indicating that heavier fruits tend to be wider. Principal component analysis (PCA) explained 83.93% of the total variation, with PC1 accounting for 17.25%, primarily influenced by leaf area (0.36), petiole length (0.38), and fruit width (0.47). The biplot analysis showed that ‘K18’, ‘K21’, and ‘K24’ were located outside the 95% confidence ellipse, indicating their distinct phenotypic characteristics. Heat map analysis revealed that accessions ‘K32’, ‘K24’, and ‘K21’ were strongly associated with high fruit weight, leaf area, and maturity index, while ‘K8’, ‘K6’, and ‘K20’ were linked to high mineral content, particularly iron, manganese, and zinc. When all datasets were collectively analyzed, the accessions with the highest values were identified as ‘K13’, ‘K12’, ‘K15’, ‘K10’, and ‘K9’, making them promising candidates for further breeding and selection studies.
These findings highlight the significant genetic diversity among kumquat accessions and their potential for breeding programs. Future studies should focus on the genetic basis of these variations and their agronomic performance under different environmental conditions.

Anahtar Kelimeler

Breeding , Genetic diversity , Multivariate analysis , Citrus japonica , Hatay

Kumkat (Citrus japonica) aksesyonlarının pomolojik, morfolojik, antioksidan aktivite, biyokimyasal içerik ve besin içeriği değerlerinin çok değişkenli analiz yöntemleri kullanılarak belirlenmesi

Öz

Bu çalışma, doğal olarak yetişen 33 kumkat (Citrus japonica) aksesyonunun pomolojik, morfolojik, biyokimyasal, antioksidan ve besin içeriklerini çok değişkenli istatistiksel yöntemler kullanarak belirlemeyi amaçlamıştır. Analizler, aksesyonlar arasında önemli varyasyonlar olduğunu ortaya koymuştur.
Tukey’nin çoklu karşılaştırma testi (p<0.05), meyve ağırlığının 1.85 (‘K2’) ile 13.23 g (‘K1’) arasında değiştiğini, meyve genişliğinin ise 10.43 (‘K2’) ile 25.01 mm (‘K1’) arasında farklılık gösterdiğini ortaya koymuştur. Pearson korelasyon analizi, meyve ağırlığı ile meyve genişliği arasında güçlü bir pozitif korelasyon olduğunu göstermiştir (r = 0.89**), bu da daha ağır meyvelerin genellikle daha geniş olduğunu göstermektedir. Temel bileşen analizi (PCA), toplam varyasyonun %83.93’ünü açıklamış ve PC1, özellikle yaprak alanı (0.36), yaprak sapı uzunluğu (0.38) ve meyve genişliği (0.47) gibi değişkenlerden etkilenerek %17.25 oranında katkı sağlamıştır. Biplot analizi, ‘K18’, ‘K21’ ve ‘K24’ aksesyonlarının %95 güven aralığının dışında kaldığını ve belirgin fenotipik farklılıklara sahip olduğunu göstermiştir. Isı haritası analizi, ‘K32’, ‘K24’ ve ‘K21’ aksesyonlarının yüksek meyve ağırlığı, yaprak alanı ve olgunluk indeksi ile güçlü bir ilişkiye sahip olduğunu, ‘K8’, ‘K6’ ve ‘K20’ aksesyonlarının ise özellikle demir, manganez ve çinko içeriği açısından yüksek mineral içeriğine sahip olduğunu ortaya koymuştur. Tüm veri setleri birlikte değerlendirildiğinde, en yüksek değerlere sahip aksesyonlar sırasıyla ‘K13’, ‘K12’, ‘K15’, ‘K10’ ve ‘K9’ olarak belirlenmiş ve bu genotipler ileri ıslah ve seleksiyon çalışmalarında umut vadeden adaylar olarak öne çıkmıştır.
Bu bulgular, kumkat aksesyonları arasındaki genetik çeşitliliğin önemini vurgulamakta ve bu türün ıslah programlarında kullanılma potansiyelini ortaya koymaktadır. Gelecekteki çalışmalar, bu varyasyonların genetik temellerini araştırmalı ve farklı ekolojik koşullardaki tarımsal performanslarını değerlendirmelidir.

Anahtar Kelimeler

Islah , Genetik çeşitlilik , Çok değişkenli analiz , Citrus japonica , Hatay

Kaynakça

• [1] Aladekoyi, G., Omosulis, V., & Orungbemi, O. (2016). Evaluation of antimicrobial activity of oil extracted from three different citrus seeds (Citrus limon, Citrus aurantifolia and Citrus aurantium). Int. J. Sci. Res. Eng. Stud, 3(3), 16-20.
• [2] Palma, A., & D’Aquino, S. (2018). Kumquat—Fortunella japonica. In Exotic Fruits (pp. 271-278). Academic Press.
• [3] Li, X., Meenu, M., & Xu, B. (2023). Recent development in bioactive compounds and health benefits of kumquat fruits. Food Reviews International, 39(7), 4312-4332.
• [4] Pawełczyk, A., Żwawiak, J., & Zaprutko, L. (2023). Kumquat fruits as an important source of food ingredients and utility compounds. Food Reviews International, 39(2), 875-895.
• [5] Chang, Y. C., Chen, I. Z., Lin, L. H., & Chang, Y. S. (2014). Temperature effects on shoot growth and flowering of kumquat trees. Horticultural Science & Technology, 32(1), 1-9.
• [6] Liu, X., Liu, B., Jiang, D., Zhu, S., Shen, W., Yu, X., Xue, Y., Liu, M., Feng, J., & Zhao, X. (2019). The accumulation and composition of essential oil in kumquat peel. Scientia Horticulturae, 252, 121-129.
• [7] Ziogas, V., Ganos, C., Graikou, K., Cheilari, A., & Chinou, I. (2024). Chemical Analyses of Volatiles from Kumquat Species Grown in Greece—A Study of Antimicrobial Activity. Horticulturae, 10(2), 131.
• [8] ImageJ. (2025). https://imagej.net/ij/download.html. (Access date 25 Jan 2025)
• [9] Gül, E. N., Altuntaş, E., & Öcalan, O. N. (2021). Determination of physico-mechanical characteristics and bioactive properties of Nagami kumquat fruits. Turkish Journal of Agricultural and Natural Sciences, 8(4), 1064-1072.
• [10] Pérez, S. M. (2022). Profile Physical and Phenolic-Chemical of Kumquat Influenced by the Environment Analyzed in Fresh. Journal of Ecological Engineering, 23(2), 196-203.
• [11] Xu, H. X., & Chen, J. W. (2011). Commercial quality, major bioactive compound content and antioxidant capacity of 12 cultivars of loquat (Eriobotrya japonica Lindl.) fruits. Journal of the Science of Food and Agriculture, 91(6), 1057-1063.
• [12] Özgen, M., Reese, R. N., Tulio, A. Z., Scheerens, J. C., & Miller, A. R. (2006). Modified 2, 2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) method to measure antioxidant capacity of selected small fruits and comparison to ferric reducing antioxidant power (FRAP) and 2, 2 ‘-diphenyl-1-picrylhydrazyl (DPPH) methods. Journal of Agricultural and Food Chemistry, 54(4), 1151-1157.
• [13] Singleton, V. L., & Rossi, J. A. (1965). Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. American journal of Enology and Viticulture, 16(3), 144-158.
• [14] Zhishen, J., Mengcheng, T., & Jianming, W. (1999). The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals. Food chemistry, 64(4), 555-559.
• [15] AOAC. (2000). Official Methods of Analysis. Association of Official Analytical Chemists, Washington DC.
• [16] Kalra, Y. P. (1998). Handbook of Reference Methods for Plant Analysis. CRC Press.
• [17] Jones, J. B. (2001). Laboratory guide for conducting soil tests and plant analysis. CRC press.
• [18] Meyer GA, Keliher PN. 1992. An Overview of Analysis by Inductively Coupled Plasma- Atomic Emission Spectrometry. Inductively Coupled Plasma in Analytical Atomic Spectrometry, 2nd: 473-516.
• [19] Balaram, V., Rahaman, W., Roy, P. (2022). Recent advances in MC-ICP-MS applications in Earth andenvironmental sciences: Challenges and solutions. Geosystems and Geoenvironment, 1, 100019.
• [20] NIST. (2001). National Institute of Standards and Technology Standard Reference Materials Catalog. 1-11
• [21] JMP®. (2024) https://www.jmp.com/en_us/home.html. (Access date 25 Jan 2025)
• [22] Savaşlı, E., Önder, O., Karaduman, Y., Dayıoğlu, R., Özen, D., Özdemir, S., Akın, A., Tunca, Z.S., Demir, B., Aydın, N. (2019). The effect of soil and foliar ürea application at heading stage on grain yield and quality traits of bread wheat (Triticium aestivum L.). Turkish J Agric Sci Technol. 7, 1928-1936.
• [23] Ahmed, I. A. M., Özcan, M. M., AlJuhaimi, F., & Albakry, Z. (2024). The Monitoring of Accumulations of Elements in Apple, Pear, and Quince Fruit Parts. Biological Trace Element Research, 1-7.
• [24] OriginLab®. (2024). https://www.originlab.com/. (Accessed 21 Jan 2025)
• [25] Khaleghi, A., & Khadivi, A. (2024). Morphological characterizations of wild nitre-bush (Nitraria schoberi L.) specimens. Genetic Resources and Crop Evolution, 71(1), 413-426.
• [26] Khadivi-Khub, A., & Etemadi-Khah, A. (2015). Phenotypic diversity and relationships between morphological traits in selected almond (Prunus amygdalus) germplasm. Agroforestry Systems, 89, 205-216.
• [27] Mohammadi, S. A., & Prasanna, B. M. (2003). Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop science, 43(4), 1235-1248.
• [28] Chang, Y.C., & Lin, T.C. (2020). Temperature Effects on Fruit Development and Quality Performance of Nagami Kumquat (Fortunella margarita [Lour.] Swingle). The Horticulture Journal, 89 (4): 351-358.
• [29] Toplu, C., Uygur, V., & Yildiz, E. (2009). Leaf mineral composition of olive varieties and their relation to yield and adaptation ability. Journal of Plant Nutrition, 32(9), 1560-1573.
• [30] Tabachnick, B. G., Fidell, L. S., Ullman, J. B. (2013). Using multivariate statistics. 6, 497- 516. Boston, MA: pearson.
• [31] Jolliffe, I. T. (2002). Principal component analysis for special types of data (pp. 338-372). Springer New York.
• [32] Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459.
• [33] Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23(3), 187-200.
• [34] Gower, J. C., Lubbe, S. G., & Le Roux, N. J. (2011). Understanding biplots. John Wiley & Sons.
• [35] Mardia, K. V., Kent, J. T., & Taylor, C. C. (2024). Multivariate analysis (Vol. 88). John Wiley & Sons.
• [36] Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244.
• [37] Wilkinson, L., & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179-184.
• [38] Atasoy, A. (2017). Soil geography of the district of Hassa (Hatay). Journal of International Social Research, 10(48), 253.