Eclidean distance can recognize the best Stevia genotype and environment to produce rebaudioside and stevioside under controlled conditions


  • María de Lourdes Tapia y Figueroa Universidad Nacional Agraria La Molina, Lima, Perú.
  • Luz R. Gómez Pando Universidad Nacional Mayor de San Marcos, Lima, Perú.



Biostatistics, research methods, glucoside, phenotypic traits, sweet grass


Stevia rebaudiana is considered an important medicinal plant possessing low-calorie glucoside sweeteners. The present work describes the comparison of three stevia genotypes (IBT 1, IBT 2 and IBT 3) in two contrasting environments simulated under controlled conditions: Sullana in Peru; and Misiones in Paraguay (regarded as the center of origin of Stevia). In the study, we explored the Euclidean distance as an integrating indicator for simultaneous selection of several stevia traits. Plant scientists often record multiple morphological, physiological and biochemical indicators in their experiments. Common statistical data evaluations involve univariate analyses such as t-test, Mann-Whitney and Analysis of Variance followed by Tukey HSD. However, these analyses do not evaluate integrally the effects of the experimental treatments because each indicator is analyzed independently. Euclidean distance from each treatment combination to the ideal phenotype of the stevia plantlets was calculated. IBT 2 grown in Sullana environmental conditions showed the best integral results, while IBT 1 displayed the worst results. esponse parameters to different contrasting environments. The analysis shown here indicates that the use of the Euclidean distance could contribute to establishing a more integrated evaluation of the contrasting Stevia genotypes. On the other hand, the Euclidean distance, as a non-dimensional indicator, can help to compare different phenotype traits.


Download data is not yet available.


Angassa, D., & Mohammed, J. (2022). Agro-morphological Variability Study of Ethiopian Barley (Hordeum vulgare L.) Accessions for Their Important Agronomical Traits at Hadiya Zone, Southern Ethiopia. J Plant Sci., 10,19–25

Blasius, J., Eilers, P., & Gower, J. C. (2009). Better Biplots. Computational Statistics and Data Analysis, 53(8), 3145–3158

Brahmachari, G., Mandal, L. C., Roy, R., Mondal. S., & Brahmachari, A. K. (2011). Stevioside and related compunds - molecules of pharmaceutical promise: a critical overview. Arch Pharm, 344(1), 5–19

Büyük, G., Bayram, C. A., İnan, M., & Kırpık, M. (2022). The effect of organic and inorganic fertilizers on plant nutrient content and agronomic performance of stevia. Journal of Plant Nutrition, 45(15), 2303–2314.

Chapman, S., Shenk, P., Kazan, K., & Manners, J. (2001). Using Biplots to interpret gene expression pattern in plants. Bioinf Applic Note, 18(1), 202–204

Christaki, E., Bonos. E., Giannenas. I., Karatzia, M. A., & Florou-Paneri, P. (2013). Stevia rebaudiana as a novel source of food additives. Journal of Food & Nutrition Research, 52(4), 167–178.

Duda, R., Hart, P., & Stork, D. (2001). Pattern Classification. John Wiley & Sons Inc, New York.

Faria, J. C., Demétrio, C. G. B., & Allaman, I. B. (2008). BPCA: Biplot of multivariate data based on principal components analysis (R package; version 1.02) UESC and ESALQ, Ilheus, Bahia, Brasil and Piracicaba, Sao Paulo, Brasil.

Fliege, J., Qi, H-D., & Xiu, N. (2019). Euclidean distance matrix optimization for sensor network localization. In: C. Gao, G. Zhao & H. Fourati (eds.), Cooperative Localization and Navigation: Theory, Research and Practice. CRC Press, Boca Raton, FL

Gabriel, K. R. (2002). Goodness of fit of biplots and correspondence analysis. Biometrika, 89(2), 423–436

Giuffre, L., Romaniuk, R., & Ciarlo, E. (2013). Stevia, ka’a he’e, wild sweet herb from South America-An overview. Emirates Journal of Food and Agriculture, 25(10), 746–50,

Gomez-Pando, L., Jimenez-Davalos, J., Eguiluz-de la Barra, A., Aguilar-Castellanos, E., Falconí-Palomino, J., Ibañez-Tremolada, M., Varela, M., & Lorenzo, J.C. (2009). Field performance of new in vitro androgenesis-derived double haploids of barley. Euphytica, 166, 269–276.

Gómez, D., Hernández, L., Yabor, L., Beemster, G. T. S., Tebbe, C. C., Papenbrock, J., & Lorenzo, J. C. (2018). Euclidean distance can identify the mannitol level that produces the most remarkable integral effect on sugarcane micropropagation in temporary immersion bioreactors. Journal of Plant Research, 131, 719–724.

Granahan, J., & Sweet, J. (2001). An evaluation of atmospheric correction techniques using the spectral similarity scale. IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, 5, 2022–2024.

Gusmaini, Kartikawati, A., Nurhayati, H., Permadi, R., & Syakir, M. (2022). The utilization of plant growth-promoting bacteria to enhance stevia (Stevia rebaudiana) herb yield at low land. IOP Conference Series: Earth and Environmental Science, 974, 012028.

Haque, F., & Haque, S. (2018). Plant recognition system using leaf shape features and minimum Euclidean distance. Ictact Journal On Image And Video Processing, (9)2, 1919–1925

Hossain, M., Islam, M., Islam, M., & Akhtar, S. (2017). Cultivation and uses of stevia (Stevia rebaudiana Bertoni): A review. African J. Food Agric. Nut. Dev. 17(4), 12745–12757.

Ichino, M. (1988). General metrics for mixed features-the cartesian space theory for pattern recognition. Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 494–497.

Ijaz, M., Pirzada, A. M., Saqib, M., & Latif, M. (2015). Stevia rebaudiana: An alternative sugar crop in Pakistan–a review. Zeitschrift für Arznei- & Gewürzpflanzen, 20(2), 88–96

Jafar, M., & Zilouchian, A. (2001). Application of soft computing for desalination technology. In: A. Zilouchian & M. Jamshidi (eds.), Intelligent Control Systems Using Soft Computing Methodologies. (pp. 315–353). CRC Press, Boca Raton.

Kantardzic, M. (2003). Data mining: concepts, models, methods and algorithms. Wiley-IEEE Press.

Kogan, J. (2007). Introduction to Clustering Large and High Dimensional Data. Cambridge University Press, New York,

Krzanowski, W. J. (2004). Biplots for multifactorial analysis of distance. Biometric, 60(2), 517–524

Lorenzo, J. C., Varela, M., Hernández, M., Gutiérrez, A., Pérez, A., Loyola, O. (2013). Integrated criteria to identify the best treatment in plant biotechnology experiments. Acta Physiol Plant, 35, 3261–3264.

Lorenzo, J. C., Yabor, L., Medina, N., Quintana, N. & Wells V. (2015). Coefficient of variation can identify the most important effects of experimental treatments. Not Bot Horti Agrobo Cluj-Nap,43(1), 287–291.

Tavazoie, S., Hughes, J. Campbell, M., Cho, R., & Church, G. (1999). Systematic determination of genetic network architecture. Nat Genet, 22, 281–285.

Villalobos-Olivera, A., Hernández, L., Martínez, J., Quintana, N., Zevallos, B. E., Yabor, L., Martínez-Montero, M. E., González-Olmedo, J., Sershen, J. C. L. (2019) Euclidean distance can recognize the Biojas® concentration that produces the ideal physiological status of pineapple in vitro-plantlets. In Vitro Cell.Dev.Biol.-Plant, 56, 259–263 (2020).




How to Cite

Tapia y Figueroa, M. de L., & Gómez Pando, L. R. . (2022). Eclidean distance can recognize the best Stevia genotype and environment to produce rebaudioside and stevioside under controlled conditions. eruvian ournal of gronomy, 6(3), 222–228.