Euclidean distance can identify the mannitol level that produces the most remarkable integral effect on sugarcane micropropagation in temporary immersion bioreactors
Plant scientists usually record several indicators in their abiotic factor experiments. The common statistical management involves univariate analyses. Such analyses generally create a split picture of the effects of experimental treatments since each indicator is addressed independently. The Euclidean distance combined with the information of the control treatment could have potential as an integrating indicator. The Euclidean distance has demonstrated its usefulness in many scientific fields but, as far as we know, it has not yet been employed for plant experimental analyses. To exemplify the use of the Euclidean distance in this field, we performed an experiment focused on the effects of mannitol on sugarcane micropropagation in temporary immersion bioreactors. Five mannitol concentrations were compared: 0, 50, 100, 150 and 200 mM. As dependent variables we recorded shoot multiplication rate, fresh weight, and levels of aldehydes, chlorophylls, carotenoids and phenolics. The statistical protocol which we then carried out integrated all dependent variables to easily identify the mannitol concentration that produced the most remarkable integral effect. Results provided by the Euclidean distance demonstrate a gradually increasing distance from the control in function of increasing mannitol concentrations. 200 mM mannitol caused the most significant alteration of sugarcane biochemistry and physiology under the experimental conditions described here. This treatment showed the longest statistically significant Euclidean distance to the control treatment (2.38). In contrast, 50 and 100 mM mannitol showed the lowest Euclidean distances (0.61 and 0.84, respectively) and thus poor integrated effects of mannitol. The analysis shown here indicates that the use of the Euclidean distance can contribute to establishing a more integrated evaluation of the contrasting mannitol treatments.
KeywordsAbiotic factors Biostatistics Drought In vitro culture Saccharum
This research was supported by the Institute of Botany (Leibniz University of Hannover, Germany), the Laboratory for Integrated Plant Physiology Research (University of Antwerp, Belgium), the Thünen Institute of Biodiversity (Braunschweig, Germany) and the Bioplant Centre (University of Ciego de Ávila, Cuba). Authors are grateful to Mrs. Bárbara Valle, Mrs. Julia Martínez, Mrs. Mariela Cid, Dr. Maritza Escalona and Dr. Martha Hernández for their important suggestions provided.
DG, LH, LY, GTSB, CCT, JP and JCL designed the biological and mathematical research; DG and LH conducted the experiment; DG, LH, LY, GTSB, CCT, JP and JCL wrote the paper; JCL had primary responsibility for the final content. All authors have read and approved the final manuscript.
Compliance with ethical standards
Conflict of interest
The authors do not have any conflict of interests.
Human and animal rights
This research did not involve experiments with human or animal participants.
Informed consent was obtained from all individual participants included in the study. Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.
- Avramova V, AbdElgawad H, Zhang Z, Fotschki B, Casadevall R, Vergauwen L, Knapen D, Taleisnik E, Guisez Y, Asard H, Beemster GTS (2015) Drought induces distinct growth response, protection and recovery mechanisms in the maize leaf growth zone. Plant Physiol 169:1382–1396CrossRefPubMedPubMedCentralGoogle Scholar
- Chao SK, Kim JE, Jong AP, Eom TJ, Kim WT (2006) Constitutive expression of abiotic stress-inducible hot pepper CaXTH3, which encodes a xyloglucan endotransglycosylase/hydrolase homolog, improves drought and salt tolerance in transgenic Arabidopsis plants. FEBS Lett 580:3136–3144CrossRefGoogle Scholar
- Duda R, Hart P, Stork D (2001) Pattern classification. Wiley, New YorkGoogle Scholar
- Granahan J, Sweet J (2001) An evaluation of atmospheric correction techniques using the spectral similarity scale. IEEE 5:2022–2024Google Scholar
- Gurmani AR, Bano A, Saleem M (2007) Effect of ABA and BA on growth and ion accumulation of wheat under salinity stress. Pak J Bot 39:141–149Google Scholar
- Gurr S, McPherson J, Bowles D (1992) Lignin and associated phenolic acids in cell walls. In: Wilkinson DL (ed) Molecular plant pathology. Oxford Press, Oxford, pp 51–56Google Scholar
- Hernández L, Loyola-González O, Valle B, Martínez J, Díaz-López L, Aragón C, Vicente O, Papenbrock J, Trethowan R, Yabor L, Lorenzo JC (2015) Identification of discriminant factors after exposure of maize and common bean plantlets to abiotic stresses. Not Bot Horti Agrobo Cluj-Nap 43:589–598Google Scholar
- Ichino M (1988) General metrics for mixed features-the cartesian space theory for pattern recognition. IEEE 1:494–497Google Scholar
- Jafar M, Zilouchian A (2001) Application of soft computing for desalination technology. In: Zilouchian A, Jamshidi M (eds) Intelligent control systems using soft computing methodologies. CRC Press, Boca Raton, pp 315–353Google Scholar
- Jiménez E, Pérez J, Gil V, Herrera J, García Y, Alonso E (1995) Sistema para la propagación de la caña de azúcar. In: Estrade M, Riego E, Limonta E, Tellez P, Fuente J (eds) Avances en Biotecnología Moderna. Elfos Scientiae, La Habana, Cuba, pp 11.2Google Scholar
- Kantardzic M (2003) Data mining: concepts, models, methods and algorithms. Wiley, New JerseyGoogle Scholar
- Kogan J (2007) Introduction to clustering large and high dimensional data. Cambridge University Press, New YorkGoogle Scholar