Modeling some mineral nutrient requirements for micropropagated wild apricot shoot cultures

Plant Cell, Tissue and Organ Culture

Volumn 129, Issue 2, 2017, Pages 325-335

  Kovalchuk, Irina Y ^a   Mukhitdinova, Zinat ^a   Turdiyev, Timur ^a   Madiyeva, Gulnara ^a   Akin, Melekşen ^b,c   Eyduran, Ecevit ^c   Reed, Barbara M ^b,d  

^a INSTITUTE OF PLANT BIOLOGY AND BIOTECHNOLOGY   (Kazakhstan)

^b OREGON STATE UNIVERSITY   (United States)

^c IGDIR UNIVERSITY   (Turkey)

^d USDA ARS National Clonal Germplasm Repository   (United States)

Author keywords

CART data mining algorithm; In vitro culture; Mineral nutrition; Prunus armeniaca; Response surface methodology

Indexed keywords

CALCIUM; CALCIUM CHLORIDE; CHLORIDE MINERALS; DATA MINING; FORESTRY; MINERALS; NITRATES; NUTRIENTS; POTASSIUM; SURFACE PROPERTIES;

DATA MINING ALGORITHM; IN-VITRO; MINERAL NUTRITION; PRUNUS ARMENIACA; RESPONSE SURFACE METHODOLOGY;

QUALITY CONTROL;

MINERALS; NUTRIENTS; PRUNUS; SHOOTS;

EID: 85011620960     PISSN: 01676857     EISSN: 15735044     Source Type: Journal    
DOI: 10.1007/s11240-017-1180-0     Document Type: Article

References (26)

1. Akin M (2016) Statistical methods for tissue culture medium optimization and a multiplexed fingerprinting set for hazelnuts. Dissertation, Oregon State University, Corvallis
2. Akin M, Eyduran E, Reed BM (2016) Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell Tiss Organ Cult. doi: 10.1007/s11240-016-1110-6
3. Alanagh E, Garoosi GA, Haddad R, Maleki S, Landín M, Gallego P (2014) Design of tissue culture media for efficient Prunus rootstock micropropagation using artificial intelligence models. Plant Cell Tiss Organ Cult 117(3):349–359. doi:10.1007/s11240-014-0444-1
4. Ali M, Eyduran E, Tariq MM, Tirink C, Abbas F, Bajwa MA, Baloch MH, Nizamani AH, Waheed A, Awan MA, Shah SH, Ahmad Z, Jan S (2015) Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pak J Zool 47(6):1579–1585
5. Anderson MJ, Whitcomb PJ (2005) RSM simplified: optimizing processes using response surface methods for design of experiments. Productivity Press, New York
6. Design-Expert (2010) Stat-Ease, Inc., Minneapolis, MN
7. Driver JA, Kuniyuki AH (1984) In vitro propagation of Paradox walnut rootstock. HortScience 19:507–509
8. Gago J, Perez-Tornero O, Landin M, Burgos L, Gallego PP (2011) Improving knowledge of plant tissue culture and media formulationby neurofuzzy logic: a practical case of data mining using apricot databases. J Plant Physiol 168(15):1858–1865
9. Hand CR, Maki S, Reed BM (2014) Modeling optimal mineral nutrition for hazelnut (Corylus avellana) micropropagation. Plant Cell Tiss Organ Cult 119:411–425. doi:10.1007/s11240-014-0544-y
10. Kovalchuk I, Mukhitdinova Z, Turdiyev T, Reed B (2017) Optimization of WPM for a wild Kazakhstan Apricot, Prunus armeniaca. Acta Hortic (in press)
11. Lloyd G, McCown B (1980) Commercially feasible micropropagation of mountain laurel, Kalmia latifolia, by use of shoot-tip culture. Comb Proc Int Plant Prop Soc 30:421–427
12. Lopez-Lefebre LR, Rivero RM, Garcia PC, Sanchez E, Ruiz JM, Romero L (2001) Effect of calcium on mineral nutrient uptake and growth of tobacco. J Sci Food Agric 81:1334–1338
13. Marino G, Bertazza G, Magnanini E, Altan AD (1993) Comparative effects of sorbitol and sucrose as main carbon energy sources in micropropagation of apricot. Plant Cell Tiss Organ Cult 34(3):235–244. doi:10.1007/bf00029712
14. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497
15. Niedz RP, Evens TJ (2007) Regulating plant tissue growth by mineral nutrition. In Vitro Cell Dev Biol-Plant 43:370–381.
16. Niedz RP, Hyndman SE, Evens TJ (2007) Using a gestalt to measure the quality of in vitro responses. Sci Hortic 112(3):349–359
17. Nisbet R, Elder J, Miner G (2009) Handbook of statistical analysis and data mining applications. Academic Press, London
18. Pérez-Tornero O, Burgos L (2000) Different media requirements for micropropagation of apricot cultivars. Plant Cell Tiss Organ Cult 63:133–141
19. Pérez-Tornero O, Burgos L (2007) Apricot micropropagation. In: Jain SM, Häggman H (eds) Protocols for micropropagation of woody trees and fruits. Springer Netherlands, Dordrecht, pp 267–278
20. Poothong S, Reed BM (2014) Modeling the effects of mineral nutrition for improving growth and development of micropropagated red raspberries. Sci Hortic 165(0):132–141
21. Quoirin M, Lepoivre P (1977) Improved media for in vitro culture of Prunus sp. Acta Hortic 78:437–442
22. Rashidi S, Ranjitkar P, Hadas Y (2014) Modeling bus dwell time with decision tree-based methods. Transp Res Rec: J Trans Res Board 2418:74–83
23. Reed BM, Wada S, DeNoma J, Niedz RP (2013) Improving in vitro mineral nutrition for diverse pear germplasm. In Vitro Cell Dev Biol-Plant 49:343–355
24. SPSS (2013) Statistics for Windows, Version 22.0. IBM Corp, Armonk, NY
25. Wada S, Niedz RP, Reed BA (2015) Determining nitrate and ammonium requirements for optimal in vitro response of diverse pear species. In Vitro Cell Dev Biol-Plant 51:19–27
26. Yordanova A, Gocheva-Illieva S, Kulina H, Yordanova L, Marinov I (2015) Classification and regression tree analysis in modeling the milk yield and conformation traits for Holstein cows in Bulgaria. J Animal Sci Technol 7(2):208–213