Volume 4, Issue 3, May 2018, Page: 52-59
The Multivariate Analysis of Foliar Fertilization by Using Random Forest for Improving Alliin Concentration of Garlic (Allium sativum l.)
Qiang Shi, Agricultural Research Institute, Xinjiang Agricultural Vocational Technical College, Changji, China
Xuxin Liu, Agricultural Research Institute, Xinjiang Agricultural Vocational Technical College, Changji, China
Tursunay Dilxat, Agricultural Research Institute, Xinjiang Agricultural Vocational Technical College, Changji, China
Jun Zhang, Agricultural Research Institute, Xinjiang Agricultural Vocational Technical College, Changji, China
Qing Hao, Institute of Horticulture Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China
Received: May 7, 2018;       Accepted: May 30, 2018;       Published: Jul. 26, 2018
DOI: 10.11648/j.ijaas.20180403.11      View  602      Downloads  62
Abstract
This study was for analyzing significance and optimum proportion of components in foliar fertilizer, which aimed on improving alliin concentration of garlic (Allium sativum l.) in field condition. The Study with designed experiments implemented with uniform experimental design method U30 (3013) (D=0.1926). Alliin concentration from garlic clove was measured by HPLC, the analysis of data set engaged by Random Forest for components significance test and optimal formula calculation. The results shown that the RF is an appropriate choice for this kind of study; the top 3 important components of foliar fertilizer were: Glutathione, Ethanethiol and Mg2+; the application of foliar fertilizer with optimal combination of components had significant effects on improving alliin concentration in garlic clove; there was synergistic interaction between components, combination of different elements can get much better effects. The maximal data of alliin concentration in garlic clove from experiment results obtained 3.5% (mg/g) of fresh weight far more than control 1.5%.
Keywords
Garlic, Alliin, Random Forest, HPLC, Multivariate Analysis
To cite this article
Qiang Shi, Xuxin Liu, Tursunay Dilxat, Jun Zhang, Qing Hao, The Multivariate Analysis of Foliar Fertilization by Using Random Forest for Improving Alliin Concentration of Garlic (Allium sativum l.), International Journal of Applied Agricultural Sciences. Vol. 4, No. 3, 2018, pp. 52-59. doi: 10.11648/j.ijaas.20180403.11
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
M. Ipek, A. Ipek, S. G. Almquist, P. W. Simon, Demonstration of linkage and development of the first low-density genetic map of garlic, based on AFLP markers, Theor Appl Genet 110 (2) (2005) 228-36.
[2]
M. JA, Garlic: its anticarcinogenic and antitumorigenic properties, Nutr Rev. (54) (1996) s82-6.
[3]
H. A. Collin, Garlic and cardiovascular disease. In Functional Foods, Cardiovascular Disease and Diabetes, Arnoldi, A., Ed.; Woodhead Publishing, Ltd.: Cambridge, U. K. (2004) 240-260.
[4]
V. G. M Dirsch, Alex Vollmar, Angelika, Ajoene, a compound of garlic, induces apoptosis in human promyeloleukemic cells, accompanied by generation of reactive oxygen species and activation of nuclear factor kappaB, 1998.
[5]
J. Pinto, S. Lapsia, A. Shah, H. Santiago, G. Kim, Antiproliferative Effects of Garlic-Derived and Other Allium Related Compounds, 2001.
[6]
G. Sigounas, J. Hooker, A. Anagnostou, M. Steiner, S-allylmercaptocysteine inhibits cell proliferation and reduces the viability of erythroleukemia, breast, and prostate cancer cell lines, 1997.
[7]
A. Hall, A. Troupin, B. Londono-Renteria, T. M. Colpitts, Garlic Organosulfur Compounds Reduce Inflammation and Oxidative Stress during Dengue Virus Infection, Viruses 9 (7) (2017).
[8]
S. M. Kawakishi, Y., Sulfur chemistry of onions and inhibitory factors of the arachidonic acid cascade, In Food Phyto chemicals for Cancer Prevention I, American Chemical Society, 1993, pp. 120-127.
[9]
H. J. T. A. T. A. B, Synthesis of the flavour precursor, alliin, in garlic tissue cultures, Phytochemistry 66 (2) (2005) 187-194.
[10]
M. S. Rahman, Allicin and Other Functional Active Components in Garlic: Health Benefits and Bioavailability, International Journal of Food Properties 10 (2) (2007) 245-268.
[11]
B. Dethier, M. Laloux, E. Hanon, K. Nott, S. Heuskin, J. P. Wathelet, Analysis of the diastereoisomers of alliin by HPLC, Talanta 101 (2012) 447-52.
[12]
S. A. Nasim, B. Dhir, R. Kapoor, S. Fatima, Mahmooduzzafar, A. Mujib, Alliin obtained from leaf extract of garlic grown underin situconditions possess higher therapeutic potency as analyzed in alloxan-induced diabetic rats, Pharmaceutical Biology 49 (4) (2011) 416-421.
[13]
E. B. S. H. E. Schnug, Influence of Nitrogen and Sulfur Fertilization on the Alliin Content of Onions and Garlic, Journal of Plant Nutrition 27 (10) (2004) 1827-1839.
[14]
S. H. ELKE BLOEM, AND EWALD SCHNUG, Influence of Fertilizer Practices on S-Containing Metabolites in Garlic (Allium sativum L.) under Field Conditions, J. Agric. Food Chem. 58 (2010) 10690–10696.
[15]
E. B. S. H. E. Schnug, Influence of nitrogen and sulfur fertilization on the alliin content of onions and garlic J. Plant Nutr 27 (2004) 1827-1839.
[16]
M. Mutsch-Eckner, Isolierung, Analytik und biologische Aktivität von Aminosäuren und Dipeptiden aus Allium sativum L, ETH: Zürich; Dissertation Nr (1991) 9462.
[17]
L. Breiman, Random forests, Mach. Learn 45 (1) (2001) 5-32.
[18]
L. Breiman, Bagging predictors, Mach. Learn 24 (2) (1996) 123-140.
[19]
L. H. Wu JS, Duan XY, Ding Y, Wu HT, Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid eature., Bioinformatics 25 (2009) 30-35.
[20]
P.-A. S. Dehzangi A, Dehzangi O Using Random Forest for Protein Fold Prediction Problem: An Empirical Study, Journal of Information Science and ngineering 26 (2010) 1941–1956.
[21]
W. L. Liu ZP, Wang Y, Zhang XS, Chen LN., Prediction of protein-RNA binding sites by a random forest method with combined features, Bioinformatics 26 (2010) 1616–1622.
[22]
C. K. Kandaswamy KK, Martinetz T, Moller S, Suganthan PN., AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties, Journal of Theoretical Biology 270 (2011) 56–62.
[23]
M. S. M. Kohbalan Moorthy, Random forest for gene selection and microarray data classification, Bioinformation 7 (3) (2011) 142-146.
[24]
T. Shi, D. Seligson, A. S. Belldegrun, A. Palotie, S. Horvath, Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma, Modern Pathology 18 (4) (2004) 547-557.
[25]
A. Anaissi, P. J. Kennedy, M. Goyal, D. R. Catchpoole, A balanced iterative random forest for gene selection from microarray data, BMC Bioinformatics 14 (2013) 261.
[26]
V. Botta, G. Louppe, P. Geurts, L. Wehenkel, Exploiting SNP correlations within random forest for genome-wide association studies, PLoS One 9 (4) (2014) e93379.
[27]
B. A. Tuulaikhuu, H. Guasch, E. Garcia-Berthou, Examining predictors of chemical toxicity in freshwater fish using the random forest technique, Environ Sci Pollut Res Int (2017).
[28]
V. N. Uversky, W.-Z. Lin, J.-A. Fang, X. Xiao, K.-C. Chou, iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model, PLoS ONE 6 (9) (2011) e24756.
[29]
J. Wu, H. Liu, X. Duan, Y. Ding, H. Wu, Y. Bai, X. Sun, Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature, Bioinformatics 25 (1) (2009) 30-35.
[30]
M. W. Andy Liaw Classification and Regression by randomForest, R News 2 (3) (2002) 18-22.
[31]
S. Kuhn, B. Egert, S. Neumann, C. Steinbeck, Building blocks for automated elucidation of metabolites: Machine learning methods for NMR prediction, BMC Bioinformatics 9 (1) (2008) 400.
[32]
J. Borlinghaus, F. Albrecht, M. C. Gruhlke, I. D. Nwachukwu, A. J. Slusarenko, Allicin: chemistry and biological properties, Molecules 19 (8) (2014) 12591-618.
[33]
Z. C. Chou KC, Review: Prediction of protein structural classes, Critical Reviews in Biochemistry and Molecular Biology 30 (1995) 275–349.
[34]
G. G. M. Freeman, N., Influence of sulphate nutrition on the flavour components of garlic (Allium sativum) and wild Onion (A. vineale). J. Sci. Fd Agric (22) (1971) 330-334.
[35]
B. Granroth, Biosynthesis and decomposition of cysteine derivatives in onion and other Allium species, Ann. Acad. Sci. Fenn. Chem. (154) (1970) 4–71.
[36]
B. Granroth, Biosynthesis and decomposition of cysteine derivatives in onion and other Allium species., Ann. Acad. Sci. Fenn. Chem. (154) (1970) 4-71.
[37]
J. P. Comstock, Hydraulic and chemical signalling in the control of stomatal conductance and transpiration, Journal of Experimental Botany 53 (367) (2002) 195-200.
[38]
R. Finkelstein, Abscisic Acid synthesis and response, Arabidopsis Book 11 (2013) e0166.
[39]
W. M. K. Randle, D. E.; Kopsell, D. A, Sequentially reducing sulfate fertility during onion growth and development affects bulb flavor at harvest., HortScience 37 (1) (2002) 118-121.
Browse journals by subject