IC4R009-Metabolomics-2013-25056584 - Revision history

Liman: /* Labs working on this Project */

2016-07-28T09:21:58Z

‎Labs working on this Project

Liman: /* Labs working on this Project */

2016-07-28T09:21:39Z

‎Labs working on this Project

Liman: /* Research Findings */

2016-07-28T09:21:21Z

‎Research Findings

Liman: /* Research Findings */

2016-07-28T09:20:52Z

‎Research Findings

Liman: /* Research Findings */

2016-07-28T09:19:56Z

‎Research Findings

Liman: /* Labs working on this Project */

2016-07-28T09:19:10Z

‎Labs working on this Project

Liman at 09:18, 28 July 2016

2016-07-28T09:18:07Z

Liman: /* Research Findings */

2016-07-28T09:17:10Z

‎Research Findings

Liman: /* Research Findings */

2016-07-28T09:16:31Z

‎Research Findings

Liman: /* Research Findings */

2016-07-28T09:12:56Z

‎Research Findings

← Older revision		Revision as of 09:21, 28 July 2016
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	[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]		[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
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← Older revision		Revision as of 09:21, 28 July 2016
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	[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]		[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
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	==Labs working on this Project==		==Labs working on this Project==

← Older revision		Revision as of 09:21, 28 July 2016
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	[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]		[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
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← Older revision		Revision as of 09:20, 28 July 2016
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	[[File:IC4R009-Metabolomics-2013-25056584-f4.png \|left \|thumb \|300px \|'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]		[[File:IC4R009-Metabolomics-2013-25056584-f4.png \|left \|thumb \|300px \|'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]

−	[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|~~right~~ \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]	+	[[File:IC4R009-Metabolomics-2013-25056584-f5.png \|center \|thumb \|300px \|'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]

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 * All 45 primary metabolites of rice grain samples were divided into 14 training set samples and four test set samples (Fig. 4). The predictive model had a R2Y of 0.912 and RMSEE of 0.4994. The predictive model created in this study had a Q2 of 0.806 and RMSEP of 0.5267. The contribution of variables in the projection could be explained using variables important in the projection (VIP). Variables with VIP values greater than 1 are the most influential to the model (Jumtee et al., 2009). Primary metabolites that have a strong positive correlation with phenolic acid, as shown in the correlation matrix, were important to create a prediction model for rice grain quality (Fig. 5). Phenylalanine, a major amino acid donor for the synthesis of phenolic acid, was found to be most significant in creating a phenolics prediction model for rice grain.
-[[File:IC4R009-Metabolomics-2013-25056584-f4.png |left |thumb |200px |'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f4.png |left |thumb |300px |'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]
-[[File:IC4R009-Metabolomics-2013-25056584-f5.png |right |thumb |200px |'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f5.png |right |thumb |300px |'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
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 * All 45 primary metabolites of rice grain samples were divided into 14 training set samples and four test set samples (Fig. 4). The predictive model had a R2Y of 0.912 and RMSEE of 0.4994. The predictive model created in this study had a Q2 of 0.806 and RMSEP of 0.5267. The contribution of variables in the projection could be explained using variables important in the projection (VIP). Variables with VIP values greater than 1 are the most influential to the model (Jumtee et al., 2009). Primary metabolites that have a strong positive correlation with phenolic acid, as shown in the correlation matrix, were important to create a prediction model for rice grain quality (Fig. 5). Phenylalanine, a major amino acid donor for the synthesis of phenolic acid, was found to be most significant in creating a phenolics prediction model for rice grain.
-[[File:IC4R009-Metabolomics-2013-25056584-f4.png |left |thumb |600px |'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f4.png |left |thumb |400px |'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]
-[[File:IC4R009-Metabolomics-2013-25056584-f5.png |right |thumb |600px |'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f5.png |right |thumb |400px |'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
 ==Labs working on this Project==

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 * In total, 52 metabolites including 19 amino acids, 13 organic acids, nine sugars, seven phenolic acids, three sugar alcohols, and one amine were detected in black rice (Fig. 1).
-[[File:IC4R009-Metabolomics-2013-25056584-f1.png |center |thumb |800px |'''Fig. 1. Selected ion chromatogram of metabolites extracted from black rice (cv. Josengheugchalbyeo; BR-3) as MO/TMS derivatives separated on a 30 m × 0.25 mm I.D. fused-silica capillary column coated with 0.25-mm CP-SIL 8 CB low bleed. Peak identification: 1, pyruvic acid; 2, lactic acid; 3, valine; 4, alanine; 5, oxalic acid; 6, glycolic acid; 30 , valine; 7, serine; 8, ethanolamine; 9, glycerol; 10, leucine; 11, isoleucine; 12, proline; 13, nicotinic acid; 14, glycine; 15, succinic acid; 16, glyceric acid; 17, fumaric acid; 70, serine; 18, threonine; 19, b-alanine; 20, malic acid; 21, salicylic acid; 22, aspartic acid; 23, methionine; 24, pyroglutamic acid; 25, 4-aminobutyric acid; 26, threonic acid; 27, arginine; 28, glutamic acid; 29, phenylalanine; 30, p hydroxybenzoic acid; 31, xylose; 32, asparagine; 33, vanillic acid; 34, glutamine; 35, shikimic acid; 36, citric acid; 37, quinic acid; 38, fructose; 380 , fructose; 39, galactose; 40, glucose; 41, syringic acid; 42, mannose; 43, mannitol; 44, ferulic acid; 45, p-coumaric acid; 46, inositol; 440 , ferulic acid; 47, tryptophan; 48, sinapic acid; 49, sucrose; 50, cellobiose; 51, trehalose; 52, raffinose; IS, internal standard (ribitol).''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f1.png |center |thumb |600px |'''Fig. 1. Selected ion chromatogram of metabolites extracted from black rice (cv. Josengheugchalbyeo; BR-3) as MO/TMS derivatives separated on a 30 m × 0.25 mm I.D. fused-silica capillary column coated with 0.25-mm CP-SIL 8 CB low bleed. Peak identification: 1, pyruvic acid; 2, lactic acid; 3, valine; 4, alanine; 5, oxalic acid; 6, glycolic acid; 30 , valine; 7, serine; 8, ethanolamine; 9, glycerol; 10, leucine; 11, isoleucine; 12, proline; 13, nicotinic acid; 14, glycine; 15, succinic acid; 16, glyceric acid; 17, fumaric acid; 70, serine; 18, threonine; 19, b-alanine; 20, malic acid; 21, salicylic acid; 22, aspartic acid; 23, methionine; 24, pyroglutamic acid; 25, 4-aminobutyric acid; 26, threonic acid; 27, arginine; 28, glutamic acid; 29, phenylalanine; 30, p hydroxybenzoic acid; 31, xylose; 32, asparagine; 33, vanillic acid; 34, glutamine; 35, shikimic acid; 36, citric acid; 37, quinic acid; 38, fructose; 380 , fructose; 39, galactose; 40, glucose; 41, syringic acid; 42, mannose; 43, mannitol; 44, ferulic acid; 45, p-coumaric acid; 46, inositol; 440 , ferulic acid; 47, tryptophan; 48, sinapic acid; 49, sucrose; 50, cellobiose; 51, trehalose; 52, raffinose; IS, internal standard (ribitol).''']]
 * The quantification data of 52 metabolites normalized based on IS signal intensity were subjected to PCA to identify differences in metabolite profiles among cultivars (Fig. 2). PCA revealed that the two highest-ranking principal components accounted for 56.8% of the total variance within the data set. The PCA results clearly showed the absence of significant variances among the same cultivar. The first principal component, accounting for 36.6% of the total variance, resolved the metabolite profiles of BR-4 and WR-1 cultivars.
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 * All 45 primary metabolites of rice grain samples were divided into 14 training set samples and four test set samples (Fig. 4). The predictive model had a R2Y of 0.912 and RMSEE of 0.4994. The predictive model created in this study had a Q2 of 0.806 and RMSEP of 0.5267. The contribution of variables in the projection could be explained using variables important in the projection (VIP). Variables with VIP values greater than 1 are the most influential to the model (Jumtee et al., 2009). Primary metabolites that have a strong positive correlation with phenolic acid, as shown in the correlation matrix, were important to create a prediction model for rice grain quality (Fig. 5). Phenylalanine, a major amino acid donor for the synthesis of phenolic acid, was found to be most significant in creating a phenolics prediction model for rice grain.
-[[File:IC4R009-Metabolomics-2013-25056584-f4.png |left |thumb |800px |'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f4.png |left |thumb |600px |'''Fig. 4. The PLS predictive model constructed from 18 rice samples as training sets (filled triangle) (A). A predicted result after four samples of test sets (open triangle) were projected to the model (B).''']]
-[[File:IC4R009-Metabolomics-2013-25056584-f5.png |right |thumb |800px |'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
+[[File:IC4R009-Metabolomics-2013-25056584-f5.png |right |thumb |600px |'''Fig. 5. The influence of variables used to create a total phenolics predictor for rice grain.''']]
 ==Labs working on this Project==