Relationship between antioxidant properties and chemical composition of some Thai plants

ARTICLE IN PRESS JOURNAL OF FOOD COMPOSITION AND ANALYSIS Journal of Food Composition and Analysis 21 (2008) 229–240 www.elsevier.com/locate/jfca

Original Article

Relationship between antioxidant properties and chemical composition of some Thai plants Pitchaon Maisuthisakula,, Sirikarn Pasukb, Pitiporn Ritthiruangdejc a

Department of Food Industrial System, School of Science, University of the Thai Chamber of Commerce, 126/1 Vibhavadee-rangsit Road, Bangkok 10400, Thailand b Faculty of Science and Technology, Valaya Alongkorn Rajabhat University under Royal Patronage, Prathumthani 12150, Thailand c Faculty of Science, Mahidol University, Karnjanaburi 71150, Thailand Received 17 November 2006; received in revised form 3 October 2007; accepted 4 November 2007

Abstract Plants, which are rich in phenolic components, are of interest as sources of natural antioxidants. However, the biosynthesis of secondary metabolites is not well understood, and many plant components can act as antioxidants, so whole extracts from a wide range of plants need to be examined. To gain further knowledge in this area, the antioxidant activity and composition of organic solvent extracts from 28 Thai plants were investigated. A wide range of analytical parameters were studied including yield, phenolic compounds, flavonoids, moisture, ash, protein, fat, carbohydrate, dietary fiber, calcium, iron and vitamin C, and the data were analyzed by partial least square (PLS) regression analysis and principal component analysis (PCA) to allow correlation of the parameters and classification of the plants. The carbohydrate content was strongly associated with yield of extract. Antioxidant activity correlated well with phenolic and flavonoid contents, whereas the total phenolic compounds correlated weakly with other components except flavonoid content. In addition, fat and energy were useful parameters to distinguish seeds from other plant parts. These results provide useful data about the studied relationships and chemical patterns in plant tissues. r 2007 Elsevier Inc. All rights reserved. Keywords: Antioxidant; Antioxidant capacity; Phenolic compounds; Chemical composition; Partial least square (PLS) regression; Principal component analysis (PCA); Thai plants; Leaf; Fruit; Flower; Seed

1. Introduction Plants are a major source of phenolic compounds, which are synthesized as secondary metabolites during normal development in response to stress conditions, such as wounding and UV radiation among others (Stahl and Sies, 2003; Close et al., 2005). Plants may contain simple phenolics, phenolic acids, coumarins, flavonoids, stilbenes, hydrolysable and condensed tannins, lignins and lignans (Naczk and Shahidi, 2006). Distribution of phenolics in plants at the tissue, cellular and subcellular levels is not uniform. Insoluble phenolics are found in cell walls, while soluble phenolics are present within the plant cell vacuoles (Randhir and Shetty, 2005; Bengoechea et al., 1997). Cell Corresponding author. Tel./fax: +66 2 6976525.

E-mail address: [email protected] (P. Maisuthisakul). 0889-1575/$ - see front matter r 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jfca.2007.11.005

wall phenolics may be linked to various cell components such as sugars (Chaoui and El Ferjani, 2005). Therefore, the nature of polyphenol compounds in plants is complex. The biosynthesis of phenolic compounds and related substances is derived from some proteins, including tyrosine and tryptophan, in the shikimic acid pathway. In addition, the phenolics usually occur in bound form such as flavonoid glycosides and phenolic acid derivatives, which are synthesized from sugars. An interesting aspect is the complex relation between chemical components and phenolic compounds in plants. The phenolic compounds and their antioxidant properties present in no fewer than 3000 plant species including some Thai plants have been studied (Aruoma et al., 1996; Amarowicz et al., 2004; Maisuthisakul et al., 2007). Thai plants are widely distributed throughout the tropics particularly in Southeast Asia. Several researchers have

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shown that various tissues from plants grown in a tropical and subtropical climate contain high concentrations of natural phenolic phytochemicals, including flavonoids (Demo et al., 1998; Ka¨hkonen et al., 1999; Leong and Shui, 2002). So far, relatively few Thai plant sources have been studied as sources of phenolic compounds which may be useful because of their antioxidant potential (Chanwitheesuk et al., 2005). These studies have shown that some Thai plants have potent antioxidant properties. In addition, some Thai plants are reported to possess antimutagenic properties (108 species) (Trakoontivakorn et al., 2001; Nakahara et al., 2002) and activity related to inflammation (four Thai plants) (Laupattarakasem et al., 2003), which were related to their antioxidant properties. However, there are very few reports in the literature on the chemical composition of Thai plants. Many literature reports showed a simple relationship between the content of phenolic compounds and the antioxidant capacity of plant extracts, although the relationship between the phenolic components and their contribution to the antioxidant activity was not clear (Bocco et al., 1998; Sellappan et al., 2002; Goffman and Bergman, 2004). Other chemical substances in plants including protein, carbohydrate, vitamin and fiber also contribute to the antioxidant capacity (Amarowicz and Shahidi, 1997; Mukhopadhyay et al., 2000; Jing and Kitts, 2002; Betancur-Ancona et al., 2004). Hence, the relationship between chemical substances including phenolic compounds in plants and antioxidant properties may be complex. However, there is very little data to elucidate the relationship between chemical composition and antioxidant capacity. This study was therefore carried out to evaluate the antioxidant activity, phenolic contents and chemical composition of some Thai plants and to find a possible relationship between antioxidant activity and chemical composition of these plants by using linear regression analysis (partial least square, PLS). In addition, this work aimed at characterizing 28 plant samples on the basis of their analytical characteristics. Principal component analysis (PCA) was applied to the data obtained in order to separate the plants into homogenous groups. These data should be useful for screening plants as potential sources of natural antioxidants.

chemicals and solvents used in this experiment were analytical grade purchased from Sigma-Aldrich Chemical Co. (Steinheim, Germany). 2.2. Preparation of plant extracts Three batches of 28 plant materials, classified into four groups as plant leaf, flower, fruit and seed (Table 1), were purchased from market places in Thailand from March to June in 2004–2005. Each plant sample (50 g) was ground with 95% ethanol (150 mL) in a blender for 1 min and shaken in the dark at 25 1C for 4.5 h. The extract was evaporated to dryness using a rotary evaporator at 50 1C and stored under nitrogen at 20 1C until it was analyzed. Extraction was done in duplicate. 2.3. Determination of plant extract yield The yield of evaporated extracts was calculated on a dry weight basis. 2.4. Determination of antioxidant activity The free radical scavenging activity of plant extracts was evaluated using the stable radical DPPH as described by Masuda et al. (1999) with modifications (Maisuthisakul et al., 2007). DPPH radical in methanol (5 mM) was prepared and this solution (100 mL) was added to extract or antioxidant sample solutions in methanol (4.9 mL) at different concentrations. After 30 min, absorbance was measured at 517 nm. The percentage of DPPH radical scavenging activity of each plant extract was calculated from [A0(A1As)]/A0  100. A0 is the absorbance of the control solution (containing only DPPH); A1 is the absorbance of the DPPH solution containing plant extract; and As is the absorbance of the sample extract solution without DPPH. The DPPH radical scavenging activity (%) was plotted against the plant extract concentration (mg/mL) to determine the concentration of extract necessary to decrease DPPH radical scavenging by 50% (EC50). These values were changed to antiradical activity (AAR) defined as 1/EC50, since this parameter increases with antioxidant activity.

2. Materials and methods

2.5. Determination of chemical compositions

2.1. Chemicals

The total phenolic content of ethanolic extracts was determined using Folin–Ciocalteu’s phenol reagent (modified from Ka¨hkonen et al., 1999). Briefly, each extract (200 mL) was mixed with 1 mL of Folin–Ciocalteu’s phenol reagent thoroughly. After mixing for 3 min, 0.8 mL of 7.5% (w/v) sodium carbonate was added. The mixtures were agitated and allowed to stand for a further 30 min in the dark, and centrifuged at 3300g for 5 min. The absorbance of plant extracts and a prepared blank were measured at 765 nm using a spectrophotometer (UV–Vis model 1601, Shimadzu, Japan). The concentration of total phenolic

Folin Ciocalteu’s phenol reagent, 2,2-diphenyl-1-picrylhydrazyl radical (DPPH), sodium carbonate, hexamethyltetramine, aluminum chloride, rutin, stock solutions of iron and calcium of AA grade and retinol were purchased from Sigma Chemical Co., Ltd (St. Louis, MO). Gallic acid was purchased from Acros Organics (Morris plains, NJ). The 2,6-dichlorophenolindophenol disodium salt (DCPI) was obtained from Merck AG (Darmstadt, Germany), and a 12 mg L1 DCPI solution was prepared. The other

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Table 1 Moisture content and yield of ethanolic extracts obtained from various plant parts of some Thai plants with scientific name and common name Scientific name Herb and vegetable leaves Basella alba Linn. Careya sphaerica Roxb. Centella asiatica Linn. Cratoxylum formosum Dyer. Erythrina crista Galli. Lasia spinosa (Linn.) Thw. Leucaena glauca Benth Limnocharis flava Buch. Ocimum basilicum Linn. Ocimum sanctum Linn. Sauropus androgynus Linn. Spondias pinnata Kurz. Syzygium gratum (Wight) S.N.Mitra var. gratum Herbs and vegetable flowers Allium ascalonicum Linn. Azadirachta indeca A. Juss Var. siamensis valeton Cassia siamea Britt. Musa spiantum Linn. Sesbania grandiflora Desv. Berries and fruits Capsicum frutescus Linn. Eugenia siamensis Craib. Euginia malaccenses Linn. Momordica charantia Linn. Phyllanthus emblica Spondias pinnata Kurz. Berry and fruit seeds Nephelium lappaceum Linn. Parkia speciosa Hassk. Piper nigrum Linn. Tamarindus indica Linn a

Common name

Plant part

Moisture content (%)

Yield (%, db)a

Ceylon spinash Tummy wood

Leaf Young Leaf Young Leaf Leaf Young Leaf Young Young Young Young Young

93.5 75.3 86.6 85.5 81.7 94.8 79.9 94.7 89.8 87.6 89.9 76.4 79.6

0.7 2.3 1.6 3.1 1.2 1.4 2.5 1.9 1.6 1.1 2.2 1.2 1.2

Banana Cork wood

Flower Flower Flower Flower Flower

94.7 77.2 74.8 92.8 91.1

2.5 1.1 0.8 2.3 2.0

Chilli pepper Jambolan plum Malay apple Balsam pear Indian gooseberry Hog plum

Fruit Fruit Fruit Fruit Fruit Fruit

85.4 85.1 93.7 75.9 82.6 77.3

2.2 4.5 3.6 1.3 4.0 3.9

Rambutan

Seed Seed Seed Seed

36.3 70.7 72.5 49.5

3.7 2.5 1.6 2.5

Coral tree Lead tree

Holy basil Hog plum

Onion

Pepper Tamarind

leaf and leaf leaf and leaf

leaf and leaf leaf leaf leaf leaf leaf

and and and and and

leaf leaf leaf leaf leaf

Dry weight basis of the original sample of plant parts.

compounds in all plant extracts was expressed as mg of gallic acid equivalent per g dry weight of plant using the linear equation. The total flavonoid content of plant extracts was evaluated by a colorimetric assay according to the method of Bonvehı´ et al. (2001). One milliliter of 0.5% (w/v) hexamethyl tetramine, 20 mL of acetone, and 2 mL of 0.1 M HCl were added to each ground thawed-frozen plant sample (5 g) and boiled under reflux for 30 min. The supernatant was filtered and the residue was further washed with 20 mL of acetone. The filtrate volume was finally adjusted to 100 mL with acetone. An amount of 10 mL of filtrate was transferred into a separating funnel, along with 20 mL of H2O and then the aqueous phase was extracted with 25 mL of ethyl acetate three times. The extraction was repeated twice using another 50 mL of H2O each time. The total amount of extract in the ethyl acetate layer collected from the separating funnel was subsequently made up to 100 mL with ethyl acetate. To determine the total flavonoid content, 10 mL of extract in ethyl acetate was pipetted into a test tube and mixed with 1 mL of 2% (w/w) AlCl3 in methanol solution containing 5% acetic

acid using a vortex mixer. The absorbance was read immediately at 425 nm using a spectrophotometer (UV–Vis model 1601, Shimadzu, Japan). The absorbance of a prepared blank was also recorded. Total flavonoid content expressed as rutin equivalents in mg g1 dry weight of plant was also determined. Proximate analysis of the extracts was performed in triplicate following AOAC (1990) procedures and included the following: moisture by air oven (AOAC method 930.15); ash by muffle furnace (AOAC method 942.05); protein by kjeldahl nitrogen (AOAC method 984.13) with conversion factor 6.25; fat by ether extraction (AOAC method 954.02); dietary fiber by enzymatic and gravimetric methods (AOAC method 985.29); and carbohydrate calculated by difference. Calcium and iron were determined using an atomic absorption spectrometer (model 1602, Shimadzu, Japan). White ash of the extracts was washed three times with 2 mL 2 M HNO3, vacuum filtered, and diluted in a 25 mL volumetric flask with deionized water (soln A). A quantity of 1 mL of soln A was transferred to a 50 mL volumetric flask, and 2 mL of 5% lanthanum chloride solution was

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added before the solution was diluted to volume with deionized water. Calcium and iron were determined in this solution by atomic absorption spectrophotometry at 422 and 324.7 nm using an air/acetylene oxidizing flame. Readings were compared to a standard curve between 5 and 30 mg L1. The standard solutions used in the establishment of the flow injection calibration graphs of calcium were prepared by rigorous dilution of BDH calcium nitrate standard solutions of 1000 mg L1. The calibration curve for iron was obtained by successive dilutions of standard ferric nitrate solution with doubledistilled deionized water using working ranges from 0 to 12.48 mg L1. The edible portions of the plant samples were analyzed for vitamin C content by a spectrophotometric method (Durust et al., 1997). An equal weight of oxalic acid solution (w/v, 0.4%) was added, and the mixture was then homogenized in an electrical high-speed homogenizer. A known portion of the homogenized mixture was diluted with oxalic acid solution to prepare an extract with 10 g of plant material extracted into 50 mL. Samples were diluted to a suitable concentration for analysis depending on the ascorbic acid content of the sample. The mixture was then filtered. An ascorbic acid calibration curve was prepared by using various standard ascorbic acid solutions (10, 20, 30, 40 and 50 mg L1). The spectrophotometer was adjusted to zero using deionized water. The absorbance at 530 nm of oxalic acid solution (1 mL)+acetate buffer solution (1 mL)+DCPI solution (8 mL) was recorded at the end of 15 s. This value was noted as X1. Then, the instrument again was adjusted to zero with a mixture of standard ascorbic acid solution of 10 mg L1 (1 mL), acetate buffer solution (1 mL) and deionized water (8 mL). Soon after, the absorbance of standard ascorbic acid solution (1 mL)+ acetate buffer solution (1 mL)+DCPI (8 mL) was recorded as X2. Here, X1 is the absorbance of total DCPI, and X2 is the absorbance value of the remaining DCPI after its reaction with ascorbic acid. X1X2 values are the absorbance of each working standard. The calibration graph was constructed by plotting the absorbance values versus concentration (mg L1) of standard ascorbic acid solutions (Durust et al., 1997). Energies were calculated from the protein, fat and carbohydrate content by using factors 4, 9 and 4 cal/g, respectively. 2.6. Data analysis Results are presented as mean value7standard deviation (at least three replicate experiments). Analysis of variance and significant differences among means were determined by one-way ANOVA using SPSS (Version 10, SPSS Inc., Chicago, USA). Significant differences were declared at Po0.05. PCA and PLS were used to classify the samples by Unscrambler software package (Version 9.2; CAMO, Trondheim, Norway). PCA was used to determine the simplest mathematic model able to describe the data set

satisfactorily. It is the most appropriate statistical approach when the goal is to detect the relative importance of individual variables for determining the data structure. PLS was used to detect cause–effect relationships. 3. Results and discussion This study focused on plant leaves because leaves are subject to illumination and consequently have an efficient antioxidant system (Masuda et al., 1999), but other plant tissues also showed strong antioxidant activity. All selected plant parts are used for food or medicine in Thailand except the seeds of Nephelium lappaceum Linn. Plants to study were selected for their astringency (for leaves) or for their yellow and violet colors (for flowers and fruits). Many literature reports indicate that phenolics contribute astringency to plants and flavonoids contribute violet colors (anthocyanins) or yellow colors (flavonols). The relationships between selected variables including yield, radical scavenging activity (1/EC50), content of total phenols and other chemical components in the ethanolic extracts from various varieties and parts of Thai plants were evaluated using PLS. This approach has been adopted because PLS can detect relationships and provide models useful for prediction. PLS extracts a few linear combinations (PLS factors) of the chemical and antioxidant data that predict as much of the systematic variation in the sample data as possible. Data were centered prior to PLS regression so that all results were interpretable in terms of variation around the mean. Variables were standardized, by weighting with the standard deviation, so that all variables were given the same chance to influence the estimation of the selected variables including yield, antioxidant activity, and total phenolic content with other chemical variables. Regression coefficients for the relationships between variables revealed by the PLS were estimated by the Jack-Knifing method. With the cross-validation employed, the same samples were used for both calibration and validation of the models. With full cross validation, each sample is removed one at a time from the sample set, a new calibration performed and a predicted score calculated for the sample removed. This procedure is repeated until all samples have been removed from the sample set once. In order to search for the best relationship for yield and the other chemical variables, correlation coefficients (R) were calculated. The root mean square error of cross-validation (RMSECV) was considered in order to compare the different established models for a given variable. The characterization of the plant tissues was achieved by PCA using all variables. PCA was performed on the basis of the covariance structure of the data which converts the means across the originally measured numerical information into new variables, the principal components (PCs), which are orthogonal. Hence, the sets presented on these axes are non-correlated with each other. The PCA allows the latent structures among the selected variables to be observed. The original variables are total phenolic content,

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total flavonoid content, radical scavenging activity, moisture content, yield, ash, protein, fat, carbohydrate, energy, dietary fiber, calcium, iron and vitamin C. The PCs are linear combinations of the variables. The pretreatment of the data with standardization was manipulated. This scaling procedure ensured that all the variables had the same weighting in the model. The results of PCA were visualized by a score and loading plot. A PCA model was validated using the ‘‘full cross-validation’’ technique to ensure predictive validity, which guards against overfitting. Consistency of data obtained from the two assessment years was checked before crossing chemical and antioxidant data, and it was found that the standard deviations of the data were less than 0.1, and hence the data had good reproducibility over the two years. 3.1. Relationship between yield and chemical composition of studied plants The yield of extractable compounds varied from 0.6% to 4.5% (w/w) (Table 1). The data showed that the yields from berries and fruits and seeds were higher than from other parts of the plants including leaves and flowers. Typically, leaf photosynthesis products being essential nutrients such as sucrose can be translocated from leaves to fruits and seeds which are the food storage organs of the plants (Salisbury and Ross, 1992). The content of carbohydrates, fat and protein (Table 2) in the fruits and seeds was higher than in the leaves and flowers of the Thai plants. A high yield (Table 1) did not correspond to high content of phenolic compounds and high antioxidant activity (Table 3). This is consistent with the finding of Ka¨hkonen et al. (1999) who reported that extracts contained some inactive compounds including residual sugars. From our results, Thai plants, which gave a high yield, had a high carbohydrate (Table 3) and a low dietary fiber (Table 4). Typically, the components of dietary fiber are both of insoluble plant cell wall materials and little water-soluble polysaccharides. Plant cell walls mostly contain lignin and cellulose (Salisbury and Ross, 1992). Cellulose, which is a highly ordered crystalline molecule, has poor solubility in common solvents like water and alcohols (Baird et al., 2008). In the extraction process in our experiment, the plant samples were ground with ethanol, and consequently the ethanol was slightly able to extract a range of plant cell components including cellulose. A data set of 15  28  3 (1260) values was used for PLS to investigate the relationship between yield and antioxidant activity, total phenolic content, total flavonoid content, moisture, ash, protein, fat, carbohydrate, dietary fiber, calcium, iron and vitamin C. Seventy percent of the variance was explained by the first PLS factor. The optimal number of PLS factors for the prediction of the yield value was 1. Table 5 shows that the explained variances of PLS factor 1 and 2 were 67.5% and 5.1%, respectively; so we

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can predict yield from PLS factor1 which gave a high explained variance sufficient for the total variance. According to the PLS analysis, factor1, accounting for 67.5% of the total variance, was strongly related to carbohydrate and slightly related to antioxidant activity, total phenolic content and total flavonoid content in the positive range of the regression coefficient (Fig. 1). Protein, dietary fiber and ash were on the negative regression coefficient. It appeared that yield closely depended on carbohydrate and dietary fiber values of the samples. The validation results showed that the RMSECV and correlation coefficients (R) were similar with no significant bias between them (Table 5). A scatter plot of actual yield value versus computed value for the sample validation (Fig. 2) sets also showed that the PLS calculation gave a good prediction of the yield values. In the literature, no model to predict yield of extract using chemical compositions of plants has been reported to compare with the present results. It has been reported in previous investigations that high yields of extract from some plants contained high levels of phenolic compounds and antioxidant properties (Guillen and Manzanos, 1996; Lehtinen and Laakso, 1998; Borneley and Peyrat-Maillard, 2000). This was not found in the present investigation especially for several types of plants studied. 3.2. Relationship between antioxidant activity and chemical composition of studied plants In general, the antioxidant activity of plant extracts is associated with specific compounds or classes of compounds, such as flavones, flavonols and proanthocyanidins in plant materials native to the Mediterranean area (Skerget et al., 2005), carotenoids (Stahl and Sies, 2003) and melatonin (Chen and Huo, 2003). Most of the antioxidant substances in plants are phenolic compounds. Phenolic substances serve as oxidation terminators by scavenging radicals to form resonance stabilized radicals (Rice-Evans et al., 1997). Although the antioxidant capacities are influenced by many factors, which cannot be fully described by a single method, the DPPH radical scavenging activity which is the most commonly used method for assessment of the antioxidant properties of natural products, was used in this study. The DPPH  assay is suitable for solvent extracts and as a rapid assay, it can be applied for monitoring the activity of numerous samples over a limited period of time. Moreover, it is reproducible and strongly correlated with phenolic compounds (Maisuthisakul et al., 2007; Katalinic, et al., 2006; Miliauskas et al., 2004; Matsuda et al., 2001). The scavenging effects of plant extracts on the DPPH radical are shown in Table 3. According to the results shown in Table 3, the plants, which had strong antioxidant activity, had high total phenolic and flavonoid contents. These were Eugenia siamensis Craib. fruit, Cratoxylum formosum Dyer. leaf, Erythrina crista Galli. Leaf and Careya sphaerica Roxb.leaf.

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Table 2 Ash, protein, fat, carbohydrate and energy of some Thai plants Scientific name

Herb and vegetable leaves Basella alba Linn. Careya sphaerica Roxb. Centella asiatica Linn. Cratoxylum formosum Dyer. Erythrina crista Galli. Lasia spinosa (Linn.) Thw. Leucaena glauca Benth Limnocharis flava Buch. Ocimum basilicum Linn. Ocimum sanctum Linn. Sauropus androgynus Linn. Spondias pinnata Kurz. Syzygium gratum (Wight) S.N.Mitra var. gratum

Plant part

Ash (g/100 g edible portion, db)a

Protein (g/100 g edible portion, db)

Leaf Young leaf Leaf Young leaf Leaf Leaf Young leaf Leaf Young leaf Young leaf Young leaf Young leaf Young leaf

leaf and

15.9 5.3

27.7 10.4

leaf and

12.6 4.1

Fat (g/100 g edible portion, db)

Carbohydrate (g/100 g edible portion, db)

Energy (Kcal/ 100 g edible portion, db)

3.1 2.3

42.1 74.4

306.7 359.6

12.7 15.6

6.2 10.8

53.1 63.2

319.3 412.6

leaf and

7.7 17.3 6.3

24.2 17.9 16.9

5.1 3.8 4.8

40.3 45.5 58.9

303.8 288.5 346.4

leaf and

11.4 11.8

11.3 15.3

8.4 6.7

55.4 51.5

341.9 327.7

leaf and

12.9

17.7

3.2

51.3

305.4

leaf and

12.9

15.8

4.0

54.5

317.0

leaf and

13.4

18.6

4.7

45.4

298.1

leaf and

5.6

15.2

5.4

58.3

342.6

Herbs and vegetable flowers Allium ascalonicum Linn. Azadirachta indeca A. Juss Var. siamensis valeton Cassia siamea Britt. Musa spiantum Linn. Sesbania grandiflora Desv.

Flower Flower

7.5 6.3

21.4 24.6

3.8 19.0

54.1 26.2

335.8 374.0

Flower Flower Flower

5.6 11.1 7.9

19.4 20.8 24.7

1.6 2.8 2.2

34.9 54.2 46.8

231.7 325.0 306.4

Berries and fruits Capsicum frutescus Linn. Eugenia siamensis Craib. Euginia malaccenses Linn. Momordica charantia Linn. Phyllanthus emblica Spondias pinnata Kurz.

Fruit Fruit Fruit Fruit Fruit Fruit

2.3 5.4 3.7 13.6 2.5 5.3

2.7 6.9 7.9 23.7 1.9 2.5

9.1 0.9 3.2 1.7 1.1 1.3

55.0 84.6 66.1 53.9 82.2 81.2

313.2 374.0 324.9 325.3 346.7 346.7

Berry and fruit seeds Nephelium lappaceum Linn. Parkia speciosa Hassk. Piper nigrum Linn. Tamarindus indica Linn.

Seed Seed Seed Seed

2.4 4.6 5.7 2.4

11.6 27.5 19.9 28.1

22.2 13.3 34.5 15.2

61.6 52.9 10.4 51.0

492.5 441.5 432.1 452.7

a

Dry weight basis of the original sample of plant parts.

In order to quantify more precisely the relationships, using the chemical variables as explicative ones, PLS regressions with cross-validation were performed. The dependence of antioxidant activity on all studied chemical variables except energy and yield was investigated. Although the data structure is very different, antioxidant activity of extracts from most of the 28 plant correlated strongly with other chemical values. PLS factor1 accounted for 72.3% of the total variance, and was strongly positively related to total phenolic and flavonoid content, and negatively with ash, iron and dietary fiber (Fig. 3) since the R-value was 0.71 (data not shown). The negative correlation between the ash and

the antioxidant properties can be explained. The ash contains minerals and heavy metals (including iron) which can act as pro-oxidants. These results for 28 plants confirm earlier results from a more limited study (Maisuthisakul et al., 2006). It is interesting to note that the dietary fiber is inversely associated with the DPPH radical scavenging activity. The main antioxidant mechanism of dietary fiber is as a metal chelating agent. Another mechanism is free radical scavenging due to some polyphenols which are associated with dietary fiber (Ubando-Rivera et al., 2005). However, increased dietary fiber could correspond to reduced low molecular weight polyphenol content, and hence reduced radical

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Table 3 Antiradical activity, total phenolic content and total flavonoid content of some Thai plants Scientific name

Plant part

Antiradical activity (1/EC50)

Total phenolics (mg GAE/g db)a

Total flavonoids (mg RE/g db)

Herb and vegetable leaves Basella alba Linn. Careya sphaerica Roxb. Centella asiatica Linn. Cratoxylum formosum Dyer. Erythrina crista Galli. Lasia spinosa (Linn.) Thw. Leucaena glauca Benth Limnocharis flava Buch. Ocimum basilicum Linn. Ocimum sanctum Linn. Sauropus androgynus Linn. Spondias pinnata Kurz. Syzygium gratum (Wight) S.N.Mitra var. gratum

Leaf Young Leaf Young Leaf Leaf Young Leaf Young Young Young Young Young

0.7 2.3 0.7 4.4 3.1 0.1 1.5 0.1 1.8 1.8 0.7 0.7 1.8

15.5 54.5 12.4 63.4 67.5 6.4 51.2 5.4 50.5 41.9 11.5 42.6 57.3

6.2 20.5 10.6 25.5 20.2 4.4 22.3 3.7 15.3 12.6 10.4 14.8 23.6

leaf and leaf leaf and leaf

leaf and leaf leaf leaf leaf leaf leaf

and and and and and

leaf leaf leaf leaf leaf

Herbs and vegetable flowers Allium ascalonicum Linn. Azadirachta indeca A. Juss Var. siamensis valeton Cassia siamea Britt. Musa spiantum Linn. Sesbania grandiflora Desv.

Flower Flower

2.6 1.2

55.7 40.3

20.2 29.8

Flower Flower Flower

2.4 1.8 1.7

51.5 45.3 58.6

24.8 20.3 13.1

Berries and fruits Capsicum frutescus Linn. Eugenia siamensis Craib. Euginia malaccenses Linn. Momordica charantia Linn. Phyllanthus emblica Spondias pinnata Kurz.

Fruit Fruit Fruit Fruit Fruit Fruit

1.8 5.0 2.2 1.7 2.0 1.6

40.3 82.4 69.2 50.9 69.1 47.2

13.3 44.3 28.7 21.6 23.4 12.6

Berry and fruit seeds Nephelium lappaceum Linn. Parkia speciosa Hassk. Piper nigrum Linn. Tamarindus indica Linn

Seed Seed Seed Seed

2.2 1.5 3.0 2.0

43.5 51.9 53.1 40.7

13.3 20.3 22.8 23.2

a

Dry weight basis of the original sample of plant parts.

scavenging activity, which would explain the relationship observed. 3.3. Relationship between total phenolic content and chemical composition of the plants Plants contain various classes of phenolic compounds. With important exceptions, the functions of most phenolics are obscure. Phenolics appear to be by-products of metabolism in plants. Phenolic compounds are produced from the shikimic acid pathway, which occurs in plant respiration. Many other phenolics also arise from the shikimic acid pathway and subsequent reactions. Among these are the acids cinnamic, p-coumaric, caffeic, ferulic, chlorogenic, protocatechuic and gallic acids. These are derived from phenylalanine and tyrosine, which are amino acids. In addition, there are several processes to translocate carbohydrate from leaves to various sink organs during photosynthesis.

Even though a variety of plants are known to be sources of phenolic compounds, data on their composition are insufficient. The Folin–Ciocalteu method is the one adopted in almost all the published works about screening of natural antioxidants, being considered the best method for determination of total phenolic content (Spigno et al., 2007). The standard gallic acid is most often used for the Folin–Ciocalteu method. It is well known that Folin– Ciocalteu’s phenol reagent gives different responses to different phenolic compounds, depending on chemical structure (Spigno et al., 2007). Nevertheless, a good correlation (R ¼ 0.8) was observed between 1/EC50 and total phenolic content of Thai plants. Table 3 shows the total phenolic and flavonoid contents. We found that fruit of E. siamensis Craib. had the highest antioxidant activity and total phenolic as well as total flavonoid content. The fruit has purple peel, which indicates that it has a high anthocyanin content that

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Table 4 Dietary fiber, calcium, iron, vitamin C of some Thai plants Scientific name

Plant part

Dietary fiber (g/100 g edible portion, db)

Calcium (mg/100 g edible portion, db)

Iron (mg/100 g edible portion, db)

Vitamin C (mg/100 g edible portion, db)

Herb and vegetable leaves Basella alba Linn. Careya sphaerica Roxb. Centella asiatica Linn. Cratoxylum formosum Dyer. Erythrina crista Galli. Lasia spinosa (Linn.) Thw. Leucaena glauca Benth Limnocharis flava Buch. Ocimum basilicum Linn. Ocimum sanctum Linn. Sauropus androgynus Linn. Spondias pinnata Kurz. Syzygium gratum (Wight) S.N.Mitra var. gratum

Leaf Young Leaf Young Leaf Leaf Young Leaf Young Young Young Young Young

11.3 7.7 15.3 6.2 22.8 15.4 13.1 52.6 14.7 30.6 36.0 47.3 15.5

48.7 52.6 902.0 448.3 304.2 250.0 684.9 452.8 998.7 198.9 118.8 203.4 55.6

21.5 6.9 28.6 17.2 9.8 19.2 46.1 285.8 170.6 126.1 13.5 42.7 56.2

400.0 515.5 107.0 395.4 364.3 455.1 39.8 452.8 22.9 201.6 56.1 220.3 73.5

leaf and leaf leaf and leaf

leaf and leaf leaf leaf leaf leaf leaf

and and and and and

leaf leaf leaf leaf leaf

Herbs and vegetable flowers Allium ascalonicum Linn. Azadirachta indeca A. Juss Var. siamensis valeton Cassia siamea Britt. Musa spiantum Linn. Sesbania grandiflora Desv.

Flower Flower

13.2 53.8

94.3 959.9

40.3 19.3

616.4 532.2

Flower Flower Flower

38.5 11.1 50.9

55.6 407.4 27.7

6.9 9.3 13.5

483.3 328.7 485.7

Berries and fruits Capsicum frutescus Linn. Eugenia siamensis Craib. Euginia malaccenses Linn. Momordica charantia Linn. Phyllanthus emblica Spondias pinnata Kurz.

Fruit Fruit Fruit Fruit Fruit Fruit

30.8 2.2 19.0 7.2 12.3 9.7

44.5 295.3 16.4 188.1 111.1 709.3

3.4 7.4 11.6 13.8 3.1 6.2

246.6 366.9 264.6 460.6 636.0 44.1

Berry and fruit seeds Nephelium lappaceum Linn. Parkia speciosa Hassk. Piper nigrum Linn. Tamarindus indica Linn

Seed Seed Seed Seed

1.7 1.7 29.5 3.4

30.4 265.1 563.6 9.1

2.8 2.7 11.6 0.5

23.5 19.3 49.7 22.4

a

Dry weight basis of the original sample of plant parts.

Table 5 Partial least square (PLS) validation statistics for yield prediction from chemical compositions of each plant in the set of samples PLS factor(s)

Ra

RMSECV (%)b

Bias (%)

Percent of variation explained by the model

1 2

0.72 0.74

0.63 0.60

0.02 0.01

67.48 72.61

a

R, correlation coefficient. RMSECV, the root mean square error of cross validation.

b

contributes to the strong antioxidant activity and the total phenolic and flavonoid content. Correlations of chemical variables with total phenolic content are shown in Fig. 4. This illustrates the relationships between these variables for 28 studied Thai plants. According to the PLS factor1, accounting for 71.0% of the total variance, only the total flavonoid content was correlated

with a positive regression coefficient (Fig. 4). The validation results showed that the RMSECV and bias were low with good correlation coefficients (R ¼ 0.7). We know that flavonoids are a subset of phenolic compounds. Hence, it is expected that the content of total phenolic compounds relates to total flavonoid content, whereas the other chemical components did not relate to phenolic compounds.

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Fig. 1. The result of regression coefficient of yield with antioxidant activity, total phenolic content (TP), total flavonoid content (TF), moisture, protein, fat, carbohydrate, dietary fiber, ash, calcium, iron and vitamin C contents.

Fig. 2. Linear regression plot of measured versus predicted yield value of sample set.

Fig. 3. The result of regression coefficient of antioxidant activity with total phenolic content (TP), total flavonoid content (TF), moisture, protein, fat, carbohydrate, dietary fiber, ash, calcium, iron and vitamin C contents.

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Fig. 4. The result of regression coefficient of total phenolic content with total flavonoid content (TF), moisture, protein, fat, carbohydrate, dietary fiber, ash, calcium, iron and vitamin C contents.

3.4. Classification of Thai plants by using PCA New sources of natural antioxidants are of interest to replace synthetic food additives. Many literature reports describe studies of the antioxidant capacity from several parts of plants (Amakura et al., 2002; Aljadi and Kamaruddin, 2004). Hence, it is valuable if we can classify the part of plants according to their antioxidant properties, phenolic compounds and chemical compositions. Tables 2 and 4 show the chemical composition of 28 studied plants. The investigated seeds showed higher fat content than leaves, flowers and fruits of studied plants. Piper nigrum Linn. showed the highest fat content. This plant seed is used as food in Thai cuisine and has a special flavor. Vitamin C in the seeds was low, whereas the other chemical values did not have a clear pattern. To understand more about the relation between the variables and the clustering group, PCA was applied. PCA on these attributes explained 54.3% of the variability in the data in the first two dimensions (Table 6). The first two PCs accounted for 33.5% and 20.8% of the data variance. Further components explained 12.8%, 9.5% and 6.9% variance, respectively. The loading of PC1 had a strong positive correlation with antioxidant activity, total phenolic content, total flavonoid content, ash and yield (Table 6). The strong positive loadings of PC2 were moisture, fat, carbohydrate and energy. In the factor loading plots, ash was in the opposite direction to antioxidant activity, and total phenolic and flavonoid content were along the axis of the first factor. Four classes were roughly grouped and used to generate the plant part image in biplots, which are combined score and loading plots (Fig. 5). This figure showed how these four populations were differently structured according to their chemical composition. On the first two dimensions, yield and carbohydrate content allowed fruit to be slightly distinguished from the other parts of plants, whereas energy and fat clearly distinguished seeds

Table 6 Explained variance of the first five principal components (PCs) from the principal component analysis (PCA) Parameters

Antiradical activity (1/EC50) Total phenolics (TP) Total flavonoids (TF) Moisture content Protein Fat Carbohydrate Dietary fiber Ash Calcium Iron Vitamin C Energy Yield Average

Explained variance PC1

PC2

PC3

PC4

PC5

61.50 59.40 52.67 21.83 12.98 2.31 29.76 36.10 69.86 3.69 26.12 0.79 38.49 53.23 33.48

0.24 2.37 0.05 41.16 25.40 81.56 50.33 0.15 2.72 4.42 0.90 26.16 42.66 13.49 20.83

14.11 19.77 23.50 3.11 29.68 0.33 16.93 9.36 0.00 4.80 7.03 30.36 5.52 14.54 12.79

5.14 3.98 7.87 8.22 8.59 4.00 0.65 11.31 1.30 54.48 27.02 0.09 0.00 0.07 9.48

1.65 0.29 2.09 2.69 10.08 3.81 0.06 32.97 14.61 17.60 1.95 7.64 0.18 0.56 6.87

from the other parts. Leaf and flower parts had a similar pattern of chemical composition, because classification on the basis of chemical attributes did not occur. This implied that characterization of plant extracts requires analysis of more chemical variables than the 14 studied variables. This study has shown that antioxidant activity, total phenolic content, total flavonoid content, moisture content, yield, ash, protein, fat, carbohydrate, energy, dietary fiber, calcium, iron and vitamin C provide a basis for the observed discrimination between seeds and the other parts of plants including leaves, flowers and fruits of 28 plants. 4. Conclusions Over the data set of 28 plant samples, PLS demonstrated accurate correlations between yield and carbohydrate

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Fig. 5. Bilpot obtained from PCA illustrating separation of groups (L: leaf and young leaf, F: fruit, FW: flower, S: seed).

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