5.1. introduction - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/5087/13/13_chapter...

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5.1. INTRODUCTION At present naturally occurring phytochemicals are of major scientific

interest. Technically, the term ‘‘phytochemicals’’ refers to every naturally occurring

chemical substance present in plants, especially to those that are biologically active

(Caragay, 1992) .They occur in small amounts in all groups of plants and in all parts

of plants- woods, barks, stems, pods, leaves, fruits, roots, flowers, pollens and seeds

(Pratt and Hudson, 1990; Pratt et al., 1992; Macias et al., 2007).

All living organisms are composed of chemical substances from both the

inorganic and organic world that appear in roughly the same proportions, and

performs the same general tasks. Hydrogen, oxygen, nitrogen, carbon, phosphorus,

and sulfur form normally a major portion of all the living cells. The primary

metabolic routes use these compounds and produce primary metabolites. The

primary metabolites are present almost everywhere in nature and are essential for all

life forms. The primary metabolites include the common carbohydrates, fats,

proteins and nucleic acids that are needed to procreate and maintain life. Typically

they are involved in the energy regulation of organisms, growth and development of

tissues. In short, they are the building blocks of the organisms. (Salisbury and Ross,

1974; Taiz and Zeiger, 2003; Ari Tolenen, 2003). According to Mallette et al

(1960) the chemical constituents present in the plants can be considered from the

standpoint of utility to the cell, viz. structural materials, food reserves, metabolic

machinery, incidental and special substances.

5.1.1. Structural chemicals

The organic biomolecules are initially utilized in the synthesis of a small

number of building blocks that are, in turn, used in the construction of a vast array of

vital macromolecules. Some of these materials are providing rigid structure to the

cells and organisms. In the young cells the quantity of structural materials is very

low. On the other hand, in the matured cells the walls are so thick as to leave small

cavities within the cells. Such cells frequently die and then serve only in supporting

and protecting the softer tissues. The polysaccharides constitute approximately 75

percent of the dry weight of higher plants for example cellulose, hemi cellulose,

pectin, gum arabic, gum tragacanth, mucilage, and lignin. Most of these

polysaccharides are components of cell wall. Once formed, the structural materials

are quite inert when considered from the metabolic standpoint (Mallete et al., 1960).

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5.1.2. Food reserves

Plants prepare their own food (e.g. photosynthetic process), which serve as

reserves during the periods of surplus synthesis. This will be stored as

carbohydrates, lipids, and proteins. These reserves are stored (1) in the active cells

as amyloplasts and oil vacuoles, (2) in special storage organs for the purpose of

providing new vegetative growth (tuber, bulb, etc.) or, (3) in the fruit and seed for

the next generation (Mallete et al., 1960).

5.1.2.1. Carbohydrate

A carbohydrate is an organic compound with the general formula Cn (H2O) n

i.e. consists only of carbon, hydrogen and oxygen, with the last two in the 2:1

atomic ratio. The carbohydrates (saccharides) are classified into monosaccharide,

oligosaccharides, and polysaccharides.

The plant uses solar energy to oxidize water, releasing oxygen and reduce

carbon dioxide there by farming large carbon compounds, primarily sugars. This

complicated process can be summarized as.

CO2 + H2O (C6 H12 O6) n + O2

At the time of energy surplus these sugar molecules are polymerised and

stored as a reserve material. Carbohydrates serve various functions in a living cell;

they transport energy and supply C to other metabolic pathways. Starch and sucrose

are the storage forms of reserve carbohydrates. In the major economically valuable

timber plants, which are commercially, exploited cellulose and hemicellulose are the

carbohydrate. A number of other carbohydrates (D-fructofuranose, fructosans, D-

galactose, D-mannose, D-galactopyranose, galactomannans etc. are found in a

variety of plants (Mallete et al., 1960; Salisbury and Ross, 1974; Taiz and Zeiger,

2003).

5.1.2.2. Lipids

The term lipids include a variety of organic compounds found in the plants.

In many cases the term is synonymous to the “ether extractable,” and includes the

triglycerides, phospholipids, and waxes as well as the non- fatty acid containing

substances, resins, resin acids, terpenes (essential oil), and plant sterols, which

possess diversified biological and commercial applications.

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Lipids are classified into three main groups, namely, simple lipids,

compound lipids, and hydrolytic products of lipids or derived lipids. Simple lipids

are defined as organic esters which, upon hydrolysis, yield only aliphatic alcohols

and aliphatic monocarboxylic acids. Examples are fats and waxes. Upon hydrolysis,

compound lipids yield aliphatic alcohols, aliphatic monocarboxylic acids, and other

products such as carbohydrates, phosphoric acid, or nitrogen bases. Examples of this

group are the phospholipids and the glycolipids. The hydrolytic products of derived

lipids include the fatty acids, various alcohols, such as glycerols and the sterols, and

a number of nitrogenous compounds such as cholins or sphingosine (Mallete et al.,

1960; Salisbury and Ross, 1974; Taiz and Zeiger, 2003).

5.1.2. 3. Proteins

Proteins (also known as polypeptides) are organic compounds made of

amino acids arranged in a linear chain and folded into a globular form. The amino

acids in a polymer are joined together by the peptide bonds between the carboxyl

and amino groups of adjacent amino acid residues. The sequence of amino acids in a

protein is defined by the sequence of a gene, which is encoded in the genetic code

(Ridley, 2006).

In general, the genetic code specifies 20 standard amino acids; however, in

certain organisms the genetic code can include selenocysteine and pyrrolysine.

Shortly after or even during synthesis, the residues in a protein are often chemically

modified by post-translational modification, which alter the physical and chemical

properties, folding, stability, activity, and ultimately the functions of the proteins.

Proteins can also work together to achieve a particular function, and they often

associate to form stable complexes (Maton et al., 1993). Like other biological macromolecules such as polysaccharides and nucleic

acids, proteins are essential parts of organisms and participate in virtually every

process within the cells. Many proteins are enzymes that catalyze biochemical

reactions and are vital to metabolism. Proteins also have structural or mechanical

functions. Other proteins are important in cell signaling, immune responses, cell

adhesion, and the cell cycle. Proteins are also necessary as fodder; since animals

cannot synthesize all the amino acids they need and must obtain essential amino

acids from food. Through the process of digestion, animals break down ingested

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protein into free amino acids that are then used in the metabolism (Taiz and Zeiger,

2003; Nelson, 2005).

Proteins from different vegetable sources are so similar in chemical

composition that they are used interchangeably for many industrial purposes. Ease

of isolation and cost of the starting material are the determining factors. The most

important industrial uses of proteins are for the production of plastics and adhesives,

in coatings for the paper products, and in bonding ply wood veneers. Artificial

textile fibers can be prepared from vegetable proteins (Mallete et al., 1960;

Salisbury and Ross, 1974)

5.1.3. Metabolic machinery and cofactors

Associated with the reserve proteins in the seed, and widely distributed

throughout the remainder of the plant, are the proteins which serve to catalyze the

multitude of reactions involved in the complicated process termed metabolism.

Enzymatic proteins require the assistance of large numbers of cofactors in the

performance of their roles as catalysts. Included in this class are the inorganic

elements (macro and micro elements), vitamins, nucleotides, and the plant growth

regulators (Mallete et al., 1960).

5.1.4. Incidental substances

In addition to the above mentioned, essential elements, the plant absorbs

many of the diverse inorganic ions found in the soil (ex. selenium, mercury,

cadmium, arsenic, lead, cobalt, etc.). Plants grown in soils containing appreciable

quantities of these and similar elements accumulate measurable amounts. In most

cases such absorption is inconsequential, both to the plant and to the animal which

consumes it; however, selenium being a well-known exception (Mallete et al.,

1960).

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5.1.5. The special substances

The special substances are the substances which are produced by the plants.

Plants produce a large, diverse array of organic compounds that appear to have no

direct function in the growth and development. These substances are known as

secondary metabolites, secondary products, or natural products. Secondary

metabolites have no generally recognized, direct role in the process of

photosynthesis, respiration, solute translocation, protein synthesis, nutrient

assimilation, differentiation, or the formation of carbohydrates, proteins, and lipids.

But their roles as plant protectants and chemical reservoir have been later on brought

to light (Harborne, 1993). Secondary metabolites also differ from primary

metabolites in having a restricted distribution in the plant kingdom. That is,

particular secondary metabolites are often found in only one plant species or related

group of species, whereas primary metabolites are found throughout the plant

kingdom (Mallete et al., 1960).

5.1.6. Factors affecting the chemical composition

The evolutionary history of the plant kingdom is a story of constant

adaptations to the changing environmental conditions. Plants, as sedentary

organisms, have to adjust to the surrounding environment during their life cycle

(Harborne, 1993; Ferni., 2007).

Plants have adapted by modifying morphological and anatomical features,

through physiological variation or by biochemical means. Biochemical adaptation

may involve both basic / primary metabolism and special/ secondary metabolism

(Harborne, 1993). This renders metabolic responses to stress and to interactions with

its environment primordial features (Pichersky et al., 2006; Goff and Klee, 2006;

Kappers et al., 2005). To meet such demands it has been estimated that the plant

kingdom contains upwards of 200,000 metabolites (De Luca and St Pierre, 2000)

with values for a single species being given in the order of 15,000 (Dixon, 2001;

Hartmann et al., 2005).

In recent years, increasing attention has been paid to the ways the plants are

biochemically adapted to their differing environments. Many studies have been

conducted by biochemists world over to explore the biochemical adaptation of the

plants at various environmental variables like seasonal effects, drought, frost,

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salinity, mineral nutrients, heavy metal toxicity, altitude, various soil textures,

radiation, etc. The biotic variations such as age of the plant, microbial attack,

grazing, competition, and individual nutritional status, have been proven to have an

impact on the secondary metabolite profile in higher plants (Harborne, 1982).

Among the studies on the impact of abiotic factors over the secondary plant

products the contribution of the following phytochemists are noteworthy: Korner,

(1999); Spitaler, (2006), Owuor et al., (2008) have proved the altitudinal impacts on

plant materials. Krishnan et al., (2000) and Chen et al., (2009) implicated seasonal

changes along with altitudinal variations. The seasonal and climatic variations have

been strongly witnessed by many scientists (Adam, 1970; Kramer and Kozlowski,

1979; Bonicel et al., 1987; Ashworth et al., 1993; Rinne et al., 1994; Zidorn and

Stuppner, 2001; Barbarox and Breda, 2002; Bhowmik and Matsui, 2003; Geography

and climate- Namdeo et al., 2010).

The impact of seasonal and locational influences on the metabolites of a

popular native curative plant Calotropis has so far not been reported.

5.2. MATERIALS AND METHODS

5.2.1. Plant material collection for the seasonal and locational studies

The collection of plant materials is identical to what has been described in

the previous Chapter (2.4).

5.2.2. Drying of plant materials

This process is done to ensure good keeping qualities and suppress the

action of enzymes on the sampled plants. The collected plant materials were washed

thoroughly to remove the dirt and other contaminations. Then the collected leaves

were dried carefully under shade, at room temperature so as to retain their fresh

green colour, and also to prevent decomposition of active compounds. The dried

leaves were powdered using a stone grinder. The powdered materials are henceforth

termed as crude drugs. Crude drugs were stored in airtight, dark, glass container.

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5.2.3. Phytochemical analysis

The crude drugs were used to perform the following analyses:-

Table 5.1. Phytochemical analysis

S.No. Parameters Extraction / analytical method References

1. Organic Carbon Volumetric method Walkley and Black, 1934

2. Total Carbohydrate Hydrolysed by 2.5 N HCl – Anthrone method - colorimetry

Hedge and Hofreiter,1962

3. Total Protein Buffer Extract- Lowrys method - colorimetry

Lowry et al., 1951; Mattoo,1970

4. Total Lipid Soxhlet method - gravimetry Folch et al 1957

5. Calorific value

(Carbohydrate x 4.15)+ (Fat x 9.4) +(Protein x 5.65) Phillips, 1969

6. Ash content Dry ash – gravimetry Renaud et al., 1994.

7. Total Nitrogen Micro-Kjeldahl method - Titrimetry

Pellettand young,1980; Thenmoli Balasubramanian and Sadasivam,1987

8. Total Phosphorous Triple acid digestion-colorimetry (Spectronic 20 at 540 nm +)

Jackson,1973

9 Total Potassium, Triple acid digestion-Flame photometry (ELICO-361) Jackson,1973

10

Calcium, magnesium, zinc, copper, iron, manganese, ,boron, molybdenum, chromium, nickel, cadmium, lead, cobalt, mercury, arsenic, cyanide, Selenium and silver

Triple acid Digestion -AA Spectrophotometry (Varion 200AA)

Baker and Suhr, 1982; Allen ,1989

5.2.4. Elemental analysis

Two grams of ash were digested with mixture of nitric acid, sulphuric acid,

and perchloric acid in the ratio 11:6:3, for 24 hours to remove the organic matters.

The digested sample was made up to 100 ml and used for the assay of the trace

elements through Atomic Absorption Spectrophotometer (AAS- Varion 200AA)

using suitable hollow-cathode lamps. Appropriate working standard was prepared

for each element. All elements were determined through this procedure. A blank

reading was also obtained.

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5.2.5. Extraction and GC-MS analysis

The crude drug was subjected to extraction with analytical grade solvent of

chloroform for GC-MS analysis. 25 g of the crude drug was taken in a round bottom

flask and 50ml of analytical grade chloroform was added and refluxed for 8 hrs.

After completion of the 8hrs the round bottom flask was cooled and the extract was

filtered through the Buchner funnel. The extract was evaporated to dryness under

nitrogen atmosphere using turbo evaporator. The residue obtained was dissolved in

2ml chloroform and transferred into the GC vial and injected into the GC-MS port.

GC–MS analysis was performed on an Agilent gas chromatograph model

6890 N coupled to an Agilent 5973 N mass selective detector. Analytes were

separated on an HP-5MS capillary column (30 m X 0.25 mm X 1.0 μl) by applying

the following temperature program: 40 °C for 5 min, 40–70 °C at 2 °C /min, 70°C

for 2 min, 70–120 °C at 3 °C /min, 120–150 °C at 5 °C /min, 150–220 °C at 10°C

/min and then 220 °C for 2 min. Transfer line temperature was 280 °C . Mass

detector conditions were: electronic impact (EI) mode at 70 eV; source temperature:

230 °C; scanning rate 2.88 scan S-1; mass scanning range: m/z 29–540. Carrier gas

was helium at 1.0 ml min-1. The tentative identification of volatile components was

achieved by comparing the mass spectra with the data system library (NIST 98) and

other published spectra (Mass Spectrometry Data Centre., 1974), supported by

retention index data, which were compared with available literature retention indices

(NIST Chemistry Web Book, 2005). All compounds were quantified as 3-octanol

equivalents.

5.3. RESULTS AND DISCUSSIONS

5.3.1. Seasonal variations

5.3.1.1 Seasonal variations in the organic compounds

5.3.1.1.1 Organic carbon

Organic carbon is one of the most stable elements of plant biomass. The

present observation shows that the organic carbon differs significantly between

seasons (Figure 5.1 and Table 5.2a). In the southwest monsoon season (S4) the plant

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25.4 25.6

24.8

27.5

23

23.5

24

24.5

25

25.5

26

26.5

27

27.5

Org

anic

car

bon

( %)

S1 S2 S3 S4

sample possesses the highest organic carbon (27.5%) and in the summer season (S3)

the lowest (24.8%).

Though significant the current study recorded a little variation in the organic

carbon. The difference between the highest and lowest values is only 2.7%. The

organic carbon shows a positive relationship only with the highest R.H.and the

nitrogen (Table 5.2b and 5.2c).

Figure 5.1. Organic Carbon in the plant – seasonal study

Table 5.2a. Seasonal variations in organic carbon

(One sample t - test)

Parameter N Mean Std. Deviation t Statistical

Inference

Organic Carbon (%) 4 25.825 1.1673 44.24

9 P<0.01 significant

DF = 3

Table 5.2b. Karl Pearson correlation between season and organic carbon

Organic carbon vs. Correlation value Statistical inference

Highest R.H. morning -.957* P<0.05 Significant

N= 4

Table 5.2c. Karl Pearson correlation between soil and organic carbon

Organic carbon vs.. Correlation value Statistical inference Total Nitrogen -.959* P<0.05 Significant

N= 4

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The current global stock of soil organic carbon is estimated to be 1,500–

1,550 Pg (Batjes, 1996; Post, 2001; and Lal, 2004). This constituent of the terrestrial

carbon stock is twice as that of the earth’s atmosphere (720 Pg), and more than triple

the stock of organic carbon in terrestrial vegetation (560 Pg) (Bolin, 1970; Baes et

al., 1977).

Baties and Sombroek, (1997) reported the quantity of C stored in soils to be

about three times more than the vegetation and twice as much as that which is

present in the atmosphere (Batjes and Sombroek, 1997). But, in the case of

Calotropis the amount of organic carbon is higher than of the soil (Chapter 4.3.4).

Brogowski, et. al., (2002) observed a significant loss in the carbon content

during the winter in crop plants. This was related to the weight loss of the crop.

5.3.1.1.2. Seasonal variations in the carbohydrate, protein and lipid

The variations in the quantity of primary metabolites (carbohydrate, protein

and lipid) among seasons are statistically significant (figure 5.2 and Table 5.3a). The

carbohydrate, protein and lipids are slightly higher in southwest monsoon and lower

in summer. The average contents of carbohydrate is between 9.3% (L3) to 10.2%

(L4); the protein is between 5.3 % (L3) to 6.2 % (L4) and the lipid is 2.7 % (L2) to 3

%( L4).

The correlation analysis expresses a significant positive relation of protein

with the highest R.H. morning and evening and lipids with the lowest R.H. evening.

Highest R.H. morning is related to the protein and the lowest R.H. evening is

significantly correlating with the lipids (Table 5.3b). This suggests that the R.H is

the main factor which highly influences the primary metabolism than the other

meteorological parameters. Among the soil components, nitrogen is correlated with

the protein, the potassium is linked with the lipid and the sodium is connected with

the carbohydrate and lipids (Table 5.3c).

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9.7 9.89.3

10.2

5.5 5.4 5.36.2

2.9 2.7 2.8 3

0

2

4

6

8

10

12

1 2 3 4

( % )

TotalCarbohydrate

TotalProteins

Total Lipids

Figure 5.2. Carbohydrate, protein and lipid in the plants (seasonal study)

Table 5. 3a.Variations in the Carbohydrate, protein and lipids


Parameter N Mean Std. Deviation t Statistical Inference

Carbohydrate 4 9.75

0.369 52.748 P<0.01 significant

Protein 4 5.6

0.408 27.434 P<0.01 significant

Lipid 4 2.85 0.1291 44.152 P<0.01 significant

DF = 3

In the earlier studies contents of the carbohydrates were found to vary

between the summer and winter samples. Yoo et al. (1996) found the seasonal

variations in carbohydrate, (starch and other soluble sugars) in the White forsythia.

Carbohydrate loss in winter was integrated with the nucleic acids, and the

physiological processes (Brogowski, 2002).

Arbutus unedo and Olea europaea contained higher amounts of total lipids at

the beginning of the growing season (Christou et al., 1994). Similarly the current

observations of these compounds show significant increase in the southwest

monsoon and decrease in summer. This decrease may be, to maintain the

physiological processes.

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Table 5.3b. Karl Pearson correlation between meteorological elements and the

seasonal variation in the Carbohydrates, Proteins and Lipids

Correlation value Statistical inference Parameter

Carbohydrate Protein Lipid Carbohydrate Protein Lipid

Highest R.H. morning

-0.812

-.980*

-.775

P>0.05 N.S.

P<0.05 Significant

P>0.05 N.S.

Highest R.H. evening

-0.755

-.960*

-.721

P>0.05 N.S

P<0.05 Significant

P>0.05 N.S

Lowest R.H. evening

0.855

.837

.993**

P>0.05 N.S

P>0.05 N.S

P<0.01 Significant

Mean Wind Speed

0.785

.983*

.835

P>0.05 N.S.

P<0.05 Significant.

P>0.05 N.S.

N=4; N.S= Not significant

Table 5.3c. Karl Pearson correlation between soil parameters and the seasonal

variation in the Carbohydrates, Proteins and Lipids

Correlation value Statistical inference Parameter Carbohyd

rate Protein Lipid Carbohydrate Protein Lipid

Total nitrogen -.899 -.986* -.949 P>0.05 N.S.

P<0.05 Significant

P>0.05 N.S.

Potassium -.775 -.854 -.964* P>0.05 N.S

P>0.05 N.S

P<0.05 Significant

Sodium -.953* -.902 -.992** P<0.05 Significant

P>0.05 N.S

P<0.01 Significant


5.3.1.1.3. Seasonal variations in the energy value

Several publications deal with differences in caloric values of many species

from different sex classes, nutritional status and different stages of life history are

the main reasons for this variability of the energy contents (Wiegert and Hasler,

1965; Gyllenberg, 1969; wising, 1971; Benedetto Castro, 1975). In the higher plants,

there is a distinct dependence of caloric values up on the climatic conditions, the

availability of water and the concentration of dissolved salts in the soil (Malone,

1968; Caspers, 1975). Considerable variations of energy values between the

individual components of plant species have been reported by Runge (1971),

Larcher et al., (1973) and Caspers (1975). The endogenous, climate dependent

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98.59

96.5694.86

105.56

88

90

92

94

96

98

100

102

104

106

108

S1 S2 S3 S4

(Kca

l /10

0g)

energy value was previously recorded in eight species of a meadow and an old-field

community (Caspers, 1977).

The seasonal variations in the energy potential of the plants are depicted in

figure 5.3. The highest energy potential (9.46 kcal.) was found in southwest

monsoon season and the lowest (8.36 kcal) in summer seasons. At the time of

adverse conditions the plant spends much of the energy to maintain the basic

metabolism (Brogowski et al., 2002). In the present study also the content of

carbohydrate, protein and lipids are significantly reducing at summer, which may be

due to the adaptive mechanism to escape from the dry-hot summer. As a result of

these metabolites, the energy contents are also coming down in summer. The

variation found is statistically significant (Table 5.4a).

Figure 5.3. Energy content in the plant (seasonal study)

Table 5.4a. Seasonal variations in the energy content


Parameter N Mean Std. Deviation t Significance

Calorific value 4 8.6600 .40620 42.639 P<0.01 significant

DF = 3

The calorie value of the plant is associated with the highest R.H. (morning

and evening) and wind speed (Table 5.4b) and the nitrogen content of the soil

(Table5.4c).

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Table 5.4b. Karl Pearson correlation between season and calorie value

Calorie value vs. Correlation value Statistical inference

Highest R.H.morning -.985 P<0.05 Significant

Highest R.H. evening -.964 P<0.05 Significant.

Mean Wind Speed .968 P<0.05 Significant

N=4

Table 5.4c. Karl Pearson correlation between soil and Calorie value


Total nitrogen -.969* P<0.05 Significant

N=4

5.3.1.1.4. Seasonal variations in the yield of extract

The extract yield potential of the plant varies significantly from season to

season (Figures 5.4 and Table 5.5a). The southwest monsoon sample provided the

maximum quantity of the extract (6.95%) and the pre-summer sample yielded the

lowest amount (4.95%).

The yield of extracts is only connected with mean maximum temperature and

minimum temperature and mean R.H morning (Table 5.5b).

6.4

4.95

6.85 6.95

0

1

2

3

4

5

6

7

8

S1 S2 S3 S4

( % )

Figure 5.4. Yield of extracts in seasons

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Table 5.5a Seasonal variations in the yield of extract



Yield of Extract 4 6.288 .9232 13.621 P<0.01 significant DF = 3

Table 5.5b. Karl Pearson correlation between season and yield of extract

Yield of extract vs. Correlation value Statistical inference

Mean Max Temperature 969* P<0.05 Significant Mean Minimum Temperature .991** P<0.01Significant Highest Temperature .968* P<0.05Significant Mean R.H. morning -.958* P<0.05Significant

N= 4

5.3.1.1.5. Compounds present in the chloroform extract

The analysis of the organic compounds present in the chloroform extract of

Calotropis through GC-MS revealed the presence of 64 compounds. Out of these the

summer sample seems to possess the maximum number of compounds (50) (Figure

5.5, 5.5a and Table 5.5, 5.6a). As per the numbers of the compounds synthesized,

the ascending order of the seasons is as follows:

Pre- summer, northeast monsoon, southwest monsoon and summer

(S2 - 35) < (S1 - 37) < (S4 - 40) < (S3 - 50).

The variation observed in the number of compounds is statistically

significant (Table 5.6b.).

Out of the 64 compounds, 24 show statistically significant seasonal

variations (1.Tetradecane 2. Hexadecane 3. Bicyclo[3.1.1]heptane,2,6,6-trimethyl-

,(1alpha,2alpha,5alpha) 4. 9-Octadecyne 5. n-Hexadecanoic acid 6. Phytol 7. 9, 12,

15-Octadecatrienoic acid,(Z,Z,Z)- 8. 9-Octadecenamide,(Z)- 9. Heptacosane 10. 13-

Docosenamide,(Z)- 11. Squalene 12. Nonacosane 13. Heptacosane,1-chloro- 14.

Tricosane 15. Vitamin E 16. Campesterol 17. Stigmasterol 18. Alpha-Amyrin 19.

4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a,

14b-octadecahydro-2H-picen-3-one 20. .alpha-Amyrin 21. 12-Oleanen-3-yl acetate,

(3alpha) - 22. 9, 19-Cyclolanost-24-en-3-ol, acetate 23. Urs-12-en-24-oic acid, 3-

oxo-, methyl ester, (+) - 24. Taraxasterol) (Table 5.6c).

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The correlation analysis shows the numbers of compounds are associated

only with the soil (Table 5.6d).

37 35

50

40

0

10

20

30

40

50

60

S1 S2 S3 S4

Num

ber o

f com

poun

ds

Figure 5.5. Number of compounds identified in seasons

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S1 (Northeast monsoon) S2 (Pre-Summer)

S3 (Summer) S4 (Southwest monsoon)

Figure 5.5a. GC - MS Chromatogram - seasonal samples

89

Table 5.6a. The composition of chloroform extract of Calotropis through GC-MS

in seasons

S.No. Rt. Compound name S1 S2 S3 S4

1 7.9 Dodecane - 0.28 0.29 0.14 2 9.1 2-Methoxy-4-vinyl phenol 0.35 - 0.11 - 3 9.7 Tetradecane 0.41 0.16 0.15 0.21 4 10.7 phenol, 2,4-bis(1,1-dimethylethyl) 0.24 0.12 0.11 -

5 11.1 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl- 0.08 - 0.06 -

6 11.5 Hexadecane 0.21 0.15 0.24 7 12.7 Tridecanoic acid, 12-methyl-methylester - - 0.14 - 8 13.1 Tetradecanoic acid 0.48 - 0.39 - 9 13.5 5-Ethylcyclopent-1-enecarboxaldehyde - - - 0.23

10 13.8 Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1alpha,2alpha,5alpha) 0.53 0.52 0.25 0.28

11 14.2 9-Octadecyne 0.23 0.15 0.15 0.28 12 14.8 Hexadecanoic acid,methyl ester 0.17 0.19 1.3 - 13 15.3 n-Hexadecanoic acid 9.68 6.33 4.33 2.68 14 16.7 9-Octadecanoic acid,methylester,(E)- - - 0.73 - 15 16.8 Phytol 1.11 1.05 0.76 0.56 16 17 Octadecanoic acid,methyl ester - - 0.46 - 17 17.3 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 3.3 2.36 2.05 0.55 18 17.5 9,12-Octadecadienoic acid(z,z)- 0.91 - - - 19 17.7 Dodecanamide 0.53 0.53 - 0.34 20 17.8 Hexadecanamide - - 0.37 - 21 19.2 Octacosane - - 1.19 -

22 19.5 A'-Neogammacer-22(29)-en-3-ol,acetate,(3beta,21beta)- - 1.39 - 1.55

23 20 9-Octadecenamide,(Z)- 4.42 2.51 2.47 3.99 24 20.4 Urs-20-en-3-ol,(3alpha,18alpha,19alpha)- - - - 1.09 25 20.6 Lup-20(29)-en-3-ol, acetate,(3beta)- - 2.57 1.57 2.1 26 21.6 Hop-22(29)-en-3alpha-ol - 14.19 7.29 9.67

27 22.3 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl)ester 0.37 - - -

28 23.2 Nonadecane,1-chloro- - - 0.71 - 29 23.6 Ergost-22-en-3-0l,(3alpha,5alpha,22E,24R)- - - 0.32 - 30 23.7 Stigmastane-3,6-dione,(5alpha) - 1.29 - - 31 24.2 Z-12-Pentacosene - - 0.08 - 32 24.6 Heptacosane 0.26 0.16 0.37 0.17 33 25 Tetracosanoic acid,methyl ester - - 0.07 - 34 25.6 13-Docosenamide,(Z)- 0.23 0.19 0.12 0.14 35 26 Squalene 1.76 0.89 1.84 0.97 36 26.9 Z-14-Nonacosane - - 1.16 1.84 37 27.2 Nonacosane 1.97 1.21 1.53 1.88

38 27.4 Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8-hexamethyl-,(1alpha,6beta, 7alpha,9alpha)- 0.14 0.16 0.43 1.49

39 27.6 Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7-(4-methyl-3- - - 0.92 -

90

pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1alpha,6alpha,7alpha,8alpha(1E,5E)]-

40 28.1 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E)- - - - 0.89

41 28.6 Heptacosane,1-chloro- 0.67 0.5 0.61 0.37 42 29.4 gamma-Tocopherol 0.25 - 0.26 - 43 30 17-Pentatriacontene - - 0.23 - 44 30.4 Tricosane 8.52 4.84 6.27 5.17 45 30.8 Vitamin E 1.15 3.86 3.85 1.47

46 31 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a-dimethyl-6-(1-methylethenyl)- - - - 3.25

47 31.2 Desmosterol - - 0.2 - 48 32.6 Campesterol 1.7 2.06 2.57 1.57 49 33.2 Stigmasterol 1.21 1.1 1.73 1.09

50 34.1 Ergost-8,24(28)-dien-3-ol,4,14-dimethyl-,(3alpha,4alpha,5.alpa.,)- 0.26 - 0.42 0.59

51 34.7 Stigmasterol,22,23-dihydro- 3.59 3.56 - 1.7 52 34.8 gamma-Sitosterol - - 3.86 - 53 34.9 Heneicosane,11-decyl- - - - 0.89

54 35.1 Pyridine-3-Carboxamide,oxime,N-(2-trifluromethyl phenyl)- - - - 2.53

56 35.2 Stigmasta-5,24(28)-dien-3-ol,(3alpha,24Z)- - - 2.04 - 57 35.7 alpha-Amyrin 5.76 4.07 4.02 4.2

58 36 4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a,14b-octadecahydro-2H-picen-3-one

1.36 1.45 0.63 0.98

59 36.5 4,22-Stigmastadiene-3-one 1.69 0.89 - 1.68 60 37 .alpha-Amyrin 8.42 6.04 13.35 13.67 61 38.4 12-Oleanen-3-yl acetate, (3alpha)- 16.9 12.44 9.58 10.16 62 38.7 9,19-Cyclolanost-24-en-3-ol,acetate 3.12 2.23 1.88 1.98 63 39.9 Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 13.6 15.5 13.03 15.11 64 40.2 Taraxasterol 4.16 5 3.6 2.3

Table 5.6b. Number of compounds identified in seasons through GC-MS



Total Number of compounds 4 40.50 6.658 12.165 P<0.01

significant DF= 3

91

Table 5.6c. Seasonal variation in the composition of chloroform extract


S.No. Parameter N Mean Std. Deviation t Significance

1 Tetradecane 4 .2325 .12121 3.836 P<0.05 significant

2 Hexadecane 4 .2675 .14009 3.819 P<0.05 significant.

3 Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1alpha,2alpha,5alpha)

4 .3950 .15067 5.243 P<0.05 significant

4 n-Hexadecanoic acid 4 5.7550 3.01234 3.821 P<0.05 significant

5 Phytol 4 .8700 .25703 6.770 P<0.01 significant

6 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 4 2.065 1.1413 3.619 P<0.05

significant

7 Heptacosane 4 .2400 .09764 4.916 P<0.05 significant

8 13-Docosenamide,(Z)- 4 .1700 .04967 6.846 P<0.01 significant

9 Squalene 4 1.3650 .50441 5.412 P<0.05 significant

10 Nonacosane 4 1.6475 .34798 9.469 P<0.01 significant

11 Heptacosane,1-chloro 4 .5375 .13200 8.144 P<0.01 significant

12 Tricosane 4 6.2000 1.66311 7.456 P<0.01 significant

13 Vitamin E 4 2.5825 1.47516 3.501 P<0.05 significant

14 Campesterol 4 1.975 .4475 8.826 P<0.01 significant

15 Stigmasterol 4 1.2825 .30325 8.458 P<0.01 significant

16 alpha-Amyrin 4 4.5125 .83512 10.807

P<0.01 significant

17

4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a,6b, 7,8,8a,9,10,11,12,12a,14,14a, 4b-octadecahydro-2H-picen-3-1

4 1.1050 .37652 5.870 P<0.05 significant

18 .alpha-Amyrin 4 10.370 3.75596 5.522 P<0.05 significant

19 12-Oleanen-3-yl acetate, (3alpha)- 4 12.270 3.3244 7.382 P<0.01

significant

20 9,19-Cyclolanost-24-en-3-ol,acetate 4 2.3025 .56453 8.157 P<0.01

significant

21 Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 4 14.310 1.1830 24.19

2 P<0.01 significant

22 Taraxasterol 4 3.7650 1.13353 6.643 P<0.01 significant

DF=3

92

Table 5.6d. Karl Pearson correlation between soil and the total numbers of

compound

Total numbers of compound vs.

Correlation value Statistical inference

Total magnesium -.965* P<0.05 Significant

N=5

5.3.1.1.6. Groups of phytochemicals

The identified 64 compounds (Table 5.6a) belong to different phytochemical

groups such as, terpenes, sterols, fatty acids, hydrocarbons, heterocyclic compounds,

phenolics and hydroxylamines (Table 5.7) Terpenes are the major constituents of the

chloroform extract. According to the percentage of compounds it can be arranged as

follows; Terpene Fatty acids>Hydrocarbon> Sterols>Heterocyclic

compounds>Phenolics>Hydroxylamines. According to the number of compounds it

can be arranged as follows; Hydrocarbon (8 - 14) > Terpenes (9 - 13) > Fatty acids (7

-12) >sterols (6 - 8)>Phenolics (1 - 2)> Heterocyclic compounds (1). The number

within the parentheses represents the number of component ranging between seasons.

Table 5.7. Groups of compounds identified in seasons

S.no. Compound group S1 S2 S3 S4

1 Terpenes 9 *(53.65 **)

12 * (65.14**) 11*(55.92**) 13*(62.44**)

2 Sterols 6*(11.54**) 6*(11.12**) 8 *(13.02**) 6*(8.61**)

3 Fatty acids 9*(19.94**) 7*(12.24**) 12*(12.56**) 7*(8.86**)

4 Hydrocarbons 8*(12.81**) 8 *(7.52**) 14*(14.10**) 12 *(15.85**)

5 Heterocyclic compounds 3 *(1.47**) 1 *(3.86**) 3*(4.18**) 1*(1.47**)

6 Phenolics 2 *(0.59**) 1*(0.12**) 2 *( 0.21**) -

7 Hydroxylamines - - - 1*(2.52**)

(* Total number; ** Tentative Quantity)

93

5.3.1.1.7. Seasonal variations in terpenoids

Terpenoids or terpenes comprise one of the most important groups of active

compounds in plants with over 20000 known structures. All terpenoid structures may

be derived from isoprene (five-carbon) units containing two unsaturated bonds. They

are synthesized from acetate via the mevalonic acid pathway (Pengelly, 2006).

The terpenes form the major portion of the chloroform extract of Calotropis.

There are 13 terpenes identified in the seasonal samples which can be classified as

monoterpenes, diterpenes, and triterpenes. The triterpenes are 11 in number (Table

5.8a). As per the number of terpene compounds present they are as follows: (S4)13>

(S2)12> (S3)11> (S1)9. Quantitatively they are in the following order: (S2)65.14% >

(S4)62.44% > (S3)55.92% > (S1)53.65%. The ‘Urs-20-en-3-ol, (3 beta 18 alpha, 19

alpha)’ are found only in the southwest monsoon. The seasonal variations in the

number and the quantity of terpenes are statistically significant (Table 5.8b).

The correlation analysis shows that the quantity of terpenes is significantly

associated with the heaviest rainfall (Table 5.8c). There is no correlation between the

soil parameters and the terpenes.

Triterpenoid compounds are derived from a C30 precursor, squalene

(Bruneton, 1995). They have similar configurations to steroids (found in plants and

animals) whose C27 skeletons are also derived from squalene (Pengelly, 2006).

Triterpenes attract attention because of their biological activities; e.g. taraxasterol,

beta-amyrine and alpha-amyrine, Taraxasterol was shown to exhibit considerable

activity against 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced inflammatory

ear oedema in mice and tumor promotion in mouse skin (Akihisa et al., 1996).

Triterpene alcohols from the flowers of Compositae were demonstrated to possess

marked anti-inflammatory activity (Akihisa et al., 1996). Taraxerol possesses

antiulcer properties. α -Amyrine, lupeol and cycloartan-type triterpenes are cytotoxic

agents (Banskota et al., 1999).

Table 5.8b. Terpenoids identified in seasons

S.No. Compounds Name S1 S2 S3 S4

Monoterpenes

1. Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1alpha ,2beta 5alpha) 0.53 0.52 0.25 0.28

94

Diterpenes (Acyclic) 2. Phytol 1.11 1.05 0.76 0.56 Triterpenes 3. Hop-22(29)-en-3.beta.-ol - 14.19 7.29 9.67 4. Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 13.6 15.5 13.03 15.115. Squalene 1.76 0.89 1.84 0.97 6. Taraxasterol 4.16 5 3.6 2.3

7. A'-Neogammacer-22(29)-en-3-ol,acetate,(3.beta.,21.beta.)- - 1.39 - 1.55

Pentacyclic triterpene 8. Alpha-Amyrin 8.42 6.04 13.35 13.679. Beta-Amyrin 5.76 4.07 4.02 4.2 10. Lup-20(29)-en-3-ol,acetate,(3.beta.)- - 2.57 1.57 2.1 11. 12-Oleanen-3-yl acetate, (3.alpha.)- 16.9 12.44 9.58 10.1612. Urs-20-en-3-ol,(3.beta.,18.alpha.,19.alpha.)- - - - 1.09

13. Beta-Amyrene derivatives-4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a, 6b,7,8,8a,9, 10,11,12,12a, 14, 14a,14b - octadecahydro-2H-picen-3-one

1.36

1.45

0.63

0.98

Table 5.8b. Seasonal variations in the quantity of Terpenes


Parameter N Mean Std.

Deviation t Statistical inference

Number of Terpenes

4 11.25 1.708 13.175 P<0.01 significant

quantity of Terpenes

4 59.2875

5.39480 21.980 P<0.01 significant

DF= 3

Table 5.8c. Karl Pearson correlation between season and Number and quantity

of the terpenes

Correlation value Statistical inference Parameters Numbers of

Terpenes Quantity of Terpenes

Numbers of Terpenes

Quantity of Terpenes

Heaviest Rain .793 .972* P>0.05 Not Significant

P<0.05 Significant

N= 4

95

5.3.1.1.8. Hydrocarbons

Large numbers of the long chain hydrocarbons constitute the chloroform

extract (Table 5.9a). There are about 17 hydrocarbons present in the chloroform

extract.

The numbers of hydrocarbons ranges between 8 (northeast monsoon and pre-

summer) to 14 (summer). The quantity is about 7.52% in pre-summer and 15.85% in

(southwest monsoon). In summer 14.10% of Hydrocarbons are present. The major

types of hydrocarbon are found arer Alkanes, Alkenes, Carbocyclic acids and Pthalic

acids. A trace amount of Bicyclo [4.2.0] oct-2-ene, 3,7-dimethyl-7-(4-methyl-3-

pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1.alpha.,6.alpha., 7.beta.,8.alpha

(1E,5E)]-; Octacosane17-Pentatriacontene; Nonadecane,1-chloro-; Z-12-Pentacosene

found only in the summer season. Heneicosane, 11-decyl-; 5-Ethylcyclopent-1-

enecarboxaldehyde are found in the southwest monsoon. 1, 2-Benzenedicarboxylic

acid, mono (2-ethylhexyl) ester is found only in the northeast monsoon. It may be due

to the favorable climate and soil conditions of the particular season.

The numbers and quantities of hydrocarbons exhibit statistically significant

variations among seasons (Table 5.9b).

The correlation analysis reveals that the numbers of hydrocarbons are

associated with mean R.H. evening and the lowest R.H.morning. The quantities of

hydrocarbons are linked with mean maximum and minimum temperatures and mean

R.H. morning (Table 5.9c). The numbers of hydrocarbons are associated with the

magnesium of the soil (Table 5.9d).

Table 5.9a. Hydrocarbons in seasons

S.No. Types of hydrocarbon S1 S2 S3 S4

Cyclic hydrocarbon

1.

Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7- (4-methyl-3-pentenyl)-8-(2,6,10-trimethyl -1, 5, 9-undecatrienyl)-, [1.alpha., 6.alpha., 7.beta.,8.alpha.(1E,5E)]-

- - 0.92 -

2. Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8- hexamethyl-,(1.alpha.,6.beta., 7.alpha.,9.alpha.)-

0.14 0.16 0.43 1.49

Alkanes 3. Tricosane 8.52 4.84 6.27 5.17 4. Nonacosane 1.97 1.21 1.53 1.88 5. Hexadecane 0.47 0.21 0.15 0.24 6. Tetradecane 0.41 0.16 0.15 0.21

96

7. Dodecane - 0.28 0.29 0.14 8. Z-14-Nonacosane - - 1.16 1.84 9. Octacosane - - 1.19 - 10. 17-Pentatriacontene - - 0.23 - 11. Heneicosane,11-decyl- - - - 0.89 Chlorinated 12. Nonadecane,1-chloro- - - 0.71 - 13 Heptacosane,1-chloro- 0.67 0.5 0.61 0.37 14 Z-12-Pentacosene - - 0.08 - 15 Heptacosane 0.67 0.5 0.61 0.37 Cyclopentanes 16 5-Ethylcyclopent-1-enecarboxaldehyde - - - 0.23 Carbocyclic acids pthalic acids

17 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl) ester 0.37 - - -

Table 5.9b. Seasonal variations in the Numbers and quantity of the Hydrocarbons


DF= 3

Table 5.9c. Karl Pearson correlation between season and numbers and

quantity of the Hydrocarbons


Hydrocarbons Quantity of Hydrocarbons

Numbers of Hydrocarbons

Quantity of Hydrocarbons

Mean max Temperature .828 .976 P>0.05 Not

significant P<0.05Significant

Mean Minimum temperature .686 1.000 P>0.05 Not

Significant P<0.01Significant

Mean R.H.morning -.633 -.991 P>0.05 Not Significant

P<0.01 Significant

Mean R.H. evening -.960 -.731 P<0.05 Significant

P>0.05 Not Significant

Lowest R.H. morning -.957 -.843 P<0.05

Significant P>0.05 Not Significant

N=4

Parameter N Mean Std. Deviation t Statistical inference

Numbers of Hydrocarbons 4 10.50 3.000 7.000 P<0.01 significant

Quantity of Hydrocarbons 4 12.57

00 3.58977 7.003 P<0.01 significant

97

Table 5.9d. Karl Pearson correlation between soil and numbers and quantity

of the Hydrocarbons


Hydrocarbons Quantity of Hydrocarbons

Numbers of Hydrocaron

Quantity of Hydrocarbon

Magnesium .951* -.470 P<0.05 Significant


N= 4

5.3.1.1.9. Fatty acids

The fatty acids are varying significantly among the seasons (Table 5.10a).

There are about 15 fatty acids identified in the chloroform extract. A maximum

number of 12 compounds are present in the summer samples. The lowest number of 7

compounds are present in the pre-summer and northeast monsoon seasons. Also, they

are varying in the peak area percentage. The descending order of the number of fatty

acids is as follows:-

12 (S3) > 9 (S1) > 7 (S2) = (S4).

In the quantity the descending order is

19.94 (S1) >12.56(S3)>S2 (12.24)>S4 (8.86).

Tetracosanoic acid; methyl ester; Hexadecanamide; Tridecanoic acid, 12-

methyl-methylester; 9-Octadecanoic acid, methylester,(E); Octadecanoic acid, methyl

ester are present only in the summer season.2,6,10,14,18,22-

tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E) is present only in the southwest

monsoon. 9, 12-Octadecadienoic acid (z, z) is present only in the northeast monsoon.

The total number and quantity of fatty acids exhibits distinct variations among

seasons (Table 5.10b). The correlation analysis with seasons shows that there is no

relationship between meteorological elements. The quantity is linked with the soil

parameter - electrical conductivity and the numbers are linked with the sulphur

content of the soil (Table 5.10c).

The fatty acids are the well known active metabolites. They serve as an

important energetic substrate for the cells. Linoleic acid is essential for the

maintenance of growth and α- linolenic acid for neural functions. Both acids were

shown to be potent cycloxygenase-2 (COX-2) catalyzed prostaglandin biosynthesis

inhibitors (Ringbom et al., 2001).

98

Table 5.10a. Fatty acids in seasons

S.No. Types of Fatty acids S1 S2 S3 S4

1. Tetracosanoic acid,methyl ester - - 0.07 - Unsaturated 2. 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 3.3 2.36 2.05 0.55 aturated 3. 9-Octadecenamide,(Z)- 4.42 2.51 2.47 3.99 Erucic acid 4. 13-Docosenamide,(Z)- 0.23 0.19 0.12 0.14 linoleic acids 5. 9,12-Octadecadienoic acid(z,z)- 0.91 - - - Palmitic acids 6. Hexadecanamide - - 0.37 - 7. n-Hexadecanoic acid 9.68 6.33 4.33 2.68 Esters, methyl 8. Hexadecanoic acid,methyl ester 0.17 0.19 1.3 - Myrsitic acids 9. Tetradecanoic acid 0.48 - 0.39 - Lauric acids 10. Dodecanamide 0.53 0.53 - 0.34 Stearic acids esters

11. Tridecanoic acid, 12-methyl-methylester - - 0.14 -

12. 9-Octadecanoic acid,methylester,(E)- - - 0.73 - 13. Octadecanoic acid,methyl ester - - 0.46 -

Omega-3 Derivatives Docosahexaenoic Acids

14. 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E)-

- - - 0.89

Polymeric fatty acids 15 9-Octadecyne 0.23 0.15 0.15 0.28

Table 5.10b. Seasonal variations in the Numbers and quantity of the Fatty acids


DF= 3


Numbers of Fatty acids 4 8.75 2.363 7.406 P<0.01 significant

Quantity of Fatty acids 4 13.40

00 4.67027 5.738 P<0.05 significant

99

Table 5.10c. Karl Pearson correlation between soil parameters and the fatty acids


fatty acids Quantity of fatty acids

Numbers of fatty acids

Quantity of fatty acids

EC 0.156 0.982* P>0.05 Not Significant

P< 0.05 Significant

Sulphur -.952* -007 P< 0.05 Significant


N= 4

5.3.1.1.10. Sterol composition

The next large numbers of compounds belongs to sterols (Table 5.11a). The

maximum numbers of compounds (8) is recorded in summer and other three seasons

are identical in the numbers of compounds (6). There are only four sterol compounds

present in the summer.

Quantitatively the order is S3 (13.02) > S1 (11.54)>S2 (11.12)>S4 (8.61).

The quality and quantity of sterols exhibit statistically significant seasonal

variations (Table 5.11b). The quality and quantity of sterols are not linked with

meteorological elements. The number is linked only with the sulphur content of the

soil (Table 5.11b).

Table 5.11a. Sterols in seasons

S.NO. TYPES OF STEROLCOMPOUND S1 S2 S3 S4

1. 9,19-Cyclolanost-24-en-3-ol,acetate 3.12 2.23 1.88 1.98 Phytosterols 2 Campesterol 1.7 2.06 2.57 1.57 3 Stigmasterol 1.21 1.1 1.73 1.09 4 gamma-Sitosterol - - 3.86 - 5 Stigmasterol,22,23-dihydro- 3.59 3.56 - 1.7

Cholesterols (Dehydrocholesterols)

6 Desmosterol - - 0.2 - Stigmasterols Analogs/Derivatives

7 Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)- - - 2.04 -

8 4,22-Stigmastadiene-3-one 1.69 0.89 - 1.68

Ergosterols Analogs/Derivatives (Withanolides)

9 Ergost-22-en-3-0l,(3.beta.,5.alpha.,22E,24R)- - - 0.32 -

10 Ergost-8,24(28)-dien-3-ol,4,14-dimethyl-,(3.beta.,4.alpha.,5.alpa.,)- 0.26 - 0.42 0.59

100

Steroids (Cholestenones)

11 Stigmastane-3, 6-dione, (5.alpha.) - 1.29 - -

Table 5.11b. Seasonal variations in the Numbers and quantity of the Sterols (One

sample t - test)

DF= 3

Table 5.11c. Karl Pearson correlation between soil and numbers and quantity of

the Sterols


Sterols Quantity of Sterols Numbers of Sterols Quantity of

Sterols

Sulphur -.991** -.793 P<0.05 significant P>0.05 Not significant

N=4

Sterols are important constituents of all eukaryotes. They play a vital role in

plant cell membranes. Plant sterols are physiologically very active; they are

precursors of many hormones and oviposition stimulants of some insects (Harborne,

2001).

Phytosterols such as stigmasterol and sitosterol are essential components of

cell membranes, and they are also used as the starting material in the production of

steroidal drugs. Phytosterols are characterised by a hydroxyl group attached at C-3

and an extra methyl or ethyl substituent in the side chain which are not present in the

animal sterols (Harborne and Baxter, 1993).

Phytosterols are minor but beneficial components of the human diet. Since

they inhibit growth of tumours and help in the regulation of blood cholesterol, they

are therapeutically important. Many herbs e.g. Withania somnifera (Pengelly, 2005);

Urtica dioica (Hirano et al., 1994) and Commiphora mukul (Bruneton, 1995) are rich

some of steroidal compounds.

The typical plant sterols, sitosterol and stigmasterol, appeared as main sterol

components (Nes and Parish, 1989). Sitosterol possesses antihyperlipoproteinaemic,

antibacterial and antimicotic activities and has been shown to act as inhibitor of tumor

cells (Yasukawa et al., 1991; Kasahara et al., 1994); cancer cells (Raicht et al., 1980).

Parameter N Mean Std. Deviation t Statistical inference Numbers of Sterols 4 6.50 1.000 13.000 P<0.01 significant Quantity of Sterols 4 11.0725 1.83280 12.083 P<0.05 significant

101

They also exhibit significant inhibitory effect on HIV reverse transcriptase (Akihisa et

al., 2001).A mixture of stigmasterol and sitosterol was shown to possess anti-

inflammatory activity upon tropical application (Gomez et al., 1999). They are used

for the treatment of prostate problems (Gomez et al., 1999).

5.3.1.1.11. Heterocyclic Compounds

There are three biologically important heterocyclic compounds identified

(Table 5.12a). These three compounds occur only in the northeast monsoon and the

summer. Quantitatively it can be arranged as S3 (4.18) >S2 (3.86) S1 & S4 (1.47).

The quality and quantity of these compounds displays statistically significant

seasonal variations (Table 5.12b). The quality of compounds shows significant

relationship with heaviest the rainfall and the quantity is linked with lowest R.H.

(Table 5.12c).

Table 5.12a. Heterocyclic compounds in the plants – seasonal study

S.No. Compound name S1 S2 S3 S4

Benzopyrans

1. Vitamin E 1.15 3.86 3.85 1.47

2. 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl- 0.08 - 0.06 -

Tocopherols

3. gamma-Tocopherol 0.25 - 0.26 -

Table 5.12b. Seasonal variations in the Numbers and quantity of the Heterocyclic

Compounds (One sample t - test)

DF = 3


Numbers of Heterocyclic Compounds

4 2.00 1.155 3.464 P<0.05 significant

Quantity of Heterocyclic Compounds

4 2.7450 1.47803 3.714 P<0.05 significant

102

Table 5.12c. Karl Pearson correlation between season and numbers and quantity

of the Heterocyclic Compounds


Parameters Numbers of Heterocyclic Compounds


Numbers of Heterocyclic Compounds


Lowest R.H. evening -.345 -.958* P>0.05 Not Significant

P<0.05 Significant

Total Rainfall -.933 -.403 P>0.05 Not Significant


Heaviest Rain -.993** -.022 P<0.01 Significant


N=4

5.3.1.1.12. Phenolics

Two phenolics are recorded through the GCMS analysis (Table 5.13a.). The 2-

Methoxy vinyl phenol is present in northeast monsoon and summer. The 2, 4-bis (1,

1-dimethyl) is not present in the southwest monsoon. The quantity varies as follows

S1 (0.59)>S3 (0.21)>S2 (0.12).The seasonal variations in the phenolics compounds

are statistically not significant (Table5.13b).

Table 5.13a. Phenolic compounds in seasons

S.No. Types of phenolics S1 S2 S3 S4

1 phenol, 2,4-bis(1,1-dimethylethyl) 0.24 0.12 0.11 -

Catechols

2 2-Methoxy-4-vinyl phenol 0.35 - 0.11 -

Table 5.13b. Seasonal variations in the Numbers and quantity of the Phenolics

Compounds (One sample t - test)

DF = 3


Numbers of Phenolics Compounds 4 1.25 .957 2.611 P>0.05 Not significant

Quantity of Phenolics Compounds 4 .2300 .25495 1.804 P>0.05 Not significant

103

Phenolics usually possess antimicrobial and antifungal activities and

consequently defensive in function. Aldehydes and ketones often act as

allelochemicals. Such activities are reported for all the three aldehydes identified in

the present study. Decanal is an attractant for some insects (Mattiacci et al., 2001;

Wang et al., 1999), while nonanal is a repellent (Huber and Borden, 2001; Wang et

al., 1999). Decanal has some pheromone- like activity (Cosse et al., 2002).

5.3.1.1.13. Hydroxylamine

Only one Hydroxylamine Oxime - Pyridine-3-Carboxamide, oxime, N-(2-

trifluromethyl phenyl - is identified in the southwest monsoon. The variations in the

number and quantity are statistically not significant (Table 5.14a).

Table 5.14a. Seasonal variations in the Numbers and quantity of the

Hydroxylamine Compounds (One sample t - test)

DF=3

5.3.1.2. Seasonal variations in the inorganic Compounds

5.3.1.2.1. Seasonal variations in the ash content

Total ash value of plant material represents the amount of minerals and earthy

materials attached. The presence of ash shows a significant difference between

seasons (figure 5.6 and Table 5.15a). The samples of southwest monsoon season (S4)

possesses highest ash content (1.79 %) and that of summer season (S3) possess the

lowest amount of ash (1.58%), Amin et al., (2007) have reported variation in the

percentage of ash matter, between dry season and green seasons in Sudan.

The ash content shows significant relationship with lowest R.H. evening and

the nitrogen, potassium and sulphur contents of the soil (Table 5.15b and 5.15c)


Numbers of Hydroxylamine 4 .25 .500 1.000 P>0.05 Not significant

Quantity of Hydroxylamine 4 .63 1.260 1.000 P>0.05 Not significant

104

1.69

1.621.58

1.79

1.451.5

1.551.6

1.651.7

1.751.8

1.85

S1 S2 S3 S4

Seasons

Ash

(%)

Figure 5.6. Presence of ash in seasons

Table 5.15a. Variations in ash content (One sample t - test)

parameter N Mean Std. Deviation T Statistical inference

Ash (%) 4 1.6700 .09201 36.299 P<0.01 significant

DF= 3

Table 5.15b. Karl Pearson correlation between season and variations in the ash content

Ash content vs. Correlation value Statistical inference

Lowest R.H. evening .966* P<0.05 Significant

N= 4

Table 5.15c. Karl Pearson correlation between soil and variations in the ash content

Ash content vs. Correlation value Statistical inference

Total Nitrogen -.989* P<0.05 Significant

Potassium -.968* P<0.05 Significant

Sulphur -.975* P<0.05 Significant

N= 4

5.3.1.2.2. Essential macro nutrients

105

The nitrogen, Phosphorous, potassium, calcium, magnesium, and sulphur

show significant seasonal variations (Table 5.16a).

Nitrogen is the second most important element of organic matter in plants.

Nitrogen content in the plants of red clover is relatively lower after the main crop

(spring barley) harvest; it reaches its peak at the start of the vegetation period in the

spring. In the subsequent stages of biomass growth, nitrogen content in red clover

becomes stable till the blooming is completed, (Brogowski, 2002). The nitrogen

(Figure 5.7a) is slightly high in the summer season (2.24%) and low in the pre-

summer (2.13%).

The phosphorous (figure 5.7b) and potassium (Figure 5.7c) are slightly higher

in the southwest monsoon (S4) and slightly lower in pre-summer season. Yoo et al.,

(1996) observed the seasonal variation in the nitrogen content. In winter the nitrogen

content was high in his study.

Phosphorus (H2PO4) content shows similar trends to that of nitrogen. Its

highest content occurs after the winter and further decreases very slowly till the stage

of blossom shed. This trend has been most likely due to carbohydrate loss as observed

along the winter time. The stems contain relatively low amounts of phosphates, richer

are leaves, and inflorescence are the richest (Brogowski, 2002).

The correlation analysis shows that the nitrogen is linked with lowest R.H.

morning and heaviest rainfall. The phosphorous is associated with highest R.H.

(morning and evening) and the wind speed. The potassium is associated with the

lowest temperature and the wind speed (Table 5.16b). The potassium and the

phosphorous are connected with the pH of the soil (Table 5.16c).

2.16

2.13

2.24

2.18

2.06

2.08

2.1

2.12

2.14

2.16

2.18

2.2

2.22

2.24

2.26

S1 S2 S3 S4Seasons

N I

t r o

g e

n (

% )

0.42 0.40.43

0.53

0

0.1

0.2

0.3

0.4

0.5

0.6

S1 S2 S3 S4Seasons

P h

o s

p h

o r

o u

s ( %

)

Figure 5.7a.Nitrogen Figure 5.7b. phosphorous

106

2.97

2.81

2.94

3.28

2.5

2.6

2.7

2.8

2.9

3

3.1

3.2

3.3

3.4

S1 S2 S3 S4Seasons

P o

t a s

s iu

m( %

)

Figure 5.7c. Potassium

Table 5.16a. Seasonal variations in nitrogen, phosphorous and potassium


parameter N Mean Std. Deviation T Statistical

inference Total Nitrogen (%) 4 2.1775 .04646 93.741 P<0.01

significant Total Phosphorous (%)

4 .4450 .05802 15.339 P<0.01 significant

Total Potassium (%) 4 3.0000 .19916 30.126 P<0.01

significant DF= 3

Table 5.16b. Karl Pearson correlation between seasonal elements and variations

in the nitrogen, phosphorous and potassium

Correlation value Statistical inference Parameters

Nitrogen phosphorous potassium Nitrogen phosphoro

us potassium

Lowest temperature .496 .949 .959* P>0.05 N.S P>0.05 N.S P<0.05

Significant Highest R.H. morning -.036 -.977* -.937 P>0.05 N.S P<0.05

Significant P>0.05 N.S

Highest R.H. evening -.125 -.987* -.944 P>0.05 N.S P<0.05


Lowest R.H. morning -.952* -.515 -.517 P<0.05

Significant P>0.05 N.S P>0.05 N.S

Heaviest Rain -.633 .293 .157 P<0.01

Significant P>0.05 N.S P>0.05 N.S

Mean Wind Speed .066 .981* .981* P>0.05 N.S P<0.05

Significant P<0.05 Significant

N= 4; N.S= Not significant

107

Table 5.16c. Karl Pearson correlation between soil and variations in the

nitrogen, phosphorous and potassium


Parameters Nitrogen

phospho

rous potassium Nitrogen phosphorous potassium

pH .215 988* .999** P>0.05 N.S P<0.05 Significant

P<0.05 Significant

N= 4; N.S= Not significant The other macronutrients, calcium, magnesium, and sulphur are depicted in

figures 5.7d to7.7f. The calcium is high in summer (4.84%) and low in the southwest

monsoon season (3.64%). where as the magnesium is high in the pre-summer season

(3.62%) and also low in southwest monsoon (2.59%). On the other hand the sulphur is

very high (0.98%) in southwest monsoon and low (0.47%) in summer. These

elements are significantly varying seasons to seasons (Table 5.16d).

The Calcium variation is subject to the highest R.H. (morning and evening)

and the wind speed. The calcium is associated with the nitrogen .The magnesium is

related to the lowest temperature and mean R.H. morning and the soil pH. The

sulphur is grately related to highest R.H. (morning and evening) and wind speed

(Table 5.16e and 5.16f).

4.59 4.62 4.84

3.64

0

1

2

3

4

5

6

S1 S2 S3 S4Seasons

C a

l c

iu m

( %

)

3.16

3.623.19

2.59

0

0.5

1

1.5

2

2.5

3

3.5

4

S1 S2 S3 S4Seasons

M a

g n

e s

iu m

( %

)

Figure 5.7d. Calcium Figure 5.7e. Magnesium

108

0.48 0.51 0.47

0.98

0

0.2

0.4

0.6

0.8

1

1.2

S1 S2 S3 S4Seasons

S u

l p h

u r

(%)

Figure 5.7f. Sulphur

Table 5.16d. Variations in the macro nutrients (One sample t - test)

parameter N Mean Std. Deviation t Statistical inference

Total Calcium (%) 4 4.4225 .53344 16.581 P<0.01 significant Total Magnesium (%) 4 3.1400 .42261 14.860 P<0.01 significant Total Sulphur (%) 4 .6100 .24725 4.934 P<0.05 significant

DF= 3

Table 5.16e. Karl Pearson correlation between season and variations in the

calcium, magnesium and sulphur


Calcium Magnesium Sulphur Calcium Magnesium Sulphur

Lowest temperature -.766 -.978* .822 P>0.05

N.S P<0.05 Significant

P>0.05 N.S

Mean R.H. morning .608 .957* -.648 P>0.05


P>0.05 N.S

Highest R.H. morning .978* .868 -.998** P<0.05


P<0.01 Significant

Highest R.H. evening .955* .885 -.989* P<0.05


P<0.05 Significant

Mean Wind Speed

-.967*

-.934

.968*

P<0.05 Significant

P>0.05 N.S

P<0.05 Significant

N=4; N.S. = Not significant

109

Table 5.16f. Karl Pearson correlation between soil and variations in the Calcium,

Magnesium and sulphur


Calcium Magnesium Sulphur Calcium Magnesium Sulphur

pH 915 -.977* .930 P>0.05 N.S

P<0.05 Significant

P>0.05 N.S

Total Nitrogen .977* .840 -.934 P<0.05 Significant

P>0.05 N.S

P>0.05 N.S

N=4; N.S. = Not significant

5.3.1.2.3 Essential micro nutrients

The significant impact was observed in the micronutrients. Zn, Cu, Fe, Mn,

Bo, and Mb are low in the southwest monsoon. Except Fe, Mn and Bo all the other

micronutrients are higher in summer. Fe, Mn and Bo are higher in pre-summer. The

contnent of zinc is 3.46 ppm (Figure 5.8a). The highest concentration of copper is

1.12 ppm. (Figure 5.8b). The (Figure 5.8c) highest iron concentration is 159.3 ppm

and the Mn (Figure 5.8d) is 26.34 ppm. The highest value of Bo is 0.13 ppm. (Figure

5.8e).The molybdenum (Figure 5.8f) is high in the S3 (0.13 ppm).

The seasonal variations are statistically significant (Table 5.17a and 5.17b).

The variation of zinc is not linked with any of the climataological elements. The

copper is associated with highest R.H. morning and mean wind speed. The iron is

subjected to the highest R.H. (morning and evening) and the mean wind speed (Table

5.17c).

The manganese and boron show significant relationship with the highest R.H.

(morning and evening) and the mean wind speed. The zinc and copper are associated

with the total nitrogen content of the soil. (Table 5.17d). The manganese is relevant to

the pH, total nitrogen. The molybdenum is associated with the total nitrogen and the

sodium present in the soil (Table 5.17e.)

110

3.19 3.29 3.46

2.84

0

0.5

1

1.5

2

2.5

3

3.5

4

S1 S2 S3 S4Seasons

Z in

c(p

p m

)

0.971.06

1.12

0.56

0

0.2

0.4

0.6

0.8

1

1.2

S1 S2 S3 S4Seasons

Cop

per(

ppm

)

Figure 5.8a. Zinc Figure 5.8b. Copper

156.3 159.3 157.2

110.2

0

20

40

60

80

100

120

140

160

180

S1 S2 S3 S4Seasons

Iron(

ppm

)

22.64

26.34 25.87

12.36

0

5

10

15

20

25

30

S1 S2 S3 S4Seasons

M a

n g

a n

e s

e (p

pm)

Figure 5.8c. Iron Figure 5.8d. Manganese

0.120.13

0.12

0.05

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

S1 S2 S3 S4

Seasons

Bor

on(p

pm)

0.1 0.1

0.13

0.05

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

S1 S2 S3 S4Seasons

M o

l y

b d

e n

u m

( p

p m

)

Figure 5.8e. Boron Figure 5.8f. Molybdenum

111

Table 5.17a. Variations in the micronutrients (One sample t - test)


Total Zinc (ppm) 4 3.1950 .26160 24.427 P<0.01 significant Total Copper (ppm) 4 .9275 .25264 7.343 P<0.01 significant Total Iron (ppm) 4 145.750 23.7333 12.282 P<0.01 significant Total Manganese (ppm) 4 21.8025 6.50630 6.702 P<0.01 significant Total Boron (ppm) 4 .2175 .18839 2.309 P>0.05 Not significant Total Molybdenum (ppm) 4 .095 .0332 5.729 P<0.05 significant

DF= 3

Table 5.17b. Karl Pearson correlation between season and variations in the zinc,

copper and Iron


Zinc Copper Iron Zinc Copper Iron Highest R.H. morning .905 .970* .999** P>0.05


P<0.01 Significant

Highest R.H. evening .864 .948 .995** P>0.05

N.S P>0.05 N.S

P<0.01 Significant

Mean Wind Speed -.927 -.983* -.988* P>0.05


P<0.05 Significant


Table 5.17c. Karl Pearson correlation between season and variations in the

manganese, boron and molybdenum


Manganese Boron molybdenum Manganese Boron molybden

um Highest R.H. morning .968* -1.000** .905 P<0.05

Significant P<0.01 Significant P>0.05 N.S

Highest R.H. evening .954* -.994** .861 P<0.05

Significant P<0.01 Significant P>0.05 N.S

Mean Wind Speed -.997** .974* -.895 P<0.01 Significant

P<0.05 Significant P>0.05 N.S


Table 5.17d. Karl Pearson correlation between soil and variations in the zinc,

copper and Iron


Zinc copper iron Zinc copper iron

Total nitrogen .992** .993** 939 P<0.05 Significant

P<0.01 Significant

P>0.05 N.S


112

Table 5.17e. Karl Pearson correlation between soil and variations in the,

Manganese, Boron and the Molybdenum

Correlation value Statistical inference Parameters Mangane

se Boron Molybdenum Manganese Boron Molybdenu

m

pH -.976* .944 -.814 P<0.05 Significant P>0.05 N.S P>0.05 N.S

Total nitrogen .981* -.932 .967* P<0.05 Significant P>0.05 N.S P<0.05

Significant

sodium .877 -.803 .966* P>0.05 N.S P>0.05 N.S P<0.05 Significant


5.3.1.2.4. Seasonal variations in the non essential element

The chromium (Figure 5.9a) shows meager variations among the seasonal

samples. The highest value of the chromium is 0.005 mg/g (southwest monsoon and

the lowest value is recorded in the pre-summer (0.1 mg/g). the difference is 0.4 mg/g.

Nickel is high (0.04 mg/g) in the southwest monsoon and low in the northeast

monsoon and pre-summer (0.01 mg/g) (Figure 5.9b).

The cadmium is high (0.05 mg/g) in northeast monsoon and low in summer

and southwest monsoon seasons (0.03 mg/g) (Figure 5.9c). The lead is high (0.16

mg/g) in the southwest monsoon and low (0.12 mg/g) in northeast monsoon and

summer seasons (Figure 5.9d).

Except northeast monsoon (0.2 mg/g) all other seasons possessed the equal

quantities of cobalt (Figure 5.9e). In the content of mercury (Figure 5.9f) there is

meager difference found. In the pre-summer and southwest monsoons the mercury is

0.002 mg/g and in the northeast monsoon it is 0.001 (mg/g).

The Silver (Figure 5.9g) is ranging between 0.05 (northeast monsoon and

summer) to 0.06 mg/g (pre-summer and southwest monsoon). In all the seasons

selenium (Figure 5.9h) is found to be higher. Among these the highest concentration

is recorded in the summer and the lowest concentration (0.57 mg/g) is recorded in the

southwest monsoon. The difference is 0.08g. The silver concentration is high (0.06

mg/g) in the pre-summer and the southwest monsoon and low in the northeast

monsoon and the summer seasons. Arsenic and Cyanide are not present at all.

113

0.002

0.001

0.002

0.005

0

0.001

0.002

0.003

0.004

0.005

0.006

S1 S2 S3 S4

Chr

omiu

m (

mg/

g

)

0.01 0.01

0.02

0.04

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

S1 S2 S3 S4

Nic

kel (

mg/

g )

Except chromium, nickel and the other analysed non essential elements

cadmium, lead, cobalt, mercury, silver and selenium show significant seasonal

variations (Table 5.18a).

The lead is highly correlated with the R.H (morning and evening). Cobalt is

highly subjected to the number of rainy days. Mercury is related to the heaviest

rainfall. Silver is linked to the R.H (morning and evening) and heaviest rainfall (Table

5.18c). The variations in the cadmium and selenium are not subjected to the

meterological elements (Table 5.18b and d).

Selenium (Se) is an essential micronutrient for many organisms, including

plants, animals and humans. The concentration of Se in plant varies between areas

(Zhu et al., 2009). Too much Se can lead to toxicity and the low level causes

deficiency. Worldwide, interest in the biological impacts of Se on the environment

and food chains is increasing because it is an essential micronutrient for many

organisms, including humans and other animals (although it is toxic at higher

concentrations) ( Terry et al., 2000). It is also a beneficial nutrient for many plants,

including higher plant taxa (Pilon Smits et al., 2009; Lyons et al., 2009).

In organisms that require Se, selenocysteine (Se Cys) is an essential

component in the so-called selenoproteins or selenoenzymes (Rayman 2002), 25 of

which have been identified in humans (Kryukov et al., 2003; Lu and Holmgren,

2009). Selenoproteins have a redox function involved in free-radical scavenging (Lu

and Holmgren, 2009), and several studies have shown that improving Se status can

lower the risk of cancer (Clark et al., 1996; Wallace et al., 2009). Se speciation varies

with plant species and the form of Se fed to the plant (Reid et al., ,2008; Sors et al.,

2005; Ximenez-Embun et al., 2004; Kapolna and fodder, 2006; Kapolna et al., 2007;

Grant et al., 2004). It is well documented that inter- and intraspecific variation in Se

accumulation in plants exists (Zhu et al., 2009).

Figure 5.9a. Chromium Figure 5.9b.Nickel

114

0.05

0.04

0.03 0.03

0

0.01

0.02

0.03

0.04

0.05

0.06

S1 S2 S3 S4

Cad

miu

m (

mg

/ g)

0.120.13

0.12

0.16

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

S1 S2 S3 S4

Lea

d (m

g / g

)

Figure 5.9c. Cadmium Figure 5.9d. Lead

0.02

0.03 0.03 0.03

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

S1 S2 S3 S4

Cob

alt (

mg

/ g )

0.001

0.002

0.001

0.002

0

0.0005

0.001

0.0015

0.002

0.0025

S1 S2 S3 S4

Mer

cury

(mg

/ g )

Figure 5.9e. Cobalt Figure 5.9f. Mercury

0.05

0.06

0.05

0.06

0.044

0.046

0.048

0.05

0.052

0.054

0.056

0.058

0.06

0.062

S1 S2 S3 S4

Silv

er (m

g /g

)

0.620.63

0.65

0.57

0.52

0.54

0.56

0.58

0.6

0.62

0.64

0.66

S1 S2 S3 S4

Sel

eniu

m (m

g /g

)

Figure 5.9g. Silver Figure 5.9h. Selenium

Table 5.18a. Variations in the nonessential elements (One sample t - test)

parameter N Mean Std. Deviation t Statistical inference Total chromium 4 .00250 .001732 2.887 P>0.05 Not significant Total Nickel 4 .0200 .01414 2.828 P>0.05 Not significant Total Cadmium 4 .0375 .00957 7.833 P<0.01 significant Total lead 4 .1325 .01893 13.999 P<0.01 significant Total Cobalt 4 .0275 .00500 11.000 P<0.01 significant Total Mercury 4 .00150 .000577 5.196 P<0.05significant Total Silver 4 .0550 .00577 19.053 P<0.01significant Total Selenium 4 .6175 .03403 36.287 P<0.01significant

DF= 3

115

Table 5.18b. Karl Pearson correlation between season and variations in

cadmium, lead and cobalt


cadmium lead cobalt cadmium lead cobalt

Highest R.H. morning .522 -.968* -.333 P>0.05 N.S P<0.05


Highest R.H. evening .588 -.953* -.375 P>0.05 N.S P<0.05


Numbers of Rainy Days .870 -.440 -

1.000** P>0.05 N.S P>0.05 N.S P<0.01 Significant

N=4; N.S= not significant

Table 5.18c. Karl Pearson correlation between season and variations in mercury,

silver and selenium

Correlation value Statistical inference Parameters mercury silver selenium mercury silver selenium

Highest R.H. morning -.577 -.577 .930 P>0.05


P>0.05 N.S

Highest R.H. evening -.537 -.537 .894 P>0.05


P>0.05 N.S

Heaviest Rainfall .993** .993** -.527 P<0.01

Significant P<0.01 Significant

P>0.05 N.S

N=4; N.S= not significant

Table 5.18d.Karl Pearson correlation between soil and variations in cadmium,

lead and cobalt


cadmium lead cobalt cadmium Lead cobalt

EC .900 -.585 -.985* P>0.05 N.S P>0.05 N.S P<0.05 Significant

Manganese .525 -.970* -.576 P>0.05 N.S P<0.05 Significant P>0.05 N.S


5.3.1.3 Discussion

The seasonal carbohydrate cycles are particularly well defined in many

deciduous trees of the temperate zone. Total carbohydrate contents of stems and

branches reach a maximum in autumn at the time of leaf fall, begin to decrease in late

winter, and decrease rapidly in early spring when carbohydrates are being depleted by

accelerated respiration and used in growth of new tissues (Kramer and Kozlowski,

1979).Seasonal changes in carbohydrate concentrations have been reported is many

116

plants (Bonicel et al., 1987; Ashworth et al., 1993; Rinne et al., 1994; Barbarox and

Breda, 2002; Bhowmik and Matsui, 2003).

The lowest nitrogen contents of branchlets at the different development stages

occurred in summer, when the growth is active in Cassuarina equisetifolia (Zhang et

al., 2009). A portion of N was allocated to other portions (e.g., roots and flowers); for

example, the peak of the flowering period appears from April to June for C.

equisetifolia (Morton, 1980). Similarly, Aerts et al., 1999) suggested that summer

warming reduced N contents of mature and senescent leaves in Rubus.

Second, N contents was diluted by branchlet’s mass accumulation during

summer when C. equisetifolia grew rapidly. Changes in leaf N contents has direct

impact on the photosynthetic capacity of the species involved, as there is usually a

direct relation between leaf N content and the maximum rate of photosynthesis

(Lambers et al., 1998). This may be true of Calotropis also.

It is common to find a negative correlation between N and secondary

compound contents, such as phenolics and tannins (Horner et al., 1987; Mansfield et

al., 1999).The carbon nutrient balance (CNB) hypothesis postulates that phenolic

levels in plants are determined by the balance between carbon and other nutrient

availability (Bryant et al., 1983). The growth-differentiation balance (GDB)

hypothesis (Loomis, 1932; Lorio, 1986; Herms and Mattson, 1992) considers factors

that limit growth and differentiation (the sum of chemical and morphological changes

that occur in maturing cells, including carbon-based secondary synthesis) also limit N.

The production of phenolics dominates when factors other than photosynthate supply

are suboptimal for growth.

This pattern tend to support source-sink hypotheses, such as the carbon

nutrient balance hypothesis (Bryant et al., 1983) and the growth-differentiation

balance hypothesis (Herms and Mattson, 1992) that predict increased C allocation to

secondary C compounds under low nutrient conditions. In this study at summer the

nitrogen concentration is high. At the time of increased nitrogen the hydrocarbons, the

fatty acids and sterols also tend to be at the higher side.

Several researchers have noted pronounced seasonal variation in the elemental

concentration in plants elsewhere in the world, with the largest trace metal

concentrations generally occurring in the springtime (Ashton & Riese 1989, Stednick

et al. 1987).

117

Leaves and twigs from various shrub species were evaluated for comparative

seasonal contents of Ca, Mg, Na, K, P, Cu, Fe, Mn and Zn. Plants were collected in

summer, autumn, winter and spring in the counties: Linares, Santiago, Iturbide and

Montemorelos belonging to the state of Nuevo Leon, Mexico. During summer,

mineral concentrations were higher in general. Only Ca, Mg, K, Mn and Fe were in

substantial amounts (Ram´ırez et al., 2006).

The seasonal variation of the essential oil extracts from the aerial parts of a

Santolina rosmarinifolia population has been studied in detail by Paul et al., (2001).

He found that the oil yields increased in the months of March, April, May and June.

Oil concentration showed significant correlations with both precipitation (positive)

and temperature (negative). The main components of the essential oil showed a

significant negative correlation with temperature, while capillene offered a strong

positive correlation with precipitation. The rest of the essential oil components did not

show any noticeable trend.

Faini et al., (1999) observe that the production of epicuticular waxes to be at a

minimum level in winter when the relative percentage of less polar compounds

(waxes and hydrophobic solids) increases in comparison to the other seasons. A high

production of epicuticular waxes in Summer, when drought and solar radiation is

highest, suggests that the polar solids (triterpenes, flavonoids, diterpene acids) present

in high concentrations might act as a physical barrier to prevent water permeation and

dehydration of the leaves and filtering or reflecting of incident light (Harborne et al.,

1975). By contrast, during the winter season, production of waxes decreases as a

result of the low vegetative activity and changes in chemical composition could then

be explained in terms of a thermoregulatory action of the hydrophobic compounds

which would also protect the plant from desiccation due to the wind. Costa et al.,

(2009) found a clear seasonal influence on the essential oil compositions. Variations

of the Camptothecin content of Nothapodytes nimmoniana according to the seasons

and locations are well documented (Namdeo et al., 2010).

The overall seasonal variation in the macro and micro nutrients and the

secondary metabolites shows that some prominent differences occur between seasons.

The organic carbon (2.75%), carbohydrate (1.02%), protein (0.62%), lipids (0.3%),

ash content (1.79%) phosphorous (0.53%), potassium (3.28%), sulphur (0.98%) and

lead (0.16mg/g) are higher in southwest monsoon. At the same time the calcium

(3.64%), magnesium (2.59%), zinc (2.84 ppm), copper (0.56 ppm), iron (110.2ppm),

118

manganese (12.36ppm), boron (0.05ppm), molybdenum (0.05ppm) and selenium

(0.57mg/g) are found to be low in the southwest monsoon. The meteorological

elements such as mean maximum temperature (36.3 °C) and rain fall (384 mm) and

mean wind speed (11.7kmph) are high in this season. The mean R.H morning (67.7%)

and evening (46.3 %) are comparatively low in this season. The variation in this

analysed parameters may be due to the mean maximum and minimum temperature,

Highest temperature, highest and lowest R.H. (morning and evening), mean R.H.

(morning and evening), heaviest rainfall, numbers of rainy days, heaviest rainfall and

mean wind speed.

5.3.2. Locational variations

5.3.2.1. Locational variations in the organic compounds

5.3.2.1.1. Organic carbon

The quantity of organic carbon is high (27.5%) in the coastal tract (L1) and the

terrestrial-rural stretch (L4). In the riverine zone (L3) it is meager (26.2 %) (Figure

5.10). The organic carbon shows statistically significant variations among locations

(Table 5.19). The organic carbon shows no correlation with meteorological and soil

parameters.

27.5

26.5

26.2

27.5

26.4

25.4

25.6

25.826

26.2

26.4

26.6

26.8

27

27.2

27.4

27.6

Org

anic

car

bon

(%)

L1 L2 L3 L4 L5

Figure 5.10. Organic carbon in the plant (locational study)

119

Table 5.19. Locational variations in the organic carbon (One sample t - test)


Organic Carbon (%) 5 26.82 .6301 95.18 P<0.01 significant

DF = 4

5.3.2.1.2. Locational variations in carbohydrate, protein and lipid

Statistically significant variations are noticed in the quantity of

macromolecules (carbohydrate, protein and lipid) among the locations studied (Table

5.20). The maximum amount of carbohydrate (10.6%) is present in the coastal tract

(L1) and the lowest level (10.2%) is present in the terrestrial-rural stretch (L4).

The protein is also slightly higher (6.9 %) in the coastal tract (L1) and low

(5.8%) in riverine zone (L3). The lipid is slightly higher (3.3%) in the hilly terrain

(L2) and low (2.8%) in the terrestrial -urban area (L5) (Figure 5.11).

The locational variations in the carbohydrate, protein, and lipid show no

relationship with the meteorological elements and the soil parameters.

10.6

6.9

2.9

10.3

6.4

3.3

10.5

5.8

3.1

10.2

6.2

3

10.3

6.4

2.8

0

2

4

6

8

10

12

( % )

1 2 3 4 5

Carbohydrate

Proteins

Lipids

Figure 5.11. Carbohydrate, protein and lipid in the plant- locational study

Table 5.20. Locational variations in the carbohydrate, protein and lipid



Carbohydrate 5 10.380 0.1643 141.254 P<0.01 significant

Protein 5 6.340 0.3975 35.665 P<0.01 significant

Lipid 5 3.020 0.1924 35.107 P<0.01 significant

DF = 4

120

110.235 109.925

105.485 105.56 105.225

102

103

104

105

106

107

108

109

110

111

L1 L2 L3 L4 L5

5.3.2.1.3. Locational variations in the energy content

The energy values of the plant samples are varying significantly in accordance

with the macro molecules (Table.5.21). The range is 105.225 calories/ 100 g (L5-

terrestrial-urban area) to 110.235 calories/ 100 g (L1- coastal tract) (Figure 5.12).

Figure 5.12. Locational variations in the energy content

The locational variation in the energy content is highly correlated with the

meteorological elements (Table 5.22). The energy content of the plant sample shows

no relationship with the soil.

Table 5.21. Locational variations in the energy value


Calorific value 5 9.4060 .20864 100.808 P<0.01 significant

DF = 4

Table 5.22. Karl Pearson correlation between meteorological elements and calorific value


Seasonal lowest temperature .980** P<0.01 Significant

Monthly lowest temperature .917* P<0.05 Significant.

Annual highest R.H. evening -.932* P<0.05 Significant

Annual lowest R.H. morning -.927* P<0.05 Significant

Annual lowest R.H. evening .982** P<0.01 Significant

Annual heaviest rainfall -.977** P<0.01 Significant

Seasonal heaviest rainfall -.963** P<0.01 Significant

Monthly heaviest rainfall -941* P<0.05 Significant

Annual numbers of rainy days 972** P<0.01 Significant

N=4

121

5.3.2.1.4. Locational variations in the yield of extract

The extract yield potential of the plant varies significantly from location to

location (Figures 5.13). The plant collected at the coastal tract provided the maximum

quantity of the extract (6.9%) and the terrestrial urban area sample yielded the lowest

amount (4 %). This variation is statistically significant (Table 5.23)

The yield of extracts is not correlated with any of the meteorological or soil

parameters.

6.95 6.7

5.4

6.9

4

0

1

2

3

4

5

6

7

8

L1 L2 L3 L4 L5

Ext

ract

yie

ld (

%)

Figure 5.13. Yield of extracts in locations

Table 5.23. Locational variations in the yield of extract

Parameter N Mean Std. Deviation

t Statistical Inference

Yield of extract 5 5.990 1.28082 10.457 P<0.01 significant

DF = 4

5.3.2.1.5. Compounds present in the chloroform extract

The GC-MS analysis of the chloroform extract of the Calotropis which is

collected from various locations has been identified to contain a complex mixture of

64 compounds (Figure 5.14 – 5.14e). They are varying from 39 (L2) to 43(L1)

compounds (Table 5.25). As per the numbers of compounds isolated the location can

be arranged as L5 (38) > L4 (40)> L1 (41) > L2 and L3 (42). The variations in the

numbers of compounds are statistically significant (Table 5.24 a)

Out of these 64 compounds 31 compounds possesses significant variations

among locations (2-Methoxy-4-vinyl phenol; Hexadecane; Tridecanoic acid, 12-

methyl-methylester; Tetradecanoic acid ; 9-Octadecyne ; n-Hexadecanoic acid ; 9-

Octadecanoic acid,methylester,(E)- ; Octacosane; A'-Neogammacer-22(29)-en-3-

122

ol,acetate,(3beta,21beta)-; 9-Octadecenamide,(Z)-; Lup-20(29)-en-3-ol,

acetate,(3beta)-; Z-12-Pentacosene; Heptacosane; Tetracosanoic acid,methyl ester;

13-Docosenamide,(Z)-; Squalene; Nonacosane; Bicyclo[4.2.0]oct-2-ene, 3,7-

dimethyl-7-(4-methyl-3-pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1alpha,

6alpha, 7alpha,8alpha(1E,5E)]-;2,6,10,14,18,22-tetracosahexanone,2,6,10,15, 9, 23-

hexamethyl- ,(all-E)- ; Heptacosane,1-chloro-; gamma-Tocopherol ; 17-

Pentatriacontene; 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a-dimethyl-6-(1-

methylethenyl)-; Desmosterol; gamma-Sitosterol; Pyridine-3-Carboxamide,oxime,N-

(2-trifluromethyl phenyl)- ; alpha-Amyrin ; ,4,6a,6b,8a,11,12,14b-Octamethyl-

1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a, 4b -octadecahydro-2H-picen-3-one ;

.alpha-Amyrin;12-Oleanen-3-yl acetate, (3alpha)-;9,19-Cyclolanost-24-en-3-

ol,acetate ;Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)) (Table 5.24b ) .

The total numbers of compounds are interconnected to some of the

meteorological parameters (annual lowest temperature, seasonal and monthly mean

R.H morning, seasonal lowest R.H (morning and evening), monthly lowest R.H.

evening, seasonal and monthly total rainfall, and the numbers of rainy days) and the

soil parameters (Total nitrogen and the iron content) (Table 5.24c and 5.24d).

41

42 42

40

38

36

37

38

39

40

41

42

43

L1 L2 L3 L4 L5

Figure 5.14. Numbers of compounds identified in locational samples

123

GC-MS Chromotogram for L1 GC-MS Chromotogram for L2

GC-MS Chromotogram for L3 GC-MS Chromotogram for L4

Figure 5.14a. GC MS chromatogram for locational study (L1 to L4)

124

GC-MS Chromotogram for L5

Figure 5.14b. GC - MS chromatogram for locational study (L5)

125

Table 5.25. Compounds identified in the chloroform extract

S.No. Rt. Compound name L1 L2 L3 L4 L5 1 7.9 Dodecane 0.43 0.57 - 0.14 - 2 9.1 2-Methoxy-4-vinyl phenol - 0.09 - - - 3 9.7 Tetradecane 0.19 0.11 0.14 0.21 0.19 4 10.1 Caryophyllene 0.15 - - - -

5 11.1 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl- - - - - 0.12

6 11.5 Hexadecane 0.31 - - 0.24 - 7 13.1 Tetradecanoic acid 0.26 0.37 0.44 - 0.49 8 13.5 5-Ethylcyclopent-1-enecarboxaldehyde 0.18 0.19 0.11 0.23 0.18

9 13.8 Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1.alpha.,2.beta.,5.alpha.) 0.23 0.43 0.47 0.28 1.09

10 14.2 9-Octadecyne 0.15 0.62 0.28 0.28 1.16 11 14.8 Hexadecanoic acid,methyl ester 0.15 0.23 - - - 12 15.3 n-Hexadecanoic acid 4.95 6.57 4.34 2.68 6.76 13 16.7 9-Octadecanoic acid,methylester,(E)- - 0.48 - - 0.35 14 16.8 Phytol 1.45 0.76 0.94 0.56 1.79 15 17.3 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 2.58 4.37 1.58 0.55 3.42 16 17.5 Olean-12-ene,3-methoxy-,(3.beta.)- 0.66 - 0.67 - - 17 17.5 2-Methyl-Z,Z-3,13-octadecadienol - - - - 0.68 18 17.7 Dodecanamide 1.27 - 0.46 0.34 - 19 17.8 Hexadecanamide - 0.76 0.51 - 0.44 20 18.7 12-Oleanen-3-yl acetate, (3.alpha.)- - - 1.47 - - 21 19.2 Octacosane 1.86 1.35 0.71 - 0.5

22 19.5 A'-Neogammacer-22(29)-en-3-ol,acetate,(3.beta.,21.beta.)- 1.19 - 1.09 1.55 1.24

23 20 9-Octadecenamide,(Z)- 3.08 2.78 2.31 3.99 4.24 24 20.4 Urs-20-en-3-ol,(3.beta.,18.alpha.,19.alpha.)- 0.96 - - 1.09 - 25 20.6 Lup-20(29)-en-3-ol,acetate,(3.beta.)- - 2.9 - 2.1 - 26 21.6 Hop-22(29)-en-3.beta.-ol 8.13 9.01 11.67 9.67 14.55

27 22.3 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl)ester - - 0.21 - -

28 23 Oxirane,2,2-dimethyl-3-(3,7,12,16,20-pentamethyl-3,7,11,15,19-Heneicosapentaenyl)-,(all-E)-

- 0.25 - - -

29 23.1 11,13-Dimethyl-12-tetradecen-1-ol acetate - - 0.71 - - 30 23.2 Nonadecane,1-chloro- - 1.06 0.82 - - 31 24.2 Z-12-Pentacosene 0.73 0.19 0.09 - 0.19 32 24.6 Heptacosane 0.33 0.62 0.44 0.17 0.59 33 25.6 13-Docosenamide,(Z)- 0.08 0.21 - 0.14 0.18 34 26 Squalene 0.9 1.24 1.45 0.97 2.14 35 26.9 Z-14-Nonacosane - 1.98 - 1.84 2.69 36 27.2 Nonacosane 2.37 1.96 6.37 1.88 2.27

37 27.4 Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8-hexamethyl-,(1.alpha.,6.beta., 7.alpha.,9.alpha.)- 0.29 - - 1.49 0.49

38 27.6 Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7-(4-methyl-3-pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-

1.09 1.33 2.4 - 0.87

126

,[1.alpha.,6.alpha.,7.beta.,8.alpha.(1E,5E)]-

39 28.1 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E)- - - - 0.89 -

40 28.6 Heptacosane,1-chloro- 0.43 0.43 0.77 0.37 0.47 41 29.4 gamma-Tocopherol 0.23 0.16 0.32 - 0.25 42 30 17-Pentatriacontene 0.13 0.37 0.34 - 0.25 43 30.4 Tricosane 4.63 4.81 6.18 5.17 5.35 44 30.8 Vitamin E 2.42 1.53 2.02 1.47 2.8 45 30.9 Olean-12-ene 1.79 - - - -

46 31 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a-dimethyl-6-(1-methylethenyl)- - 4.04 - 3.25 -

47 32.6 Campesterol 1.78 1.91 2.16 1.57 1.83 48 33.2 Stigmasterol 1.06 1.79 1.35 1.09 1.4

49 34.1 Ergost-8,24(28)-dien-3-ol,4,14-dimethyl-,(3.beta.,4.alpha.,5.alpa.,)- 0.56 - - 0.59 -

50 34.7 Stigmasterol,22,23-dihydro- - - - 1.7 1.74 51 34.8 gamma-Sitosterol 2.76 3.35 2.14 - - 52 34.9 Heneicosane,11-decyl- - - 1.21 0.89 0.92

53 35.1 Pyridine-3-Carboxamide,oxime,N-(2-trifluromethyl phenyl)- - 1.47 1.8 2.53 0.98

54 35.2 Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)- 1.71 - - - - 56 35.7 .beta.-Amyrin 3.78 3.17 6.61 4.2 4.31

57 36 4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a,14b-octadecahydro-2H-picen-3-one

0.49 0.67 - 0.98 -

58 36.5 4,22-Stigmastadiene-3-one - 1.37 1.38 1.68 2.33 59 37 .alpha-Amyrin 12.36 8.8 12.65 13.67 11.7660 37.8 9,19-Cyclolanost-24-en-3-ol,acetate,(3.beta.)- - - 0.67 - - 61 38.4 12-Oleanen-3-yl acetate, (3.alpha.)- 12.75 4.37 7.55 10.16 7 62 38.7 9,19-Cyclolanost-24-en-3-ol,acetate 2.18 0.81 1.5 1.98 1.37 63 39.9 Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 16.97 18.95 9.84 15.11 9.24 64 40.2 Taraxasterol - 1.15 1.83 2.3 1.38

Table 5.25a. Variations in the numbers of compounds identified in locations

through GC-MS (One sample t - test)


Total Numbers of compounds 5 41.00 1.581 57.983 P<0.01

significant DF=4

Table 5.25b. Locational variation in the composition of chloroform extract (One

sample t - test)

S.No. Parameter N Mean Std. Deviation t Significance

1 Dodecane 5 .2280 .25956 1.964 P>0.05Not significant

127

2 2-Methoxy-4-vinyl phenol 5 .02 .040 1.000 P<0.05significant

3 Tetradecane 5 .1680 .04147 9.058 P>0.05Not significant

4 phenol, 2,4-bis(1,1-dimethylethyl) 5 .0300 .06708 1.000

P>0.05Not significant

5 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl- 5 .02 .054 1.000


6 Hexadecane 5 .1100 .15264 1.611 P<0.05 significant.

7 Tridecanoic acid, 12-methyl-methylester 5 .3120 .19460 3.585

P<0.05 significant.

8 Tetradecanoic acid 5 .1780 .04324 9.204 P<0.05 significant.

9 5-Ethylcyclopent-1-enecarboxaldehyde 5 .5000 .34467 3.244


10 Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1alpha,2alpha,5alpha) 5 .4980 .40905 2.722


11 9-Octadecyne 5 .0760 .10784 1.576 P<0.05 significant

12 Hexadecanoic acid,methyl ester 5 5.060 1.68560 6.712 P>0.05 Not significant

13 n-Hexadecanoic acid 5 .17 .232 1.601 P<0.05 significant

14 9-Octadecanoic acid,methylester,(E)- 5 1.100 .50779 4.844

P<0.05 significant

15 Phytol 5 2.500 1.49988 3.727 P>0.05 Not significant

16 Octadecanoic acid,methyl ester 5 .2660 .36425 1.633 P>0.05 Not significant

17 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 5 .14 .304 1.000

P>0.05 Not significant

18 9,12-Octadecadienoic acid(z,z)- 5 .4140 .52037 1.779 P>0.05 Not significant

19 Dodecanamide 5 .34 .334 2.289 P>0.05 Not significant

20 Hexadecanamide 5 .29 .657 1.000 P>0.05 Not significant

21 Octacosane 5 .8840 .72954 2.709 P<0.05 significant

22 A'-Neogammacer-22(29)-en-3-ol,acetate,(3beta,21beta)- 5 1.014 .59231 3.828

P<0.05 significant

23 9-Octadecenamide,(Z)- 5 3.280 .81495 9.000 P<0.05 significant

24 Urs-20-en-3-ol,(3alpha,18alpha,19alpha)- 5 .4100 .56329 1.628


25 Lup-20(29)-en-3-ol, acetate,(3beta)- 5 1.00 1.398 1.599

P<0.05 significant

26 Hop-22(29)-en-3alpha-ol 5 10.61 2.56130 9.259 P>0.05 Not significant

27 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl)ester 5 .04 .094 1.000


28 Nonadecane,1-chloro- 5 .05 .112 1.000 P>0.05Not

128

significant

29 Ergost-22-en-3-0l,(3alpha,5alpha,22E,24R)- 5 .14 .318 1.000


30 Stigmastane-3,6-dione,(5alpha) 5 .38 .522 1.611 P>0.05Not significant

31 Z-12-Pentacosene 5 .2400 .28513 1.882 P<0.05 significant

32 Heptacosane 5 .4300 .18668 5.151 P<0.05 significant

33 Tetracosanoic acid,methyl ester 5 .00 .000a 3.256 P<0.05 significant

34 13-Docosenamide,(Z)- 5 .1220 .08379 6.015 P<0.01 significant

35 Squalene 5 1.340 .4981 2.364 P<0.05 significant

36 Z-14-Nonacosane 5 1.30 1.231 3.474 P>0.05 Not significant

37 Nonacosane 5 2.970 1.91169 1.650 P<0.01 significant

38 Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8-hexamethyl-,(1alpha,6beta, 7alpha,9alpha)-

5 .4540 .61517 2.939 P>0.05 Not significant

39

Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7-(4-methyl-3-pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1alpha,6alpha,7alpha,8alpha(1E,5E)]-

5 1.138 .86583 1.000 P<0.05 significant

40 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E)-

5 .18 .398 6.975 P<0.05 significant

41 Heptacosane,1-chloro- 5 .4940 .15837 3.533 P<0.01 significant

42 gamma-Tocopherol 5 .1920 .12153 3.175 P<0.05 significant

43 17-Pentatriacontene 5 .2180 .15353 19.370 P<0.05 significant

44 Tricosane 5 5.228 .60351 8.011 P>0.05 Not significant

45 Vitamin E 5 2.048 .57164 1.000 P>0.05 Not significant

46 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a-dimethyl-6-(1-methylethenyl)-

5 .3580 .80051 1.617 P<0.05 significant

47 Desmosterol 5 1.46 2.016 19.319 P<0.05 significant

48 Campesterol 5 1.8500

.21413 10.157 P>0.05 Not significant

49 Stigmasterol 5 1.3380

.29457 1.632 P>0.05 Not significant

50 Ergost-8,24(28)-dien-3-ol,4,14-dimethyl- 5 .00 .000a 1.633


129

,(3alpha,4alpha,5.alpa.,)-

51 Stigmasterol,22,23-dihydro- 5 .2300 .31512 2.356 P>0.05 Not significant

52 gamma-Sitosterol 5 .69 .942 2.389 P<0.05 significant

53 Heneicosane,11-decyl- 5 1.6500

1.56582 3.210 P>0.05 Not significant

54 Pyridine-3-Carboxamide,oxime,N-(2-trifluromethyl phenyl)-

5 .60 .565 1.000 P<0.05 significant

56 Stigmasta-5,24(28)-dien-3-ol,(3alpha,24Z)- 5 1.36 .945 7.554


57 alpha-Amyrin 5 .3420 .76474 2.235 P<0.01 significant

58

4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a,6b,7,8,8a,9,10,11,12,12a,14,14a,14b-octadecahydro-2H-picen-3-one

5 4.414 1.30657 3.555 P<0.05 significant

59 4,22-Stigmastadiene-3-one 5 .4280 .42822 14.409 P>0.05 Not significant

60 alpha-Amyrin 5 1.35 .850 1.000 P<0.05 significant

61 12-Oleanen-3-yl acetate, (3alpha)- 5 11.85 1.83869 5.848

P<0.01 significant

62 9,19-Cyclolanost-24-en-3-ol,acetate 5 .13 .300 6.503

P<0.01 significant

63 Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 5 8.366 3.19913 7.264

P<0.01 significant

64 Taraxasterol 5 1.568 .53914 3.442 P>0.05Not significant

Table 5.25c. Karl Pearson correlation between meteorological parameters and

variations in the total numbers of compounds

Numbers of compounds vs. Correlation value Statistical inference Annual Lowest Temperature .882* P<0.05 significant Seasonal Mean R.H.morning .890* P<0.05 significant Monthly Mean R.H.mornin .887* P<0.05 significant Seasonal Lowest R.H.morning -.958* P<0.05 significant

Seasonal Lowest R.H. evening -.999** P<0.01 significant

Monthly Lowest R.H. evening -.996** P<0.01 significant

Seasonal Total rainfall(mm) .936* P<0.05 significant Monthly total rainfall(mm) .962** P<0.01 significant Numbers of rainy days per season (2.5mm and above) -.907* P<0.05 significant

N=5

130

Table 5. 25d. Karl Pearson correlation between soil parameters and variations in

the total numbers of compounds

Numbers of compounds vs.soil parameters


Total Nitrogen (%) .906* P<0.05 significant Iron (ppm) .911* P<0.05 significant

N=5

5.3.2.1.6. Groups of chemical

The total numbers of compounds identified (64) belong to the families of

terpenes, sterols, fatty acids, hydrocarbons, heterocyclic compounds, phenolics and

hydroxylamines (Table 5.26). The terpenes are the major group of compound among

the identified ones.

Table 5.26. Chemical groups

Compound group L1 L2 L3 L4 L5

Terpenes 13*(61.81**) 11*(42.5**) 11*(56.26**) 13*(62.44**) 9*(54.53**)

Sterols 6*(10.06**) 5*(11.38**) 8*(9.2**) 6*(8.61**) 5*(8.67**)

Fatty acids 8*(12.52**) 8*(15.63**) 8*(10.49**) 7*(8.87**) 8*(17.28**)

Hydrocarbons 2*(12.97**) 14*(19.01**) 12*(19.79**) 12*(15.88**) 12*(14.96**)

Heterocyclic compounds 2*(2.65**) 2*(2.08**) 2*(2.34**) 2*(1.47**) 3*(3.17**)

Phenolics - 1*(0.10**) - - -

Hydroxylamines - 1*(1.81**) 1*(1.80**) 1*(2.52**) 1*(0.98**)

(* Total number; ** tentative Quantity)

5.3.2.1.7. Terpenoids

There are 17 compounds identified in the locational samples. They are of to

five types (1. Monoterpene, 2. Diterpenes, 3. Sesquiterpene 4. Triterpenes, 5.

Pentacyclic Triterpenes). Based on the numbers of compounds the locations can be

descendingly arranged as L1 (14)>L4 (13)>L3 (12)> L2 (11)>L5 (10). According to

the quantity of the phytochemicals the descending order of the locations is

L4 (62.44)>L1 (61.81)>L3 (56.26)>L5 (54.53)>L2 (42.5)

131

The 12-Oleanen-3-yl acetate, (3.alpha.) is found only in hilly terrain and

Caryophyllene is identified only in the coastal tract. The variations in the numbers of

compound and the quantity are statistically significant (Table 5.27a).

The variation in the numbers of compounds are associated with the soil

parameters (soil organic carbon and the organic matter) and not associated with any of

the meteorological elements (Table 5.27b). The quantity of the terpenoids is

associated with the meteorological elements of relative humidity only (Table 5.27c).

Table 5.27.Terpenoids S.no. Compounds name L1 L2 L3 L4 L5

MONOTERPENES

1. Bicyclo[3.1.1]heptane,2,6,6-trimethyl-,(1alpha ,2beta 5alpha) 0.23 0.43 0.47 0.28 1.09

DITERPENES (ACYCLIC) 2. Phytol 1.45 0.76 0.94 0.56 1.79 SESQUITERPENE 3. Caryophyllene 0.15 - - - -

TRITERPENES

4. Olean-12-ene 1.79 - - - -

5. Urs-12-en-24-oic acid, 3-oxo-,methyl ester,(+)- 16.97 18.95 9.84 15.11 9.24

6. Hop-22(29)-en-3.beta.-ol 8.13 9.01 11.67 9.67 14.557. Squalene 0.9 1.24 1.45 0.97 2.14 8. Taraxasterol - 1.15 1.83 2.3 1.38

9. A'-Neogammacer-22(29)-en-3-ol,acetate,(3.beta.,21.beta.)- 1.19 - 1.09 1.55 1.24

10. Olean-12-ene,3-methoxy-,(3.beta.)- 0.66 - 0.67 - -

11. Oxirane,2,2-dimethyl-3-(3,7,12,16,20-pentamethyl-3,7,11,15,19-Heneicosapentaenyl)-,(all-E)-

- 0.25 - - -

PENTACYCLIC TRITERPENE 12. Alpha-Amyrin 12.36 8.8 12.65 13.67 11.7613. Beta-Amyrin 3.78 3.17 6.61 4.2 4.31 14. Lup-20(29)-en-3-ol,acetate,(3.beta.)- - 2.9 - 2.1 - 15. 12-Oleanen-3-yl acetate, (3.alpha.)- - - 1.47 10.16 -

16. Urs-20-en-3-ol,(3.beta.,18.alpha.,19.alpha.)- 0.96 - - 1.09 -

17.

Beta-Amyrene derivatives-4,4,6a,6b,8a,11,12,14b-Octamethyl-1,4,4a,5,6,6a, 6b,7,8,8a,9, 10,11,12,12a, 14, 14a,14b - octadecahydro-2H-picen-3-one

0.49 0.67 - 0.98 -

132

Table 5.27a. Seasonal variations in the number and quantity of terpenoids




Numbers of Terpenes

5 12.00 1.581 16.971 P<0.01 significant

quantity of Terpenes

5 55.4920

8.02332 15.465 P<0.01 significant

DF=4

Table 5.27b. Karl Pearson correlation between numbers of terpenoids and soil

parameters

Numbers of terpenes vs. Soil parameters


Organic Carbon (%) .924* P<0.05 significant Organic Matter (%) .924* P<0.05 significant

N=5

Table 5.27c. Karl Pearson correlation between quantity of terpenoids and

meteorological parameters

Quantity of terpenes vs.

Meteorological parameters Correlation value Statistical inference

Annual Mean R.H. morning .927* P<0.05 significant Monthly Mean R.H. morning .890* P<0.05 significant Annual Highest R.H. morning -.905* P<0.05 significant

Seasonal Highest R.H. morning .917* P<0.05 significant

Monthly Highest R.H. morning .917* P<0.05 significant

N=5

5.3.2.1.8. Hydrocarbons

In the locational comparison, 18 hydrocarbons are identified (Table 5.28). But

for L2 (13 compounds) other location possess 12 compounds. According to the

quantity they can be arranged as L3 (19.79) > L2 (19.01)>L4 (15.88) > L5 (14.96)>

L1 (12.97). Bicyclo [4.2.0] oct-2-ene,3, 7-dimethyl- 7- (4-methyl-3-pentenyl) -8-

(2,6,10-trimethyl-1,5,9-undecatrienyl) -, [1alpha., alpha, 7beta, 8.alpha (1E,5E)]-; 1,2-

Benzenedicarboxylic acid, mono(2-ethylhexyl) ester ; is present only in the riverine

zone. The locational variation in the hydrocarbon is statistically significant (Table

133

5.28a). The variations in the number is associated with the meteorological parameter

of Annual Mean R.H. morning (Table 5.28b) and the soil parameters of nitrogen,

phosphorous, iron, zinc and copper (Table 5.28c) . The quantity is related to the soil

sodium (Table 5.28d) and it is not associated with any of the meteorological elements.

Table 5.28. Locational variations in the Hydrocarbons

S.no. Compound L1 L2 L3 L4 L5

1.

Bicyclo[4.2.0]oct-2-ene, 3,7-dimethyl-7-(4-methyl-3-pentenyl)-8-(2,6,10-trimethyl-1,5,9-undecatrienyl)-,[1.alpha.,6.alpha.,7.beta.,8.alpha.(1E,5E)]-

1.09 1.33 2.4 - 0.87

2. Tricyclo[4.3.0.0(7,9)]nonane, 2,2,5,5,8,8-hexamethyl-,(1.alpha.,6.beta., 7.alpha.,9.alpha.)-

0.29 - - 1.49 0.49

3. 2(1H)Naphthalenone,3,5,6,7,8,8a-hexahydro-4,8a-dimethyl-6-(1-methylethenyl)-

- 4.04 - 3.25 -

ALKANES 4. Tricosane 4.63 4.81 6.18 5.17 5.35 5. Nonacosane 2.37 1.96 6.37 1.88 2.27 6. Hexadecane 0.31 - - 0.24 - 7. Tetradecane 0.19 0.11 0.14 0.21 0.19 8. Dodecane 0.43 0.57 - 0.14 - 9. Z-14-Nonacosane - 1.98 - 1.84 2.69 10. Octacosane 1.86 1.35 0.71 - 0.5 11. 17-Pentatriacontene 0.13 0.37 0.34 - 0.25 12. Heneicosane,11-decyl- - - 1.21 0.89 0.92 CHLORINATED 13. Nonadecane,1-chloro- - 1.06 0.82 - - 14. Heptacosane,1-chloro 0.43 0.43 0.77 0.37 0.47 ALKENES 15. Z-12-Pentacosene 0.73 0.19 0.09 - 0.19 DICARBOXYLIC ACIDS 16. Heptacosane 0.33 0.62 0.44 0.17 0.59 CYCLOPENTANES 17. 5-Ethylcyclopent-1-enecarboxaldehyde 0.18 0.19 0.11 0.23 0.18

CARBOCYCLIC ACIDS PTHALIC ACIDS

18. 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl) ester - - 0.21 - -

134

Table 5.28a. Locational variations in the numbers and quantity of the

Hydrocarbons (One sample t - test)

DF = 4

Table 5.28b. Karl Pearson correlation between meteorological parameters

and numbers of the hydrocarbons

Parameters Correlation value Statistical inference

Annual Mean R.H. morning .889* P<0.05 significant

N=5

Table 5.28c. Karl Pearson correlation between soil parameters and the

numbers of hydrocarbons


Total Nitrogen (%) .895* P<0.05 significant

Total Phosphorous (%) .980** P<0.01 significant

Iron .928* P<0.05 significant

Zinc .950* P<0.05 significant

Copper .929* P<0.05 significant

Table 5.28d. Karl Pearson correlation between soil parameters and quantity

of the hydrocarbons


Total Sodium (%) 893* P<0.05 significant

5.3.2.1.9. Fatty acids

The locational samples consist of 13 fatty acids (Table 5.29). Except L4 (7-

compounds) all the other locations possesses 8 compounds. The maximum quantity is

present in L5 (17.28) and the minimum is present in the L4 (8.87). According to the

quantity they can be descendingly arranged as L5 (17.28) >L2 (15.63) >L1 (12.52)

>L3 (10.49) >L4 (8.87). The Tetracosanoic acid, methyl ester, Hexadecanamide,



Numbers of Hydrocarbons

5 12.40 .894 31.000 P<0.01 significant

Quantity of Hydrocarbons

5 16.7240

2.56771 14.564 P<0.01 significant

135

11,13-Dimethyl-12-tetradecen-1-ol acetate are present only in riverine zone;

2,6,10,14,18,22-tetracosahexanone, 2,6,10,15,9,23- hexamethyl-, (all-E)- appear only

in terrestrial – rural stretch.

The locational variations found in the fatty acids are statistically significant

(Table 5.29a). The numbers of compounds is associated only with the soil parameters

of total potassium. The quantity is associated neither with climate nor soil parameters

(Table 5.29b).

Table 5.29. Fatty acids

S.No. Compound L1 L2 L3 L4 L5 UNSATURATED

1. 9,12,15-Octadecatrienoic acid,(Z,Z,Z)- 2.58 4.37 1.58 0.55 3.42

MONOUNSATURATED 2. 9-Octadecenamide,(Z)- 3.08 2.78 2.31 3.99 4.24 Erucic Acid 3. 13-Docosenamide,(Z)- 0.08 0.21 - 0.14 0.18 LINOLEIC ACIDS PALMITIC ACIDS 4 Hexadecanamide -- - 0.37 - - 5 n-Hexadecanoic acid 4.95 6.57 4.34 2.68 6.76 ESTERS, methyl 6 Hexadecanoic acid,methyl ester 0.15 0.23 - - - MYRSITIC ACIDS 7 Tetradecanoic acid 0.26 0.37 0.44 - 0.49 LAURIC ACIDS 8 Dodecanamide 1.27 - 0.46 0.34 -

STEARIC ACIDS ESTERS

9 9-Octadecanoic acid,methylester,(E)- - 0.48 - - 0.35

Omega-3 Derivatives Docosahexaenoic Acids

10 2,6,10,14,18,22-tetracosahexanone,2,6,10,15,9,23-hexamethyl-,(all-E)-

- - - 0.89 -

FATTY ALCOHOLS 11 2-Methyl-Z,Z-3,13-octadecadienol - - - - 0.68

12 11,13-Dimethyl-12-tetradecen-1-ol acetate - - 0.71 - -

POLYMERIC FATTY ACIDS 13 9-Octadecyne 0.15 0.62 0.28 0.28 1.16

136

Table 5.29a. Locational variations in the numbers and quantity of the Fatty acids


DF= 4

Table 5.29b. Karl Pearson correlation between soil parameters and numbers of

fatty acids


Total Potassium (%) .925* P<0.05 significant

5.3.2.1.10. Sterol composition

There are 12 sterols identified in the locational samples (Table 5.30). The

location sample 1, L3, and L4 possess 6 compounds and the others possess 5

compounds. As per their quantity the descending order is as follows L2 (11.38)> L1

(10.06)>L3 (9.2)>L5 (8.67)> L4 (8.61). The following compounds are present in any

one location only 9, 19-Cyclolanost-24-en-3-ol, acetate, (3.beta.)-,

Desmosterol,Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)-,Ergost-22-en-3-l, (3beta,

5alpha, 22E, 24R)- (riverine zone ); stigmasterol, 22, 23 – dihydro - (coastal tract).

The locational variations among the sterol compounds are qualitatively and

quantitatively statistically significant (Table 5.30a). The quantities of the sterol

compounds are associated only with the meteorological elements such as seasonal

lowest temperature and the annual lowest R.H. morning. The number of the

compounds is not associated with the meteorological elements and the soil parameters

(Table 5.30b).

Table 5.30. Sterol composition

S.no. Compound L1 L2 L3 L4 L5 1. 9,19-Cyclolanost-24-en-3-ol,acetate 2.18 0.81 1.5 1.98 1.37 2. 9,19-Cyclolanost-24-en-3-ol,acetate,(3.beta.)- - - 0.67 - - PHYTOSTEROLS 3. Campesterol 1.78 1.91 2.16 1.57 1.83


Numbers of Fatty acids 5 8.00 .707 25.298 P<0.01 significant

Quantity of Fatty acids 5 13.32

80 4.68214 6.365 P<0.05 significant

137

4. Stigmasterol 1.06 1.79 1.35 1.09 1.4 5. gamma-Sitosterol 2.76 3.35 2.14 - - 6 Stigmasterol,22,23-dihydro- - - - 1.7 1.74 CHOLESTEROLS -(Dehydrocholesterols) 7. Desmosterol - - 0.2 - STIGMASTEROLS Analogs/Derivatives 8. Stigmasta-5,24(28)-dien-3-ol,(3.beta.,24Z)- - - 2.04 - - 9. 4,22-Stigmastadiene-3-one 1.69 0.89 - 1.68 1.69

ERGOSTEROLS Analogs/Derivatives (Withanolides)

10. Ergost-22-en-3-0l,(3.beta.,5.alpha.,22E,24R)- - - 0.32 - -

11. Ergost-8,24(28)-dien-3-ol,4,14-dimethyl-,(3.beta.,4.alpha.,5.alpa.,)- 0.56 - - 0.59 -

Table 5.30a. Locational variations in the numbers and quantity of the Sterols


DF= 4

Table 5. 30b. Karl Pearson correlation between meteorological elements and

quantity of sterols


Seasonal Lowest Temperature .894* P<0.05 significant

Annual Lowest R.H. morning -.980** P<0.01 significant

DF= 4

5.3.2.1.11. Heterocyclic Compounds

There are three heterocyclic compounds present in the locational samples. L5

possesses all the three compounds and the other locations possess two compounds.

The quantity of the compound ranges between 2.65(L1) to 3.17(L5). 2(4H)-

Benzofuranone, 5, 6, 7, 7a-tetrahydro-4, 4,7a-trimethyl- is present only in the sample

L5- terrestrial urban area (Table 5.31). The locational variations among these

compounds are significant (Table 5.31a). The variation in the numbers is correlated

with potassium content of the soil (Table 5.31b). The quantity is associated with pH

and potassium content of the soil (Table 5.31c).

Parameter N Mean Std. Deviation t Statistical inference Numbers of Sterols 5 5.60 .548 22.862 P<0.01 significant Quantity of Sterols 5 9.5840 1.16014 18.472 P<0.05 significant

138

Table 5.31. Heterocyclic compounds

S.no. Compound L1 L2 L3 L4 L5 BENZOPYRANS 1. Vitamin E 2.42 1.53 2.02 1.47 2.8

2. 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-4,4,7a-trimethyl- - - - - 0.12

TOCOPHEROLS 3. gamma-Tocopherol 0.23 0.16 0.32 - 0.25

Table 5.31a. Locational variations in the numbers and quantity of the Heterocyclic

compounds (One sample t - test)

DF= 4

Table 5.31b. Karl Pearson correlation between soil parameters and number of

heterocyclic compounds


Total Potassium (%) .925* P<0.05 significant

N=5

Table 5.31c. Karl Pearson correlation between soil parameters and number of

heterocyclic compounds

Parameters Correlation value Statistical inference pH .892* P<0.05 significant Total Potassium (%) .996** P<0.01 significant

N=5

5.3.2.1.12. Phenolics

Only one phenolic compound and hydroxyl amine are identified in the

locational samples (Table 5.32 and 5.33). The phenolic compound is found only in the

hilly terrain. The hydroxyl amine is found in all the locations except L1.The quantity

is 0 (L1) to 2.53 (L4). The variation in this compound is not associated with the

meteorological elements. The quantity of the hydroxylamine is correlated with the pH

of the soil (Table 5.33a).

Parameter N Mean Std. Deviation

t Statistical inference

Numbers of Heterocyclic compounds

5 2.00 .707 6.325 P<0.01 significant

Quantity of Heterocyclic compounds

5 2.3420

.63433 8.256 P<0.01 significant

139

Table 5.32. Phenolics and hydroxylamine

S.no. Compound L1 L2 L3 L4 L5 Phenolics - Catechols 1 2-Methoxy-4-vinyl phenol - 0.09 - - -

Table 5.33. Hydroxylamine

S.no. Compound L1 L2 L3 L4 L5

Hydroxylamine- Oximes

2 Pyridine-3-Carboxamide,oxime,N-(2-trifluromethyl phenyl)- - 1.47 1.8 2.53 0.98

Table 5.33 a. Karl Pearson correlation between soil parameters and quantity of

hydroxylamines compounds


pH -.941* P<0.05 significant

N=5

5.3.2.2. Locational variations in the inorganic compounds

5.3.2.2.1. Locational variations in the ash content

The ash composition is high in the L5 (1.89%) and low in the L1 (1.72 %)

(Figure 5.14). The locational variations in the ash composition are statistically

significant (Table 5.34). The variations noticed are not associated with any

meteorological and soil factors.

1.72

1.81

1.74

1.79

1.89

1.6

1.65

1.7

1.75

1.8

1.85

1.9

Ash

(%)

L1 L2 L3 L4 L5

Figure 5.15. Locational variations in the ash content

140

Table 5.34. Variations in ash content (One sample t - test)


Ash (%) 5 1.7900 .06671 60.001 P<0.01 significant

DF=3

5.3.2.2.2. Locational variations in the essential macro nutrients

The essential macronutrients present in the plant samples collected at five

different locations show distinct variations. The figure (5.15a) exhibits the variations

in the nitrogen which is between 2.05 %( L1) to 2.18% (L2 and L4). The nitrogen is

comparatively rich in hilly terrain and the terrestrial - rural stretch and lower in L1

(coastal tract).

The figure (5.15b) shows the ranging of phosphorous between 0.53% (L4) to

0.58% (L5). The phosphorous is rich in the L5 (terrestrial-urban area) and low in L4

(the terrestrial-rural stretch). The potassium is rich in the terrestrial –rural stretch (L4)

and lower in the coastal tract (L1) (Figure 5.15c).

2.07

2.18

2.09

2.18

2.03

1.95

2

2.05

2.1

2.15

2.2

Nitr

ogen

( %

)

L1 L2 L3 L4 L5

0.57

0.54

0.57

0.53

0.58

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

Phos

phor

ous (

% )

L1 L2 L3 L4 L5

Figure 5.15a. Nitrogen Figure 5.15b. Phosphorous

3.1

3.22

3.12

3.28

3.19

3

3.05

3.1

3.15

3.2

3.25

3.3

Pota

ssiu

m (

% )

L1 L2 L3 L4 L5

Figure 5.15c. Potassium

141

0.64

0.780.84

0.98

0.56

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sulp

hur

( %

)

L1 L2 L3 L4 L5

The other macronutrients, calcium, magnesium, and sulphur are depicted in

figures (5.15d to 5.15f). The variations in these compounds (N, P, K, Ca, Mg, and S)

are statistically significant (Table 5.35).

The variations found in the essential macronutrients are only associated with

the soil elements (Table 5.35 to 5.38). The nitrogen content of plant is associated with

the soil nitrogen, phosphorous, potassium, sodium, iron, zinc and copper of the soil

(Table 5.36).The phosphorous content of plant is related to the pH, nitrogen,

potassium iron, zinc and copper of the soil (Table 5.37). The potassium content of

plant is correlated with the soil nitrogen, while sulphur is associated with the pH,

electrical conductivity, and potassium. The calcium and magnesium are not associated

with any of these soil parameters (Table 5.36 and 5.37).

3.64

3.94

3.79

3.643.59

3.4

3.5

3.6

3.7

3.8

3.9

4

Cal

cium

( %

)

L1 L2 L3 L4 L5

2.51

2.65

2.48

2.592.61

2.35

2.4

2.45

2.5

2.55

2.6

2.65

Mag

nesi

um (

% )

L1 L2 L3 L4 L5

Figure 5.15d. Calcium Figure 5.15e. Magnesium

Figure 5.15f. Sulphur

142

Table 5.33. Seasonal variations in the essential macronutrients



Total Nitrogen (%) 5 2.1100 .06745 69.946 P<0.01

significant Total Potassium (%) 5 3.182 .0736 96.646 P<0.01

significant Total Phosphorous (%)

5 .5580 .02168 57.553 P<0.01 significant

Total Calcium (%) 5 3.7200 .14405 57.746 P<0.01

significant Total Magnesium (%)

5 2.5680 .07085 81.045 P<0.01 significant

Total Sulphur (%) 5 .7600 .16553 10.267 P<0.01

significant DF= 4

Table 5.34. Karl Pearson correlation between soil parameters and Nitrogen

Plant nitrogen vs. Correlation value Statistical inference

Total Nitrogen (%) -.927* P<0.05 significant Total Phosphorous (%) -.889* P<0.05 significant Total Potassium (%) -.952* P<0.05 significant Total Sodium (%) -.904* P<0.05 significant Iron -.936* P<0.05 significant Zinc -.970** P<0.01 significant Copper -.935* P<0.05 significant N=5

Table 5.35. Karl Pearson correlation between soil parameters and phosphorous

Plant phosphorous vs. Correlation value Statistical inference pH .903* P<0.05 significant Total Nitrogen (%) .948* P<0.05 significant Total Potassium (%) .944* P<0.05 significant Iron .945* P<0.05 significant Zinc .957* P<0.05 significant Copper .955* P<0.01 significant

Table 5.36. Karl Pearson correlation between soil parameters and potassium

Plant potassium vs. Correlation value Statistical inference Total Nitrogen (%) -.888* P<0.05 significant N=5

143

Table 5.37. Karl Pearson correlation between soil parameters and sulphur

Plant sulphur vs. Correlation value Statistical inference pH -.879* P<0.05 significant Electrical Conductivity (ds/m) -.964** P<0.05 significant

Total Potassium (%) -.934* P<0.05 significant N=5

5.3.2.2.3 Essential micro nutrients

The micronutrients show some variations among the locations 9 Figure 5.16a

to 5.16f). The Zn ranges from 2.69 ppm (coastal tract) to 2.89 (riverine zone) and the

copper is 0.57ppm (hilly terrain) to 0.81(coastal tract). The iron ranges between

106.3ppm (coastal track) and 112.6 ppm (hilly terrain). The manganese was about

10.36 ppm (hilly terrain) and 12.64 ppm (coastal tract). The boron was about 0.05

(terrestrial- rural stretch) to 0.08 ppm (hilly terrain and riverine zone). The

molybdenum was about 0.03 ppm (coastal tract) and 0.06 ppm (riverine zone and the

terrestrial-urban area).

The analysed micronutrients show significant variations between places (Table

5.37a). Zinc and molybdenum are mostly pertinent to the meteorological parameters

like temperature, R.H., and rainfall (Table 5.37b and c). The other parameters show

no relevance to the meteorological and soil parameters.

2.69

2.83

2.89

2.842.87

2.55

2.6

2.65

2.7

2.75

2.8

2.85

2.9

Zin

c (p

pm )

L1 L2 L3 L4 L5

0.81

0.57 0.59 0.56

0.79

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Cop

per

( ppm

)

L1 L2 L3 L4 L5 Figure 5.16a. Zinc Figure 5.16b. Copper

144

106.3

112.6

110.3 110.2

106.9

103104105106107108109110111112113

Iron

( p

pm )

L1 L2 L3 L4 L5

12.64

10.36

12.49 12.36

10.48

0

2

4

6

8

10

12

14

Man

gane

se (

ppm

)

L1 L2 L3 L4 L5 Figure 5.16c. Iron Figure 5.16d. Manganese

0.070.08 0.08

0.05

0.06

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Bor

on (

ppm

)

L1 L2 L3 L4 L5

0.03

0.05

0.06

0.05

0.06

0

0.01

0.02

0.03

0.04

0.05

0.06

Mol

ybde

num

( p

pm )

L1 L2 L3 L4 L5

Figure 5.16e. Boron Figure 5.16f. Molybdenum

Table 5.37a. Variations in the micronutrients (One sample t - test)


Total zinc (ppm) 5 2.824 .07861 80.326 P<0.01 significant Total copper (ppm) 5 .6640 .12482 11.895 P<0.01 significant Total iron (ppm) 5 109.260 2.6197 93.259 P<0.01 significant Total manganese (ppm) 5 11.6660 1.14253 22.832 P<0.01 significant Total boron (ppm) 5 .0680 .01304 11.662 P<0.01 significant Total molybdenum (ppm) 5 .0500 .01225 9.129 P<0.01significant

DF=4

Table 5.37b. Karl Pearson correlation between Meteorological parameters and

Zinc


Annual mean maximum temperature 953* P<0.05 significant Seasonal mean maximum temperature 945* P<0.05 significant

Monthly mean maximum temperature .903* P<0.05 significant

Annual mean minimum temperature .973** P<0.01 significant

145

Seasonal mean minimum temperature .967** P<0.01 significant

Monthly mean minimum temperature .973** P<0.01 significant

Annual highest temperature .973** P<0.01 significant

Seasonal highest temperature .960 P<0.05 significant

Monthly lowest temperature -.895* P<0.05 significant

Annual mean R.H.evening -954* P<0.05 significant

Seasonal mean R.H.morning -963** P<0.01 significant

Monthly mean R.H.evening 963** P<0.01 significant

Seasonal highest R.H. evening 973** P<0.01 significant

Monthly highest R.H. evening 949* P<0.05 significant

Seasonal lowest R.H. evening -.943* P<0.05 significant

Monthly lowest.R.H. Evening -.931* P<0.05 significant

Seasonal total rainfall(mm) .963** P<0.01 significant Number of rainy days per season (2.5mm and above) 949* P<0.05 significant

N=5

Table 5.37c. Karl Pearson correlation between Metereological parameters and

Molybdenym

Molybdenym vs. Correlation value Statistical inference

Annual mean maximum temperature .913* P<0.05 significant Seasonal mean maximum temperature .904* P<0.05 significant

Annual mean minimum temperature .943* P<0.05 significant

Seasonal mean minimum temperature .930* P<0.05 significant

Monthly mean minimum temperature .939* P<0.05 significant

Annual highest temperature .943* P<0.05 significant

Seasonal highesttemperature .922* P<0.05 significant

Monthly lowest temperature -.882** P<0.01 significant

Annual mean R.H.evening -.914* P<0.05 significant

Seasonal mean R.H.morning -.925* P<0.05 significant

Monthly mean R.H.evening -.926 P<0.05 significant

Seasonal highest R.H. evening -.973** P<0.01 significant

Monthly highest R.H. evening -.949* P<0.05 significant

Seasonal lowest R.H. evening -.902* P<0.05 significant

146

Monthly lowest R.H. evening -.888* P<0.05 significant

Seasonal total rainfall(mm) .937* P<0.05 significant Numbers of rainy days per season (2.5mm and above) -.927* P<0.05 significant

N=5

5.3.2.2.4. Locational variations in the nonessential element

Among the nonessential elements analysed Cr is high (0.005 mg/g) in

terrestrial-rural stretch and low (0.002 mg/g) in the coastal tract. The Ni is 0.02 mg/ g

(coastal tract and terrestrial – urban area) to 0.05 mg/ g. The Cd is about 0.02 (hilly

terrain) to 0.04 coastal tract mg/g. The Pb was about 0.12mg/g (terrestrial –urban

area) to 0.16 (coastal tract and terrestrial-rural stretch). The Co is about 0.02 mg/ g

(terrestrial-urban area) to) 0.06 mg/g (riverine zone). Except terrestrial-rural stretch

(0.002) all other samples contain equal mercury (0.001mg/ g). The silver is about 0.02

mg/ g (coastal tract) to 0.06 mg/ g (terrestrial rural stretch). The selenium is present in

very high level. It is about 0.52 (riverine zone) to 0.59 mg/ g (coastal tract).The

arsenic and cyanide are not present in any samples (Figures 5.17a to 5.17h).

The nonessential elements show (except cadmium) no correlation among the

meteorological and the soil parameters. The cadmium is correlated to the temperature

and the R.H., rainfall and wind speed (Table 5.38).

0.002

0.003

0.004

0.005

0.003

0

0.0005

0.001

0.0015

0.002

0.0025

0.003

0.0035

0.004

0.0045

0.005

Chr

omiu

m (m

g / g

)

L1 L2 L3 L4 L5

0.02

0.05

0.03

0.04

0.02

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

Nic

kel

( mg

/g )

L1 L2 L3 L4 L5

Figure 5.17a.Chromium Figure 5.17b.Nickel

147

0.04

0.02

0.03 0.03 0.03

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

Cad

miu

m (

mg

/g )

L1 L2 L3 L4 L5

0.16

0.13

0.150.16

0.12

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Lea

d ( m

g / g

)

L1 L2 L3 L4 L5

Figure 5.17c.Cadmium Figure 5.17d. Lead

0.05

0.04

0.06

0.03

0.02

0

0.01

0.02

0.03

0.04

0.05

0.06

Cob

alt (

mg

/ g )

L1 L2 L3 L4 L5

0.001 0.001 0.001

0.002

0.001

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002

Mer

cury

( m

g / g

)

L1 L2 L3 L4 L5

Figure 5.17e. Cobalt Figure 5.17f. Mercury

0.02

0.03 0.03

0.06

0.03

0

0.01

0.02

0.03

0.04

0.05

0.06

Silv

er (

mg

/ g )

L1 L2 L3 L4 L5

0.59

0.54

0.52

0.57

0.53

0.48

0.5

0.52

0.54

0.56

0.58

0.6

Sele

nium

( m

g / g

)

L1 L2 L3 L4 L5

Figure 5.17g. Silver Figure 5.17h. Selenium

Table 5.38a. Variations in the nonessential elements (One sample t - test)

parameter N Mean Std. Deviation t Statistical

inference Total chromium 5 .0034 .001140 6.668 P<0.01 significant Total Nickel 5 .0320 .01304 5.488 P<0.01 significant Total Cadmium 5 .0300 .00707 9.487 P<0.01 significant Total lead 5 .1440 .01817 17.725 P<0.01 significant Total Cobalt 5 .0400 .01581 5.657 P<0.01 significant Total Mercury 5 .0012 .000447 6.000 P<0.01significant Total Silver 5 .6175 .03403 36.287 P<0.01significant Total Selenium 5 .5500 .02915 42.183 P<0.01significant

DF= 4

148

Table 5.38b. Karl Pearson correlation between Meteorological parameters and

cadmium


Monthly Mean Maximum -.885* P<0.05 significant Monthly Highest temperature -.943* P<0.05 significant Annual Lowest Temperature .987** P<0.01 significant Seasonal Mean R.H. morning .995** P<0.01 significant Monthly Mean R.H.morning .992** P<0.01 significant Seasonal Highest R.H. morning .968** P<0.01 significant Monthly Highest R.H. morning .968** P<0.01 significant Seasonal Lowest R.H.morning 933* P<0.05 significant Seasonal Lowest R.H. evening 940* P<0.05 significant Seasonal heaviest rainfall in 24 HRS(mm) -.928* P<0.05 significant Annual mean wind speed(kmph) 993** P<0.01 significant Monthly mean wind speed(kmph) .968** P<0.01 significant

5.3.2.3. Discussion

On a comprehensive view the locational variations in phytochemicals is

markable.

The plant sample of L1 (coastal tract) reveals high amount of carbohydrate,

protein, organic carbon, copper, manganese, and the cadmium and the maximum

numbers of terpenes, sterols and hydrocarbon content. The ash value, potassium, zinc,

iron, molybdenum, chromium and nickel are lower than the other locations.

The L2 (hilly terrain) samples possesses lipids, nitrogen, calcium, magnesium;

iron, boron, nickel, manganese, and the cadmium in lower quantity and the other

parameters analysed are in moderate level.

In the L3 (riverine zone) zinc, boron, cobalt, are higher and the organic

carbon, magnesium, protein, mercury, selenium are lower and the other parameters

are moderate.

In the L4 (Terrestrial-rural) area organic carbon , nitrogen, potassium, sulphur,

chromium lead mercury, silver are considerably higher than other locations and the

Carbohydrates, phosphorous, sodium, iron, boron, are lower than the other locations

and the other factors are in moderate level.

L5 (Terrestrial-urban) area shows high contents of ash, phosphorous, sodium,

molybdenum, and the low contents of lipids, nitrogen, calcium, sulphur, nickel, lead,

cobalt, and mercury. The other parameters are at moderate level.

Mohamed and Alain (1995) suggested that accumulation of carbohydrates

under salinity stress being due to reduction in their utilization, either as a source of

149

energy or for the formation of new cells and tissues. On the other hand, Cornic and

Massacci (1996) and Abo Kassem et al (2002) reported that high salt concentration

can result in osmotic adjustment by regulating the accumulation of solutes especially

sugars and proteins.

In calotropis procera seedlings the total soluble and insoluble carbohydrates

content in the shoot and root tended to increase with increasing salinity stress in the

solution culture and also with the age of the plant which were considered to play an

important role in the osmotic adjustment (Al-Sobhi et al., 2006).

In this connection, Ahmed and Girgis (1979) emphasized the importance of

nitrogen intermediates as osmotically active ingredients in plant metabolism and

showed that desert plants depend, to a large extent, on the accumulation of organic

intermediates in building up their osmotic pressure. Nilsen and Orcutt (2000) reported

that plants frequently produce a number of unique proteins in their response to

environmental stresses. The rate of element uptake by plant is substantially affected

by plant species grown on different soils (Tlustoš et al. 2001).

Khanzada et al., (2008) worked on Calotropis procera (Ait). R.Br.

(Ascelpiadaceace) which are collected from, the different locations of Sindh shows

significant variations in the composition of As, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na,

Pb and Zn elements. The amount of Ca was the highest among them. Ca varied

according to the collection point. Maximum amount (1481.2 mg/g) of Ca was present

in the samples collected from Daulatpur Saffan and minimum amount 9.0 mg/g was

present in the samples from Jamshoro.

Khanzada et al., (2008), also reported the variations in the protein content of

calotropis procera which collected from different places. The highest value of total

protein recorded was 50.80% of dry weight (Daulatpur), 32.11% (NawabShah), 25%

(Hyderabad) and (Jamshoro) and 29.45% from different sites. Previously the highest

total protein was reported in Calotropis procera in leaf extracts 23.94, stem 8.94, bark

12.69 (Kalita et al., 2004).

Samat et al.,(2009) found that the seasonal and locational variations in the

minerals by analyzing Twenty-nine of browse plant species that recommended by

camel herders (trees and shrubs) and 24 forage types of crop residues, grasses and

forbs were collected by hand plucking and clipping from different part of Sudan

during dry and wet seasons.

150

The concentration of Mg, K, Fe, Zn was higher than other elements and the

amount of Cd, As, Pb and Cr was minimum 0.12 to 0.97 mg/g, whereas Cu and Mn

was11.9 to 12.33 mg/g and Zn 5.15 to 2.022 ppm As 40.2 μg to 30.11 μg in C.

procera from Sindh. Variations of elemental concentrationsvaried from high in Ca

(1481.2 ppm) and low in K (387.8ppm) where the K, Mg, Ca. was reported in

maximum values, wile Na, Mn, Zn, Cr are present in minimum range.

Spitaler et al., (2006) found that the total contents of sesquiterpene lactones

and flavonoids were not positively correlated with the altitude of the growing site.

However, the proportion of flavonoids with vicinal free hydroxy groups in ring B to

the flavonoids lacking this feature significantly increased with elevation.

Additionally, the level of caffeic acid derivatives also positively correlated with the

altitude of the growing site. In particular the amounts of 1-methoxyoxaloyl-3, 5-

dicaffeoylquinic acid significantly increased in elevated sites and samples from the

summit region contained 85% more of this compound than samples from valley sites.

A study was carried out by Negi et al., (2009) to determine the accumulation

and variation of trace elements in roots and leaves of Asparagus racemosus collected

from four different altitudes in Uttarakhand, India. The metals investigated were Zn,

Cu, Mn, Fe, Co, Na, K, Ca, and Li. The concentration level of Fe was found to be

highest at an altitude of 2,250 m, whereas the level of Cu was lowest.

Chieh et al., (2006) found that the concentration of synephrine,

evodiamine, dehydroevodiamine and rutaecarpine are highly varying from location to

location.

In plants, polyphenol synthesis and accumulation is generally stimulated in

response to biotic/abiotic stresses (Dixon and paiva, 1995; Naczk, and shahidi, 2004)

such as salinity (Navarro et al., 2006). Ksouri et al., (2007) found that Jerba and

Tabarka accessions differed in their growth response to salinity level, and the poly

phenol content.

Oliveira et al. (2006) studied that the Mikania cordifolia which are collected

from different locations and found that there are no significant qualitative differences

related to the presence of triterpenes and steroids. Finally they concluded that all

collected specimens of M. cordifolia presented similar constitution of triterpenoids,

despite some possible differences in proportions.

5.1. introduction - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/5087/13/13_chapter...

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