EVALUATION OF CHLORIDE USING EMBEDDED SYSTEM...

EVALUATION OF CHLORIDE USING EMBEDDED SYSTEM BASED

ANALYSER AND ARTIFICIAL NEURAL NETWORK COMPUTATION IN

BIOLOGICAL AND ENVIRONMENTAL SAMPLES

A THESIS SUBMITTED TO

BHARATHIDASAN UNIVERSITY, TIRUCHIRAPPALLI

FOR THE AWARD OF THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

PHYSICS

By R.VASUMATHI

A.V.V.M SRI PUSHPAM COLLEGE (AUTONOMOUS) (AFFILIATED TO BHARATHIDASAN UNIVERSITY)

POONDI – 613503 THANJAVUR DISTRICT TAMILNADU, INDIA

AUGUST 2011

Dr. P. Neelamegam

Reader in Physics(Retd),

Department of Physics,

A.V.V.M Sri Pushpam College,

Poondi – 613 503,

Tamil Nadu, India.

CERTIFICATE

Certified that this thesis entitled “EVALUATION OF CHLORIDE

USING EMBEDDED SYSTEM BASED ANALYSER AND ARTIFICIAL

NEURAL NETWORK COMPUTATION IN BIOLOGICAL AND

ENVIRONMENTAL SAMPLES” is the bonafied work of Mrs. R.VASUMATHI

who carried out the research under my supervision. Certified further, that to the best of

my knowledge the contents of this thesis have not formed the basis for the award of any

degree, diploma, or other similar title of any University or Institution.

Poondi. (Dr.P.NEELAMEGAM)

Date: Research supervisor/Guide

CONTENTS Page No

Preface Acknowledgements List of publications CHAPTER – I INTRODUCTION 1-18

1.1 Chloride 1 1.1.1 Pivotal Roles of Chloride in human body 2 1.1.2 Sources of Chloride 2

1.2 Roles of Chloride in biological samples 3 1.3 Roles of Chloride in environmental samples 4

1.3.1 Soil Chloride importance in plants 4 1.3.2 Yield and quality response of Chloride 5 1.3.3 Sea water Chloride importance in marine organism 5 1.3.4 Chloride influence on corrosion 7

1.4 Embedded system 8 1.5 Artificial Neural Network 10 1.6 Scope of the research work 12 References 13

CHAPTER – II MICROCONTROLLERS AND ARTIFICIAL NEURAL NETWORK 19-56 2. Basic concept of Measurement 19

2.1.1 Microcontrollers 19 2.1.2 Microcontroller Peripherals 21 2.2 Microcontroller P89C668 25 2.21 Pin descriptions 28

2.3 Microcontroller PIC16F877 29 2.3.1 Pin descriptions 32 2.4 Microcontroller P89C51RD2 33 2.4.1 Pin descriptions 36 2.5 Microcontroller ATmega32 37 2.5.1 Pin descriptions 40

2.6 Microcontroller PSoC CY8C27443 41 2.6.1 Pin descriptions 43 2.7 Artificial Neural Network 45 2.7.1 Introduction 45 2.7.2 Transfer function 46 2.7.3 Training of Artificial Neural Networks 47 2.8 Back Propagation Algorithm 49 2.8.1 Derivation for Back propagation algorithm 49

2.9 Genetic Algorithm 53 References 55

CHAPTER – III MEASUREMENT AND ANALYSIS OF CHLORIDE IN BIOLOGICAL SAMPLES 57-88

3.1 Renal physiology 57 3.1.1 Colorimetry principle 58 3.1.2 Proportionality between color and concentration 58 3.1.3 Stability of the color and clarity of the solutions 59 3.1.4 Analytical studies of the Instrument system 59

3.2 Measurement of Chloride in human urine using Microcontroller P89C668 62

3.2.1 System Architecture 62 3.2.2 Instrumental 63 3.2.3 Software 64

3.3 Materials and Method 64 3.3.1 Principle of color complex 65 3.3.2 Reagents 65 3.3.3 Measurement 65

3.4 Results and Discussion 66 3.4.1 Clinical Significance 66 3.4.2 Statistical Analysis 67 3.4.3 Detection and quantification limits 67 3.4.4 Precision 67 3.4.5 Recovery 67 3.4.6 Linear regression analysis 68

3.5 Analysis of Chloride in pharmaceutical sample - Oral Rehydration Salts 76

3.5.1 Introduction 76 3.5.2 Electrolyte supplements 76

3.6 Instrumental 77 3.6.1 Description of the Microcontroller based system 77 3.6.2 Software 78

3.7 Materials and Method 78 3.7.1 Measurement 78 3.8 Results and Discussion 79

3.8.1 Linearity and sensitivity 79

3.8.2 Recovery 79

3.8.3 Linear Regression analysis 80

3.8.4 Statistical analysis 80

References 87

CHAPTER – IV PERFORMANCE AND ANALYSIS OF THREE EMBEDDED BASED BIO-ANALYSERS IN MEASURING SERUM CHLORIDE 89-118

4.1 Introduction 89 4.2 Circuit description of three embedded based bio-analysers 90

4.2.1 P89C668 Microcontroller based bio-analyser 90 4.2.2 Bio-analyser using microcontroller PIC16F877 91 4.2.3 PSoC CY8C27443 microcontroller based bio-analyser 92 4.2.4 Software 93

4.3 Materials and Method 93 4.3.1 Measurement 94 4.4 Results and Discussion 95 4.4.1 Linearity and sensitivity 96 4.4.2 Precision 96 4.4.3 Recovery 96 4.4.4 Linear regression analysis 97 4.4.5 Statistical analysis 97

4.4.6 Interferences 97 4.5 Conclusion 97

CHAPTER – V ELECTRICAL CONDUCTIVITY AS A SURROGATE FOR CHLORIDE DETERMINATION 119-140

5.1 Introduction 119 5.1.1 Electrical Conductivity as a surrogate

for Chloride concentration 120 5.2 Agricultural Soil 120 5.3 Design and development of Electrical Conductivity Measurement set up using Microcontroller Atmega32 121

5.3.1 Design of the measurement system 121 5.3.2 AC modified Wheatstone bridge network 122 5.3.3 Microcontroller and interfacing circuit 123 5.3.4 Software 123

5.4 Materials and Method 124 5.4.1 Sampling Field 124 5.4.2 Sample Collection 124 5.4.3 Sampling procedure 125 5.4.4 Measurement 125 5.4.5 Development of regression Model 126

5.5 Results and Discussion 126 5.5.1 Analytical performance of the system 127 5.5.2 Analysis of Chloride concentration at various locations and plants 127 References 140

CHAPTER – VI COMPUTATION OF CHLORIDE IN ENVIRONMENTAL SAMPLES 141-171

Prediction of Chloride in soil samples using Artificial Neural Network 141

6.1 Introduction 141

6.2 Soil sampling 142 6.3 Implementation of Neural Network 142

6.3.1 Training phase 142 6.3.2 Validation of the developed Neural Network 143 6.3.3 Testing phase 143

6.4 Results and discussion 144 6.5 Prediction of Chloride in sea water

using Artificial Neural Network 154 6.5.1 Selection of input parameters 154

6.6 Sampling field description 154

6.6.1 Measurement 154

6.7 Back propagation Neural Network training 155

6.7.1 Genetic Algorithm based Back propagation Neural Network Training 155

6.7.2 Validation and testing pattern 156 6.8 Results and Discussion 157

6.8.1 Adaptiveness of the model 157 6.8.2 Comparison between the results obtained using Genetic and Back propagation algorithm 158 6.8.3 As an effective tool 158 6.8.4 Chloride influence for aquatic organisms 158

6.9 Conclusion 160 References 171

CHAPTER – VII PREDICTION OF RATE OF CORROSION USING CHLORIDE CONCENTRATION IN MILD STEEL 172-191

7.1 Introduction 172 7.2 Experimental procedures 174 7.2.1 Materials 174 7.2.2 Method - Rate of Corrosion by Weight Loss measurement 174 7.3 Explicit Neural Network formulations for Rate of Corrosion 174 7.3.1 Training Pattern 175 7.3.2 Validation of the proposed NN model 175 7.3.3 Testing Pattern 175

7.4 Results and Discussion 176 7.4.1 Performance of the developed model 176 7.4.2 Effect of Chloride content 176 7.4.3 Effect of pH and temperature 176

7.5 Conclusion 177 References 191

SUMMARY OF THE PRESENT WORK AND SUGGESTIONS FOR

FUTURE WORK 192-195

PUBLICATIONS

PREFACE

Chloride is one of the most eminent electrolytes required for all living cells. It is

abundant in nature and plays a vital role in biological and environmental samples. In

biological consideration, Chloride electrolyte is 70% of the body’s total negative ion

content, which is maintaining a proper balance of fluids inside and outside of human

body cells. It has a pivotal role in renal function, neurophysiology and nutrition. While

considering the environment, it influences greatly soil and sea water. Soil Chloride has

high degree of impact on plant growth and is readily taken up by the roots. It is essential

to the proper function of the plants stomatal openings and photosynthesis. It diminishes

the fungal infections of plants. Sea water contains naturally occurring Chloride which is

essential to the aquatic organisms that live there. In ocean, higher Chloride concentrations

can reduce the toxicity of Nitrite to aquatic life. In negative aspects, Chloride causes

corrosion on materials (Stainless steel, Mild steel and Concrete structures) which

produces cracks, blow holes and finally reduces the life span.

Because of the vital role of Chloride in biological and environmental samples, it

is aimed to perform the analytical studies by less expensive way. Previous research works

have suggested that the analytical method using embedded system to measure Chloride

anion in biological samples is very few. The computation of Chloride using Artificial

Neural Network (ANN) is also less compared to that of other analytical studies. Hence,

the research work is mainly focused on the evaluation of Chloride using the designed and

developed low cost embedded system in biological samples and computation of Chloride

using neural network in environmental samples.

This research report consists of seven chapters.

Chapter I explains the need for measuring Chloride electrolyte in biological and

environmental samples. It also delivers the various techniques used to measure the

Chloride electrolyte in various samples.

Chapter II presents the features of different microcontrollers used in this research

work. It also describes Artificial Neural Network and various algorithms, learning rules

related to the neural network.

Chapter III consists of the design and development of biomedical Analyser using

microcontroller P89C668 (Philips) to measure the human urinary Chloride and the

obtained results are analysed. The effect of hyperchloremia and hypochloremia are

discussed based on the results. The design and development of microcontroller

P89C51RD2 based instrument to find the concentration of Chloride in Oral Rehydration

Salts is also presented in this chapter. The recovery values of added Chloride ranged

from 98.13% to 99.15% with an average recovery of 98.42%, which indicates the

suitability of the designed instrument for bulk drugs and assay tests.

Chapter IV presents the design and implementation of Chloride analyzer to

measure Chloride in serum using the microcontrollers, (i) P89C668 (Philips) with

external peripherals, (ii) PIC16F877 (Microchip) with built in ADC, (iii) PSoC

(Programmable System on Chip- which integrates all the peripherals) - CY8C27443

(Cypress) with built in ADC, MUX, PGA etc. A comparative study has been made based

on the performance of three biomedical analysers using three microcontrollers. The

power consumption, processing time and analytical parameters of three embedded based

bio-analysers have been evaluated.

Chapter V explains the Electrical Conductivity measurement as a surrogate for

Chloride concentration measurement. The development of ATmega32 (Atmel)

microcontroller based Electrical Conductivity measurement set up has been explained.

The obtained results are compared with the EL1CO CM 180 conductivity meter to check

the accuracy of the designed instrument, which gives a correlation coefficient of R =0.98.

It is observed that the range of Electrical Conductivity of soil samples are varied from 45

to 109 mS/cm and the Chloride concentration is maximum at Ramarmadam, (1010 ppm)

and minimum at Kattuthottam (140 ppm), Tamilnadu, South India.

Chapter VI deals the Artificial Neural Network (ANN) with Back Propagation

Algorithm (BPA) to compute the Chloride concentration in soil samples. ANN has also

been used to compute the Chloride concentration in sea water samples using back

propagation algorithm neural network and Genetic Algorithm (GA) based back

propagation neural network. The training pattern consists of temperature (15 to 45° C),

pH (8.1 to 9.3) and Electrical Conductivity (25 to 34 mS/cm) as input parameters and

Chloride concentration as output parameter. The adaptiveness is checked for various

seasonal conditions by varying the input parameters.

Chapter VII presents the negative effect of Chloride concentration which induces

corrosion that reduces the life span of the materials. It explains the corrosion behavior of

mild steel in hydro chloric acid by weight loss measurements method is explained. The

rate of corrosion of mild steel at various aqueous environments by varying Chloride ion

concentration (HCl acid - 0.1N to 0.75 N), pH (0.12 to 1) and temperature (290K to

333K) is modeled by means of Artificial Neural Network is also presented in this chapter.

The summary of the present work along with the suggestions for the future

work are given in brief.

ACKNOWLEDGMENTS

I am deeply indebted to my Research Advisor, Dr.P.Neelamegam for his

guidance and kind encouragement throughout the course of my research work. His keen

interest and support are pivotal to the successful completion of my research.

I wish to thank Shriman.K. Thulasi Ayya Vandayar, Secretary and

Correspondent, A.V.V.M. Sri Pushpam College (Automonous), Poondi, Thanjavur.

I want to thank Dr.S.Chinnaian, Principal, A.V.V.M. Sri Pushpam College

(Automonous), Poondi, Thanjavur.

I owe my gratitude to Dr. A. Thayumanavan, Dean of Sciences and

Dr.P.Philominathan, Head of the Department and staff members, Department of

Physics, A.V.V.M. Sri Pushpam College (Automonous), Poondi, Thanjavur.

I wish to express my sincere thanks to the Doctoral committee members of my

research work, Dr.A. Venkatesan, Assistant professor, Department of Physics, Nehru

Memorial College, Puthanampatti and Dr.R. Radhakrishnan, Assistant professor,

Department of Physics, Jamal Mohamed College, Tiruchirappalli for all the fruitful

discussions and suggestions during the various stages of the work carried out.

I extend my sincere thanks to Dr.A.Rajendran, Assistant professor, Department

of Physics, Nehru Memorial College, Puthanampatti and Mr.R.Raghunathan,

Mr.K.Murugananthan and Mr.A.Jamaludeen for their constant interaction and

support.

I also deliver my heartfelt thanks to Mrs. N.Azhagusavithri and Mr.N.

Keerthivasan for giving a constant support during my research work.

Finally, I would like to express my thanks to my husband Mr.S.Sriram and my kid

S.Sree Krishniga for being my strength through the entire journey.

R.VASUMATHI

List of Publications

Papers Published in International Journals

1. Development of bio-analyzer for the determination of urinary Chloride –

R. Vasumathi, P. Neelamegam – Sensors & Transducers, 119, (8), 2010.

2. ATmega32 microcontroller based Conductivity measurement system as a

surrogate for Chloride estimation of soil samples - P. Neelamegam, R. Vasumathi

- Instrument and Experimental Techniques, Springer publications, No 6, 2010.

3. Measuring Chloride in serum using single Programmable System on Chip

(PSoC), P. Neelamegam, R. Vasumathi – Instrument and Experimental Techniques,

Springer publications, No 3, 2011.

4. Colorimetric determination of Chloride ion in Oral Rehydration Salts using

microcontroller P89C51RD2 - P. Neelamegam, R. Vasumathi – International Journal

of Pharmacy and Pharmaceutical Sciences, Vol 3, (2), 2011.

Papers Communicated to the Journals

1. Artificial Neural Network as a prediction tool for mild steel corrosion -

Communicated to International Journal of Pure and Applied Chemistry.

2. Prediction of Chloride in sea water of Tamil Nadu, India - Communicated to

Australian Journal of Basic and Applied Sciences.

Conference Attended/ paper presented

1. Paper presented in “National workshop on thin film preparation and

Characterisation techniques for energy conversion” 22-26th Nov 2004. Alagappa

University, karaikudi 630 003.

2. Poster presentation in “National symposium on instrumentation”

Cochin University of Science and Technology, Cochin. Nov 30th – Dec 2nd 2005.

3. Paper presented in “National conference on intelligent instrumentation”,

Department on Instrumentation Technology of Dayananda Sagar College of

Engineering, Bangalore. 7-8thApril 2006.

4. Paper presented in “5th International conference on trends in industrial

measurements and automation”, NIT, Trichy.Jan 4th-6th, 2007.

1

CHAPTER I

INTRODUCTION

Chloride is a vital mineral required for both human and animal life. All the living

things require a subtle and complex electrolyte balance between the intracellular and

extra cellular milieu. In particular, the maintenance of precise osmotic gradients of

electrolytes is important. Such gradients affect and regulate the hydration of the body,

blood pH, and are critical for nerve and muscle function. Various mechanisms exist in

living species that keep the concentrations of different electrolytes under tight control.

Both muscle tissue and neurons are considered electric tissues of the body. Muscles and

neurons are activated by electrolyte activity between the extra-cellular fluid or interstitial

fluid, and intra-cellular fluid. Electrolytes may enter or leave the cell membrane through

specialized protein structures embedded in the plasma membrane called ion channels.

Electrolytes are ions which are having electric charge. Positively charged ions are called

cations. Negatively charged ions are called anions. In physiology, the primary ions of

electrolytes are Sodium(Na+), Potassium (K+), Calcium (Ca2+), Magnesium (Mg2+),

Chloride (Cl−), Hydrogen phosphate (HPO42−), and Hydrogen carbonate (HCO3

−).

Among these, Chloride anion is an essential electrolyte for all living and nonliving

things. This chapter presents the roles of Chloride in biological and environmental

samples and the literature survey for the previous research work carried out is also

discussed.

1.1 Chloride

Chloride is a major mineral nutrient that occurs primarily in body fluids. It is a

prominent negatively charged ion of the blood, where it represents 70 percent of the

body’s total negative ion content. On average, an adult human body contains

approximately 115 grams of Chloride, making up about 0.15 percent of total body weight

[1]. The suggested amount of Chloride intake ranges from 750 to 900 milligrams (mg)

per day, based on the fact that total obligatory loss of Chloride in the average person is

close to 530 mg per day.

2

As the principal negatively charged ion in the body, Chloride serves as one of the

main electrolytes of the body. Chloride, in addition to Potassium and Sodium, assists in

the conduction of electrical impulses when dissolved in bodily water. Potassium and

Sodium become positive ions as they lose an electron when dissolved, and Chloride

becomes a negative ion as it gains an electron when dissolved. A positive ion is always

accompanied by a negative ion; hence there is close relationship between Sodium,

Potassium, and Chloride. Electrolytes are distributed throughout all body fluids including

the blood, lymph, and the fluid inside and outside cells [2]. The negative charge of

Chloride balances against the positive charges of Sodium and Potassium ions in order to

maintain serum osmolarity.

1.1.1 Pivotal roles of Chloride in human body

In addition to its functions as an electrolyte, Chloride combines with hydrogen in

the stomach to make hydrochloric acid—a powerful digestive enzyme responsible for the

breakdown of proteins, the absorption of other metallic minerals, and activation of

intrinsic factor, which, in turn, absorbs vitamin B12. Chloride is specially transported into

the gastric lumen, in exchange for another negatively charged electrolyte (bicarbonate) in

order to maintain electrical neutrality across the stomach membrane. After utilization in

hydrochloric acid, some Chloride is reabsorbed by the intestine, back into the

bloodstream where it is required for maintenance of extra cellular fluid volume [2]. For

Chloride is both actively and passively absorbed by the body, depending on the

current metabolic demands. A constant exchange of Chloride and bicarbonate between

red blood cells and the plasma helps govern pH balance and transport carbon dioxide, a

waste product of respiration, from the body. With Sodium and Potassium, Chloride works

in the nervous system to aid in the transport of electrical impulses throughout the body, as

movement of negatively charged Chloride into the cell propagates the nervous electrical

potential. Deficiency of Chloride results in a life threatening condition known as

alkalosis, in which the blood becomes overly alkaline.

1.1.2 Sources of Chloride

Healthier sources of Chloride include kelp (seaweed), ionic trace minerals, olives,

rye, tomatoes, lettuce, celery, beef liver, breads, salmon (canned), veal liver, cheese,

3

vegetables (canned), chicken liver, clams, dried beef, eggs, frankfurters, olives (green),

milk, oysters, peanut butter, table salt, tomato juice and turkey liver [3].

1.2 Roles of Chloride in biological samples

Measurement of urinary Chloride is most useful in the differential diagnosis of

persistent metabolic alkalosis [4]. Metabolic alkalosis can be classified as Chloride

responsive or resistant. Chloride responsive alkalosis is due to loss of hydrogen ion and

Chloride containing extra-cellular fluid, such as occurs with vomiting, nasogastric suction

and diuretic therapy. Chloride resistant alkalosis results from excessive secretion of

mineralcorticoid hormones, which causes bicarbonate reabsorption. The most common

causes are hyperaldosteroism, Cushing's syndrome, exogenous steroid administration,

licorice ingestion and alkali ingestion. The reference range for a 24 hour human urine

collection is 20-250 mmol/day.

The serum Chloride value is a concentration measurement (e.g., the amount of

Chloride/liter of plasma water). Therefore, the serum Chloride concentration can be

elevated above the normal range (98 - 108 mmol/l)—hyperchloremia—either by the

addition of excess Chloride to the Extra Cellular Fluid (ECF) compartment or by the loss

of water from this compartment, and vice versa. The serum Chloride concentration can be

reduced below the normal range—hypochloremia—by the loss of Chloride from the ECF

or the addition of water to this compartment. This means that one cannot evaluate total

body Chloride stores from the serum Chloride concentration. Clinical parameters must be

used in conjunction with serum Chloride values to assess the significance of

hypochloremia or hyperchloremia.

Hypochloremia is due to total body Chloride depletion through, extrarenal,

inadequate NaCl intake, losses of gastrointestinal fluids, vomiting, nasogastric suction,

small bowel fistulas, burns, renal, diuretic abusers, salt-losing nephropathy, interstitial

nephritis, adrenal insufficiency, increased effective circulatory blood volume, drugs and

nicotine.

Conditions associated with Hyperchloremia are fever, hyper metabolic states

increased ambient room temperature, inadequate water intake, loss of thirst perception

4

renal losses, central diabetes insipidus, nephrogenic diabetes insiuidos, early renal failure

and primary hyperparathyroidism.

1.3 Roles of chloride in environmental samples

1.3.1 Soil Chloride importance in plants

One of the Chloride influenced environmental samples is soil. Soil is defined as a

thin layer of earth's crust which serves as a natural medium for growth of plants. They

serve as a reservoir of nutrients and water for crops, provide mechanical anchorage and

favourable tilth. The components of soil are mineral matter, organic matter, water and air,

the proportions of which vary and which together form a system for plant growth. The

soil Chloride is readily taken up by the plants. The effect of Chloride, soil fertility and

plant nutrition on plant diseases has been the subject of numerous investigations over the

past two-decades.

Chloride is a nutrient which is important for plant growth. In the mid-1800's

Totingham, 1919 [5] reported that barley top dressed with NaCl (common table salt) is

helpful in stiffening straw and that Chloride is the active ingredient in this fertilizer. The

essentiality of Chloride for plant growth is confirmed during the 1950's. Broyer et al

(1954) [6] is generally considered to be the first study to demonstrate a requirement for

Chloride in higher plants. Growing Marglobe tomato plants in a hydroponic culture

depleted of Chloride, they found growth (dry matter production) is retarded up to 65%

compared to plants receiving adequate Chloride. Deficient plants resumed satisfactory

growth after Chloride is added to the nutrient solutions, or injected into the plants via a

hypodermic needle. Deficiency symptoms are characterized by wilting of leaflets in the

early stages, followed by chlorosis, bronzing, and necrosis in areas proximal to the

wilting. Following this initial study, Chloride requirement are demonstrated in number of

other plant species, including alfalfa, barley, bean, buckwheat, cabbage, carrot, corn,

lettuce, potato, squash, and sugarbeet (Ulrich and Ohki,1956; Johnson et al., 1957;

Ozanne, 1958; Gausman et al., 1958) [7, 8, 9, 10].

5

1.3.2 Yield and quality response of Chloride

Chloride deficiencies in plants generally occur in inland soils (Fixen, 1987) [11].

Substantial responses to Chloride containing fertilizers have been reported for different

crops in many parts of the world; e.g.,Coconut (Von Uexkull and Sanders, 1986) [12],

Corn (Heckman, 1995) [13], Kiwifruit (Smith et al., 1987) [14], Oil palm (Von Uexkull,

1990) [15], Potato (Gausman et al., 1958a) [16], Spring wheat and Barley (Fixen et al.,

1986; Engel et al., 1994) [17, 18], Tobacco (Li et al., 1994) [19], and sugarbeet (Zhou

and Zhang, 1992) [20]. Typical symptoms of Chloride deficiency include wilting of

leaves, curling of leaflets, bronzing and chlorosis similar to those seen with Mn

deficiency and severe inhibition of root growth (Ozanne et al., 1957 [21]; Smith et al.,

1987) [14].

The concentration range of Chloride deficiency in plants varies between 0.13 and

5.7 mg/g for spinach and sugarbeet respectively. In wheat, the Chloride concentration of

leaf tissue at heading is a good predictor of the response to Chloride fertilization (Engel

et al., 1998) [22]; the critical range is between 1.5 and 4 mg/g above which no further

response is expected.

In pot experiments, positive responses to Chloride at 100-200mg/kg soil are

reported for white potato, peanut, tomato and at 100-1600mg/kg soil for sugarbeet (Jing

et al., 1992) [23]. On a sandy loam soil, Chloride applications of up to 400kg/ha yielded

500- 1500kg/ha more corn gain than is obtained in the control (Heckman, 1995) [13].

Grain yields of corn are correlated positively with increases in Chloride concentrations in

the leaf ears. Yields of sugarcane fertilized with Ammonium Chloride exceeded or

equaled those of Ammonium Sulfate at 67-225 kg N/ha (about 170- 570 kg Cl/ ha) (Veda

Narayanan, 1990) [24].

1.3.3 Sea water Chloride importance in marine organism

“Life is evolved from water”[25]. The ocean holds all of the Chloride that we

need. Chloride is an essential element for aquatic and terrestrial biota, representing the

main extra-cellular anion in animals. Although Chloride is an essential element for

maintaining normal physiological functions in all aquatic organisms, elevated or

fluctuating concentrations of this substance can be detrimental. More specifically,

exposure to elevated levels of Chloride in water can disrupt osmoregulation in aquatic

6

organisms leading to impaired survival, growth, and/or reproduction. Because excess

Chloride is most frequently actively excreted from animal tissues via the kidneys or

equivalent renal organs to achieve osmoregulatory balance, the bioaccumulation potential

of Chloride is low.

Since 1977 Nitrite toxicity has been known to depend greatly on the salinity of the

water in which the Nitrite exposure took place (Crawford and Allen, 1977) [26].

Mortality in seawater occurred at Nitrite concentrations 50 to 100 times higher than in

fresh water (Crawford and Allen, 1977) [26]. The effect of Chloride on the toxicity of

Nitrite is now known to be so great that experiments in which Chloride concentrations are

not documented are of very low value because they cannot be meaningfully compared

with the results of other studies. According to Eifac (1984) [27] recommendation, it is

very important to monitor the Chloride-Nitrate ratio in aquaculture. The ratio of 17 and 8

is recommended for salmon and rough fish, respectively. In the case of Nile tilapia

(Oreochromis niloticus) health impairment without symptoms of toxicity, the ratios

ranged between 50 and 150 (Svobodova et al., 2005a) [28]. The experiments conducted

on rainbow trout and fathead minnow (Pimephales promelas) showed that the

relationship between Nitrite toxicity and Chloride concentration is linear (Russo and

Thurston, 1977; Palachek and Tomasso, 1984b; McConnell, 1985) [29, 30, 31]. Machova

et al. (2004) [32] also proved the linear relationship between lethal concentration and

Chloride concentration in water for ornamental fish (Poecilia reticulate). The most

sensitive species benefit from Chloride addition to the least extent although the benefit is

large even for sensitive fish (Lewis and Morris, 1986) [33].

Nitrite has an affinity for the active Chloride uptake mechanism by Chloride cells

in the gills (Maetz, 1971) [34]. Chloride cells excrete ammonia or H+ ions for Na+ ions

and bicarbonate (HCO3-) for Chloride ions (Love, 1980) [35]. Nitrite has affinity to

Chloride/ HCO3- exchanging. Part of Chloride demand is replaced by Nitrite when it is in

water. Fish with higher speed of Chloride uptake by gills (rainbow trout, perch, pike) are

more sensitive to Nitrites then fish with lower speed of Chloride uptake (eel, common

carp, tench) (Williams and Eddy, 1986) [36]. The competition between Chloride and

Nitrite ions transport across the gill membrane explains because the higher concentration

of Chloride protects fish against toxic impact of nitrite (Jensen, 2003) [37]. The positive

7

effect of Chloride for fish tolerance to Nitrites has been demonstrated in other studies in

fish (Svobodova et al., 1987; Hilmy, 1987; Atwood et al., 2001; Huertas et al., 2002;

Tavares and Boyd, 2003; Fuller et al., 2003) [38, 39, 40, 41, 42, 43] and for crayfish

(Jeberg and Jensen, 1994; Beitinger and Huey, 1981) [44, 45].

1.3.4 Chloride influence on Corrosion

In negative aspects, Chloride causes corrosion on steels and concrete. The

research field on evaluation of Chloride diffusion in concrete is growing with

consideration for diffusion, permeation [46] and binding capacity of Chloride ions [47,

48]. Recently, numerical techniques covering Chloride diffusion in partially saturated

condition [49], Chloride behavior in concrete with early-age cracking [50], and micro

structures formation modeling in high performance concrete [51, 52] are proposed based

on behavior in early-age concrete considering hydration and micro pore structure. The

most significant environmental condition which influences the pitting corrosion behavior

of austenitic stainless steels is the Chloride ion concentration to initiate corrosion-induced

deterioration [53]. Chloride ion causes local destruction of the passive film leading to

localised corrosion.

Since the Chloride electrolyte plays a vital role in biological and environmental

samples, measurement of Chloride is necessary. Classical research lines in the sensors

field have pursued the achievement of ever more selective devices towards a particular

chemical species, and at the same time sensitive to its lower concentrations. Several

analytical methods are available to estimate the Chloride concentration in various

samples.

From the Literature survey, it is observed that the various analytical methods used

for the determination of Chloride in biological sample of blood are iodometric procedure

(Geoffrey Arthur, D and Earl Judson King, 1936) [54], Potentiometry ISE (A. Scheipers

et al., 2001) [55], in serum are Direct potentiometry method (M. Panteghini et al., 1986)

[56], Rayleigh light scattering technique (Jing wen Chen et al., 2007) [57], Colorimetry

method (R.G. Schoenfeld et al.,) [58], Enzymatic assay method (Toehihlro Ono et al.,

1988) [59], Chloride ion selective electrode method (G. Dimeski and AE. Clague, 2004,

W.Hubl et al., 1994) [60, 61], Apple computer (Paul J.Taylor and Rosalie A. Bouska,

1988) [62], Cyclic voltametry (Kensuke Arai, 1996) [63], Flow injection analysis (S.

8

Alegret et al., 1988) [64], Optical sensors (Huber Christial et al., 2003) [65] in another

biological sample of urine are colorimetry (Katsuhiko Yokoi, 2002, Wilbur L. Reimers

and Robert M. Zollinger, 1951) [66, 67], Potentiometric method (P.H. Anderson, 1952)

[68], Screen printed silver strip sensor (2009) [69], in sweat by Electrochemical method

(Javier Gonzalo Ruiza et al., 2009) [70] and so on.

Considering the environmental sample of soil, the Chloride concentration is

determined using Electrical Conductivity measurement which can be used as a surrogate

for Chloride concentration (Hamid Zare Abyaneh et al.,2005) [71]. The measurement of

Electrical Conductivity has been made by four point probe method (Makoto Ishikawa et

al., 2006, S.P.S. Badwal et al., 1991) [72, 73], Analog interface (Diego Ramirez Munoz

and Silvia Casans Berga., 2005) [74], Microcontroller AT89C55WD (A. Rajendran and

P.Neelamegam., 2004) [75], Conducting sensors (X.Li et al., 2002) [76], Optimal

frequency range in aqueous solutions (Ferrara L. Callegaro and F. Durbiano, 2000) [77].

The effect of Chloride application in soil and influence on plants (Hiroyuki

Hattori et al., 2006, Karin wiggler et al., 2004) [78, 79], Effect of soil Chloride on

Cadmium concentration in sunflower kernels (Yin Ming Li et al., 1994) [80], on yield

quality of strawberry fruits (Mahmood Esna Ashari and Mansour Gholami, 2010) [81],

on wheat (Dr. W.M. Stewart, 2002, W.A. Norvell et al., 2000) [82, 83] have been

investigated.

From the Literature survey very few works have been done using embedded

system to perform the analytical studies which consists of Microcontroller, having many

peripherals on chip that reduces the cost, size and increases the reliability and

performance.

1.4 EMBEDDED SYSTEM

Modern analytical instruments generally employ one or more sophisticated

electronic devices such as op- amp, integrated circuits, ADC, DAC, counters,

microprocessors and computers. An embedded system is some combination of hardware

and software, either fixed in capability or programmable, that is specifically designed for

a particular function. Industrial machines, automobiles, medical equipment, cameras,

household appliances, airplanes, vending machines and toys (as well as the more obvious

cellular phone and PDA) are among the myriad possible hosts of an embedded system.

9

Embedded systems are controlled by one or more main processing cores that are typically

either microcontrollers or digital signal processors (DSP) [84]. The key characteristic,

however, is being dedicated to handle a particular task, which may require very powerful

processors. Since the embedded system is dedicated to specific tasks, design engineers

can optimize it to reduce the size and cost of the product and increase the reliability and

performance.

Physically, embedded systems range from portable devices such as digital

watches and MP3 players, to large stationary installations like traffic lights, factory

controllers, or the systems controlling nuclear power plants. Complexity varies from low,

with a single microcontroller chip, to very high with multiple units, peripherals and

networks mounted inside a large chassis or enclosure. Home automation uses wired- and

wireless-networking that can be used to control lights, climate, security, audio/visual,

surveillance, etc., all of which use embedded devices for sensing and controlling.

Characteristics

1. Embedded Systems are designed to do some specific task, rather than be a general

purpose computer for multiple tasks.

2. Embedded Systems are not always separate devices. Most often they are

physically built-in to the devices they control.

3. The software written for embedded systems is often called firmware, and is stored

in read-only memory or Flash memory chips rather than a disk drive. It often runs

with limited computer hardware resources: small or no keyboard, screen, and little

memory.

4. Safety: No harm to be caused.

5. Security: Confidential and authentic communication. Even perfectly designed

systems can fail if the assumptions about the workload and possible errors turn

out to be wrong.

6. Must be efficient:

Energy efficient

Code-size efficient (especially for systems on a chip)

Run-time efficient

10

Weight efficient

Cost efficient

7. Dedicated towards a certain application. Knowledge about behavior at design

time can be used to minimize resources and to maximize robustness.

8. Dedicated user interface. (No mouse, keyboard and screen)

9. Many embedded system must meet real-time constraints.

10. Frequently connected to physical environment through sensors and actuators.

1.5 ARTIFICIAL NEURAL NETWORK

In recent years, Artificial Neural Networks (ANNs) have been used successfully

in various fields like biology, Engineering, pattern recognition, financial analysis and so

on, because of their merits such as self learning, self adapting, good robustness and

capability of dealing with non- linear problems. The parallel and distributed structure of

Neural Networks along with their capabilities of generalization, fault tolerance, adaptive

and associative performance, ability to perform dynamic and real time functions and their

limited requirement of software, ensure their appropriateness for many practical

environmental applications.

Artificial neural network technique is particularly suited for the problem that

involves non-linear interpolation [85]. The basic advantage of ANN is that it does not

need any mathematical model; an ANN learns from examples and recognizes patterns in

input–output data without need for any prior assumptions about their nature and

interrelations. Ability to learn by example makes neural networks flexible and powerful

[86]. Provision of data error tolerance [87] and built-in dynamism makes an ANN model

more attractive.

Artificial Neural Networks have been used in waste water quality monitoring

(Ayan Hore et al., 2008) [88], urban storm water quality prediction (Daniel B. May and

Muttucumaru Sivakumar 2009) [89], to predict the dielectric constant – water content

relationship (Magnus person et al., 2002) [90], to estimate of biological oxygen demand

(Emrah Dogan et al., 2007) [91], to model Nitrate concentration river (Suen . JP et al.,

2003) [92], to predict the water quality parameters of Axio river, Northern Greece

(Diamond Poulou et al., 2005) [93] and to predict COD in waste water (Azedine Charef

et al., 2000) [94].

11

Artificial Neural Networks have been applied to predict the Rate of Corrosion in

stainless steels of nuclear reactor (Hilde M.G. Smets et al., 1995) [95], Mild steel in

acidic media (Wanlin wang and Michael L.Free, 2000) [96], steel 3 in Chloride solution

(Kiselev V.D et al 2006) [97], AISI type 316 L stainless steel (K.V.S. Ramana et al.,

2009) [98], Steel in concrete (Parthiban Thirumalai et al., 2005) [99], Mild steel in 1N

H2SO4, ( S.Saratha and V.G. Vasudha, 2009) [100], Stainless steel type AISI 304 L

(Mandal Sumantra et al., 2009) [101], Austenic stainless steel (H.M.G Smets and W.F.L.

Bogaerts, 1992) [102].

Several studies of corrosion analyses have been made in Chloride media, like

corrosion behavior of mild steel in HCl (Ehterum A. Noor et al., 2008) [103], the

prevention of corrosion of carbon steel in Chloride containing solution (Ayse Tosun and

Mubeccel ergun, 2006) [104], Corrosion performance of reinforcing bars embedded in

concrete structures exposed to Chloride environments ( David Trejoa et al, 2005) [105],

Chloride induced corrosion on steels and concrete (Shahzma J. Jaffer, 2009, GK.Glass

and NR. Buenfeld, 2000, A. O. James et al., 2007) [106, 107, 108] have also been

analysed.

In the present research work, ANN is used to compute the Chloride

concentration in environmental samples of soil, sea water and rate of corrosion of mild

steel.

12

1.6 SCOPE OF THE RESEARCH WORK

The Research work is undertaken for the evaluation of Chloride using the

embedded system and computation of Chloride using neural network in biological and

environmental samples.

The objectives of the Research work

To develop a biomedical analyser using Microcontroller P89C668 for measuring

urinary Chloride.

To analyse the effects of elevated Chloride concentration (hyper chloremia) and

low Chloride concentration (hypo Chloremia) in human body using the measured

urinary Chloride concentration.

To measure Chloride in serum using Microcontrollers, P89C668 (Philips),

PIC16F877 (Microchip) and PSoC-CY8C27443 (Cypress).

To investigate the performances of different Microcontroller based Chloride

analysers.

To determine the Chloride concentration in Oral Rehydration Salts (ORS) - a

pharmaceutical sample, using Microcontroller P89C51RD2.

To check whether the value of Chloride in ORS is within the limit of World

Health Organisation (WHO) certified values.

To explore the Chloride concentration in agricultural soil using the developed

Electrical Conductivity measurement set up.

To study the soil Chloride and its effects on plant growth using the observed

results.

To compute Chloride concentration in soil samples using Artificial Neural

Network with back propagation algorithm.

To predict the concentration of Chloride in sea water using ANN with back

propagation algorithm and Genetic Algorithm based back propagation neural

network.

To compute the Rate of corrosion in mild steel using a three layered neural

network model.

To explain the effect of Chloride, effect of pH and temperature on mild steel

using the computed results.

13

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19

CHAPTER II

MICROCONTROLLERS AND ARTIFICIAL NEURAL NETWORK

This chapter explains the basic concept of measurement, microcontroller

architecture and various features of different Microcontrollers P89C668 (Philips),

PIC16F877 (Microchip), P89C51RD2 (Philips), ATmega32 (Atmel) and PSoC

CY8C27443 (Cypress) which are used to design the embedded based bio-analysers for

the study of Chloride in biological samples. It also explains the basics of Artificial Neural

Network, Back Propagation Algorithm and Genetic Algorithm which are employed in the

computation of Chloride in environmental samples.

2.1 Basic concept of Measurement

The measuring system can be considered to consist of three basic steps. (i) The

primary element that senses the quantity under measurement. (ii) The intermediated

means that modifies suitably the output of primary element and (iii) The end device that

renders for indication possible on a calibrated scale.

The primary element must sense and measure the quantity and sets the limit for

the subsequent finding or achievement. The accuracy of the measured value may be less

than the reality if handled by some intermediate systems. Whatever signal amplitude is

produced by a detector, it is processed in the processing module. After adequate

amplification, there is a decision to be taken about whether to carry out processing with

the signal still in analog form or to convert it to digital form by use of an A/D converter.

This conversion ordinarily given higher precision and accommodates to use

Microprocessors/ Microcontrollers/ Computers for further processing.

2.1.1 Microcontrollers

In this age of information technology it is hard to find a product around us that has

not been manufactured without the use of a computer in some form. While manufacturing

tools and processes are heavily dependent on computers for their control, smaller

appliances are more dependent on a smaller version of the computer. Termed as

microprocessors, these semiconductor devices are the heart of any computer. The

advances in microelectronics have led to the growth of single chip computers for a

variety of applications.

20

A microprocessor is a central processing unit fabricated on a silicon substrate. It is

the heart of every computer, laptop or server. The first portable embedded system built

using the Intel 4004 microprocessor was a calculator. Microprocessors are conventionally

programmed using assembly language. Assembly language refers to the list of

instructions provided to the microprocessor to perform arithmetic/logical operations on a

given set of data. Assembly language is usually converted to machine language, which

consists of binary numbers using software called the assembler. Based on the list of

machine language instructions, a microprocessor would perform the following

operations:

Arithmetic operations such as addition, subtraction, multiplication and division.

Logical operations like AND, OR, NOT, EXOR.

Transfer data between memory locations.

Decision control based on the results of the mathematical/logical operations.

The present trend in microprocessor architecture design is to implement 32 bit

ALU with in-built floating point math co-processors. Pipelined execution based on RISC

(Reduced Instruction Set Computer) architecture and special instructions based on CISC

(Complex Instruction Set Computer) designs are inter-mixed to deliver one billion

instructions per second. The availability of cache memory on the chip combined with

multi million transistors packed in silicon power the latest hand held computing

applications.

These microprocessors do not have built in memory, input or output functions

such as parallel ports or serial ports etc. They are optimized to provide only the raw

arithmetic and logic functions required by the operating system at the highest speed. All

other components required to make the computer such as memory, input/output ports,

serial, parallel, and mass storage are provided by external chips and devices. But a

Microcontroller contains its own memory, parallel ports, clock and very often a good

number of peripheral functions not found on a microprocessor.

A microcontroller consists of a microprocessor, memory and all input/output

peripherals on a single silicon chip. The microcontroller with software loaded in the

internal memory is commonly referred to as an embedded chip. An application such as

the cell phone implemented with the embedded chip as the heart of the system is termed

21

as an embedded system application. Figure 2.1 shows the block diagram of a

microcontroller.

The following features are usually included in the design for a microcontroller:

Arithmetic and logic unit

Memory for storing program

EEPROM memory for non-volatile data storage

RAM memory for storing variables and special function registers

Input-Output ports

Timers and counters

Analog to Digital converter

Circuits for reset, power up, serial programming, debugging

Instruction decoder and timing control unit

Serial communication port

The design for a microcontroller has evolved from the design of an embedded

system application using a microprocessor. In order to implement a small control system

with the microprocessor, peripherals such as timers, input ports, output ports and serial

communication driver hardware need to be interfaced. Over a period of time it has been

found that several control applications can be implemented with the same framework of

interfaces. With the advances in large scale integration of transistors, the microcontroller

evolved into its present form integrating these interfaces onto a single chip solution. But

all the microcontrollers will not have the same peripherals in it. Each one is from

different family and they have different peripherals with in it.

2.1.2 Microcontroller Peripherals

Read Only Memory (ROM): Read Only Memory (ROM) is a type of memory used to

permanently save the program being executed. The size of the program that can be

written depends on the size of this memory. ROM can be built in the microcontroller or

added as an external chip, which depends on the type of the microcontroller.

22

Random Access Memory (RAM): Random Access Memory (RAM) is a type of

memory used for temporary storing data and intermediate results created and used during

the operation of the microcontrollers.

Electrically Erasable Programmable ROM (EEPROM): The EEPROM is a special

type of memory not contained in all microcontrollers. Its contents may be changed during

program execution (similar to RAM), but remains permanently saved even after the loss

of power (similar to ROM).

Special Function Registers (SFR): Special function registers are part of RAM memory.

Their purpose is predefined by the manufacturer and cannot be changed therefore.

Program Counter: Program Counter is an engine running the program and points to the

memory address containing the next instruction to execute. After each instruction

execution, the value of the counter is incremented by 1.

Input/output ports (I/O Ports): In order to make the microcontroller useful, it is

necessary to connect it to peripheral devices. Each microcontroller has one or more

registers (called a port) connected to the microcontroller pins.

Oscillator: It is usually configured as to use quartz-crystal or ceramics resonator for

frequency stabilization. Accordingly, if the system uses quartz crystal with a frequency of

20MHz, the execution time of an instruction is not expected 50nS, but 200, 400 or even

800 nS, depending on the type of the microcontroller.

Timers/Counters: Timer is used to measure the time of the events. If the registers use

pulses coming from external source, then such a timer is turned into a counter.

Interrupt: In order to prevent the microcontroller from spending most of its time

endlessly checking for logic state on input pins and registers, an interrupt is generated. It

is the signal which informs the central processor that something attention worthy has

happened.

23

ADC: Analog to Digital converter is a device which converts the analog input into digital

number which is proportional to the magnitude of the analog input.

DAC: Digital to Analog converter is a device which converts a digital code to analog

signal.

PWM: Pulse Width Modulation is a commonly used technique for controlling power to

inertial electrical devices made practical by modern electronic power switches.

The demand from several industries for a variety of such interfaces with different

precision has resulted in several families of microcontrollers.

Accordingly there are 8 bit, 16 bit and 32 bit microcontrollers in the market today.

In addition to this there are several choices of packages starting from 6 pin

microcontrollers to around 156 pin implementations. The differentiation in the

microcontrollers would be in the presence of the number of I/O ports, availability of

multiple timers, precision of A/D converters and special functions like high speed serial

communications.

24

Figure 2.1 Block diagram of a Microcontroller

25

2.2 MICROCONTROLLER P89C668

Microcontroller P89C668 from ‘Philips’ is a single chip 8-Bit Microcontroller

manufactured in advanced CMOS process and is a derivative of the 80C51

microcontroller family. It contains a non-volatile 64 kbytes Flash program memory that is

both parallel programmable and serial In-System Programmable. It has the same

instruction set as the 80C51. It also has four 8-bit I/O ports, a multi-source, four-priority-

level, nested interrupt structure, an enhanced UART and on-chip oscillator and timing

circuits [1].

The microcontroller P89C668 has 80C51 Central Processing Unit, On-chip Flash

Program Memory and can be programmed by the end-user application (IAP) , Parallel

programmed with 87C51 compatible hardware interface to programmer, Speed up to 20

MHz with 6 clock cycles per machine cycle, Full static operation , RAM expandable

externally to 64 kbytes, 8 interrupt sources, Power control modes, Programmable clock

out and I2C serial interface.

The architecture of Microcontroller P89C668 is shown in figure 2.2.1. It

consists of Port0, Port1, Port2 and Port3, RAM, Register B, Accumulator, stack pointer,

ALU (Arithmetic and Logical Unit), PSW (Program Status Word), SFRs (Special

Function Registers), Timers, PCA (The Programmable Counter Array available on the

Microcontroller P89C668 is a special 16-bit Timer that has five 16-bit capture/compare

modules associated with it. Each of the modules can be programmed to operate in one of

four modes: rising and/or falling edge capture, software timer, high-speed output, or

pulse width modulator), Timing and control unit, Instruction register, Oscillator (XTAL1

and XTAL2 are the input and output, respectively, of an inverting amplifier. The pins can

be configured for use as an on-chip oscillator), program counter and Program Address

register.

The pin diagram of microcontroller P89C668 is shown in figure 2.2.2 and

their pin descriptions are explained in 2.2.1.

26

Figure 2.2.1 Architecture of Microcontroller P89C668

27

Figure 2.2.2 Pin diagram of Microcontroller P89C668

Pin Function Pin Function Pin Function

1 P1.5/CEX2 16 VSS 31 P0.6/AD6 2 P1.6/SCL 17 NIC* 32 P0.5/AD5 3 P1.7/SDA 18 P2.0/A8 33 P0.4/AD4 4 RST 19 P2.1/A9 34 P0.3/AD3 5 P3.0/RxD 20 P2.2/A10 35 P0.2/AD2 6 NIC* 21 P2.3/A11 36 P0.1/AD1 7 P3.1/TxD 22 P2.4/A12 37 P0.0/AD0 8 P3.2/INT0 23 P2.5/A13 38 VCC 9 P3.3/INT1 24 P2.6/A14 39 NIC* 10 P3.4/T0/CEX3 25 P2.7/A15 40 P1.0/T2 11 P3.5/T1/CEX4 26 PSEN 41 P1.1/T2EX 12 P3.6/WR 27 ALE 42 P1.2/ECI 13 P3.7/RD 28 NIC* 43 P1.3/CEX0 14 XTAL2 29 EA/VPP 44 P1.4/CEX1 15 XTAL1 30 P0.7/AD7

28

2.2.1 PIN DESCRIPTIONS

MNEMONIC

PIN NUMBER

NAME AND FUNCTION

VSS VCC P0.0–0.7 P1.0–P1.7 P2.0–P2.7 P3.0–P3.7 RST ALE PSEN

16 38 37–30 40–44, 1–3 40 41 42 43 44 1 2 3 18-25 5, 7–13 5 7 8 9 10 11 12 13 4 27 26

Ground: 0 V reference. Power Supply: This is the power supply voltage for normal, idle, and power-down operation. Port 0: Port 0 is an open-drain, bidirectional I/O port. Port 0 pins that have 1s written to them float and can be used as high-impedance inputs. Port 1: Port 1 is an 8-bit bidirectional I/O port with internal pull-ups on all pins except P1.6 and P1.7 which are open drain T2 (P1.0): Timer/Counter 2 external count input/Clockout T2EX (P1.1): Timer/Counter 2 Reload/Capture/Direction Con ECI (P1.2): External Clock Input to the PCA CEX0 (P1.3): Capture/Compare External I/O for PCA module 0 CEX1 (P1.4): Capture/Compare External I/O for PCA module 1 CEX2 (P1.5): Capture/Compare External I/O for PCA module 2 SCL (P1.6): I2C bus clock line (open drain) SDA (P1.7): I2C bus data line (open drain) Port 2: Port 2 is an 8-bit bidirectional I/O port with internal pull-ups. Port 2 pins that have 1s written to them are pulled high by the internal pull-ups and can be used as inputs. Port 3: Port 3 is an 8-bit bidirectional I/O port with internal pull-ups. Port 3 pins that have 1s written to them are pulled high by the internal pull-ups and can be used as inputs. RxD (P3.0): Serial input port TxD (P3.1): Serial output port INT0 (P3.2): External interrupt INT1 (P3.3): External interrupt CEX3/T0 (P3.4): Timer 0 external input; Capture/Compare External I/O for PCA module 3 CEX4/T1 (P3.5): Timer 1 external input; Capture/Compare External I/O for PCA module 4 WR (P3.6): External data memory write strobe RD (P3.7): External data memory read strobe Reset: A high on this pin for two machine cycles while the oscillator is running, resets the device. An internal diffused resistor to VSS permits a power-on reset using only an external capacitor to VCC. Address Latch Enable: Output pulse for latching the low byte of the address during an access to external memory. Program Store Enable: When executing code from the external program memory, PSEN is activated twice each machine cycle.

29

2.3 MICROCONTROLLER PIC16F877

The Microcontroller PIC16F877 from ‘Microchip’ is a low power, high

performance RISC CPU 8 bit microcontroller. Figure 2.3.1 shows the block diagram of

microcontroller PIC16F877. It consists of 8KW (Kilo Word) of flash programmable and

erasable memory together with 368 bytes of RAM. It has four I/O ports, Timer0(8 bit

timer/counter with 8bit prescaler), Timer1(16 bit timer/counter with prescaler), and

Timer2 (8 bit timer/counter with prescaler)two capture (capture is 16bit, maximum

resolution is12ns), compare (compare is 16 bit, maximum resolution is 200ns), PWM

modules (maximum resolution is 10 bit), synchronous serial port with SPI and I2C,

Universal synchronous asynchronous receiver, parallel slave port 8 bits wide, brown out

detection circuitry for brown out reset. It also consists of parallel ports, chip oscillator,

programmable code protection, 14 interrupt sources, 10 bit 8 channel A/D converter and

low power consumption [1].

The Microcontroller PIC16F877 has 100,000 erase/write cycle enhanced FLASH

program memory typical, 1,000,000 erase/write cycle data EEPROM memory typical,

Self-reprogrammable under software control, Fully static design, wide operating voltage

range (2 to 5.5v), commercial and industrial temperature ranges, Only 35 single word

instructions to learn, operating speed DC-20MHz clock input, DC-200ns instruction

cycle, In-circuit serial programming, Watchdog timer with its own on-chip RC oscillator

for reliable operation, Programmable code protection, Power saving SLEEP mode,

Selectable oscillator options and In-circuit debug via two pins.

The pin diagram of microcontroller PIC16F877 is shown in figure 2.3.2 and their

pin descriptions are explained in 2.3.1.

30

Figure 2.3.1 Block diagram of Microcontroller PIC16F877

31

Figure 2.3.2 Pin diagram of Microcontroller PIC16F877

32


Pin# Pin Name Description

1 ~MCLR 2 RA0/AN0 PORTA.0 /Analog Channel 0 3 RA1/AN1 PORTA.1 /Analog Channel 1 4 RA2/AN2 PORTA.2 /Analog Channel 2 5 RA3/AN3 PORTA.3 /Analog Channel 3 6 RA4/T0CK1 PORTA.4 / External Clock for Timer 0 7 RA5/AN4 PORTA.5 /Analog Channel 4 8 RE0/AN5 PORTE.0 /Analog Channel 5 9 RE1/AN6 PORTE.1 /Analog Channel 6 10 RE2/AN7 PORTE.2 /Analog Channel 7 11 Vdd +3 ~ +5V 12 Vss GND 13 OSC1/CLKIN Oscillator Connection /Clock In 14 OSC2/CLKOUT Oscillator Connection / Clock Out 15 RC0/T1CK1 PORTC.0 /External Clock for Timer 1 16 RC1/CCP2 PORTC.1 /CCP2 17 RC2/CCP1 PORTC.2 /CCP1 18 RC3/SCK/SCL PORTC.3 /SCK(for SPI)/SCL(for I2C) 19 RD0 PORTD.0 20 RD1 PORTD.1 21 RD2 PORTD.2 22 RD3 PORTD.3 23 RC4/SDI/SDA PORTC.4/SDI(for SPI)/SDA(for I2C) 24 RC5/SDO PORTC.5 /SDO (for SPI) 25 RC6/TX PORTC.6 /TX (for Serial Com.) 26 RC7/RX PORTC.7 /RX (for Serial Com.) 27 RD4 PORTD.4 28 RD5 PORTD.5 29 RD6 PORTD.6 30 RD7 PORTD.7 31 Vss GND 32 Vdd +3 V ~ +5 V 33 RB0/INT PORTB.0/External Interrupt 34 RB1 PORTB.1 35 RB2/PGM PORTB.2 /Programming Input 36 RB3 PORTB.3 37 RB4 PORTB.4 38 RB5 PORTB.5 39 RB6/PGC PORTB.6 /Debugger/ICSP 40 RB7/PGD PORTB.7 /Debugger/ICSP

33

2.4 MICROCONTROLLER P89C51RD2

A Single-Chip 8-Bit Microcontroller P89C51RD2 from ‘Philips’ is

manufactured in advanced CMOS process. Figure 2.4.1 shows the block diagram of

microcontroller. It contains a non-volatile 64kB Flash program memory that is both

parallel programmable and serial In-System and In-Application Programmable. In-

System Programming (ISP) allows the user to download new code while the

microcontroller sits in the application. In-Application Programming (IAP) means that the

microcontroller fetches new program code and reprograms itself while in the system.

This allows for remote programming over a modem link. The instruction set is 100%

compatible with the 80C51 instruction set. It also has four 8-bit I/O ports, three 16-bit

timer/event counters, a multi-source, four-priority-level, nested interrupt structure, an

enhanced UART and on-chip oscillator and timing circuits [1]. Boot ROM contains low

level flash programming routines for downloading via the UART, can be programmed by

the end-user application (IAP), 6 clocks per machine cycle operation (standard), 12

clocks per machine cycle operation (optional), Speed up to 20 MHz with 6 clock cycles

per machine cycle (40 MHz equivalent performance); up to 33 MHz with 12 clocks per

machine cycle, Fully static operation, Power control modes, Programmable clock out,

Second DPTR register, synchronous port reset, Low EMI (inhibit ALE) and

Programmable Counter Array (PCA) [1]. Figure 2.4.2 shows the pin diagram of

Microcontroller P89C51RD2 and their pin descriptions are given in 2.4.1.

34

Figure 2.4.1 Block diagram of Microcontroller P89C51RD2

35

Figure 2.4.2 Pin diagram of Microcontroller P89C51RD2

36

2.4.1 PIN DESCRIPTIONS MNEMONIC

PIN NUMBER

NAME AND FUNCTION

VSS VCC P0.0–0.7

P1.0–P1.7

P2.0–P2.7

P3.0–P 3.7

RST ALE

PSEN

EA/VPP

XTAL1 XTAL2

20 40 39–32

1–8 1 2 3 4 5 6 7 8 21–28 10–17 10 11 12 13 14 15 16 17 9 30 29 31 19 18

Ground: 0 V reference. Power Supply: This is the power supply voltage for normal, idle, and power-downoperation. Port 0: Port 0 is an open-drain, bidirectional I/O port. Port 0 is also the multiplexed low-order address and data bus during accesses to external program and data memory. Port 1: Port 1 is an 8-bit bidirectional I/O port with internal pull-ups on all pins except P1.6 and P1.7 which are open drain. As inputs, port 1 pins that are externally pulled low will source current because of the internal pull-ups. (See DC Electrical Characteristics: IIL). T2 (P1.0): Timer/Counter 2 external count input/Clockout (see ProgrammableClock-Out) T2EX (P1.1): Timer/Counter 2 Reload/Capture/Direction ECI (P1.2): External Clock Input to the PCA CEX0 (P1.3): Capture/Compare External I/O for PCA CEX1 (P1.4): Capture/Compare External I/O for PCA CEX2 (P1.5): Capture/Compare External I/O for PCA CEX2 (P1.5): Capture/Compare External I/O for PCA CEX3 (P1.6): Capture/Compare External I/O for PCA Port 2: Port 2 is an 8-bit bidirectional I/O port with internal pull-ups. As inputs, port 2 pins that are externally being pulled low will source current because of the internal pull-ups. (See DC Electrical Characteristics: IIL). Port 3: Port 3 is an 8-bit bidirectional I/O port with internal pull-ups. As inputs, port 3 pins that are externally being pulled low will source current because of the pull-ups. RxD (P3.0): Serial input port TxD (P3.1): Serial output port INT0 (P3.2): External interrupt INT1 (P3.3): External interrupt T0 (P3.4): Timer 0 external input T1 (P3.5): Timer 1 external input WR (P3.6): External data memory write strobe RD (P3.7): External data memory read strobe Reset: A high on this pin for two machine cycles while the oscillator is running, resets the device. In normal operation, ALE is emitted twice every machine cycle, and can be used for external timing or clocking. Note that one. ALE can be disabled by setting SFR auxiliary.0. With this bit set, ALE will be active only during a MOVX instruction. Program Store Enable: The read strobe to external program memory. When executing code from the external program memory, PSEN is activated twice each machine cycle. External Access Enable/Programming Supply Voltage: EA must be externally held low to enable the device to fetch code from external program memory locations. Crystal 1: Input to the inverting oscillator amplifier Crystal 2: Output from the inverting oscillator amplifier.

37

2.5 MICROCONTROLLER ATmega32

ATmega32 Microcontroller from ‘Atmel’ company is a high-performance, Low-

power AVR® 8-bit Microcontroller. The block diagram of microcontroller is shown in

figure 2.5.1. It has advanced RISC architecture, 31 Powerful Instructions, Most Single-

clock Cycle Execution,32 x 8 General Purpose, Working Registers, Fully Static

Operation, up to 16 MIPS Throughput at 16 MHz, On-chip 2-cycle Multiplier, High

Endurance Non-volatile Memory segments,32K Bytes of In-System Self-programmable

Flash program memory,1024 Bytes EEPROM, 2K Byte Internal SRAM [1]. It consists of

32 K Bytes in System Programmable Flash, one 16-bit Timer/ Counter and two 8-bit

Timer/ Counter, an eight Channel 10 bit ADC, 32 programmable I/O lines in four I/O

ports (Port A, Port B, Port C and Port D) and 2k Bytes of SRAM [1]. It has high

endurance non-volatile memory segments, In-System Programming by On-chip boot

program, True Read-While-Write Operation, JTAG Interface, On-chip analog

comparator, power-on reset and programmable brown-out detection, 40-pin PDIP, 44-

lead TQFP, and 44-pad QFN/MLF.

Figure 2.5.2 shows the pin diagram of Microcontroller ATmega32 and their pin

functions are explained in 2.5.1.

38

Figure 2.5.1 Block diagram of ATmega32

39

Figure 2.5.2 Pin diagram of Microcontroller ATmega32

40


VCC: Digital supply voltage.

GND: Ground.

Port A (PA7-PA0): Port A serves as the analog inputs to the A/D Converter. Port A also

serves as an 8-bit bi-directional I/O port, if the A/D Converter is not used. The Port A

output buffers have symmetrical drive characteristics with both high sink and source

capability.

Port B (PB7-PB0): Port B is an 8-bit bi-directional I/O port with internal pull-up

resistors (selected for each bit). The Port B output buffers have symmetrical drive

characteristics with both high sink and source capability.

Port C (PC7-PC0): Port C is an 8-bit bi-directional I/O port with internal pull-up

resistors (selected for each bit). The Port C output buffers have symmetrical drive

characteristics with both high sink and source capability. PC5 (TDI), PC3(TMS) and

PC2(TCK) will be activated even if a reset occurs. The TD0 pin is tri-stated unless TAP

states that shift out data are entered.

Port D (PD7-PD0): Port D is an 8-bit bi-directional I/O port with internal pull-up

resistors (selected for each bit). The Port D output buffers have symmetrical drive

characteristics with both high sink and source capability.

RESET:. A low level on this pin for longer than the minimum pulse length will generate

a reset, even if the clock is not running.

XTAL1: Input to the inverting Oscillator amplifier and input to the internal clock

operating circuit.

XTAL2: Output from the inverting Oscillator amplifier.

AVCC: It is the supply voltage pin for Port A and the A/D Converter. It should be

externally connected to VCC, even if the ADC is not used. If the ADC is used, it should

be connected to VCC through a low-pass filter.

AREF: It is the analog reference pin for the A/D Converter.

41

2.6 MICROCONTROLLER PSoC CY8C27443

Microcontroller PSoC (Programmable System on Chip) CY8C27443 from

‘Cypress’ consists of many mixed signal array with on-chip controller device. Figure

2.6.1 shows the block diagram of microcontroller ATmega32. It is a low cost single chip

programmable device with powerful Harvard Architecture processor. It has low power,

high speed, operating voltages down to 1.0V using On-chip Switch Mode Pump (SMP),

industrial temperature range: -40°C to +85° and 3.0 to 5.25V operating voltage. The 12

Rail-to-Rail analog PSoC blocks provides 14-Bit ADCs, 9-Bit DACs, Programmable

Gain Amplifiers (PGA) and Programmable Filters and Comparators. The 8 digital PSoC

blocks have 8- to 32-Bit Timers, Counters, PWMs, CRC and PRS Modules, 2 Full-

Duplex UARTs, Multiple SPI Masters or Slaves and Connectable to all GPIO Pins. The

precision, programmable clocking of PSoC has internal 2.5% 24/48 MHz Oscillator and

24/48 MHz with Optional 32 kHz Crystal. The flexible On-chip memory of PSoC has

16K flash program storage 50,000 Erase/ Write Cycles, 256 Bytes SRAM data storage

and In-System Serial Programming. Additional system resources of PSoC are I2C Slave,

Master, and Multi-Master to 400 kHz, Watchdog and Sleep Timers and User-

Configurable low voltage detection.

The PSoC consists of digital systems comprising of digital blocks and analog

systems comprising of analog blocks. Using the development tools, library elements can

be configured to provide analog functions (from Analog blocks), such as Programmable

Gain amplifiers, filters, ADCs with exceptionally low noise, input leakage and voltage

offset, DACs, comparators, Modulators, Correlators and Peak detectors. Digital

functions such as timers, counters, PWMs, SPI and UARTs can be configured from the

digital blocks of Digital systems. There is no other microcontroller that has

programmable voltage, instrumentational, inverting, and non-inverting amplifiers.

Hardware generators of pseudorandom and CRC code, as well as analog modulators, are

unique to PSoC families [1]. Figure 2.6.2 shows the pin diagram of Microcontroller PSoC

CY8C27443 and their pin functions are given in 2.6.1.

42

Figure 2.6.1 Block diagram of Microcontroller PSoC CY8C27443

43

Figure 2.6.2 Pin diagram of Microcontroller PSoC CY8C27443

44

2.6.1 CY8C27443 28-Pin PSoC Device Pin Pin Description No. Name 1 P0[7] Analog column mux input. 2 P0[5] Analog column mux input and column output. 3 P0[3] Analog column mux input and column output. 4 P0[1] Analog column mux input. 5 P2[7] 6 P2[5] 7 P2[3] Direct switched capacitor block input. 8 P2[1] Direct switched capacitor block input. 9 SMP Switch Mode Pump (SMP) 10 P1[7] I2C Serial Clock (SCL) 11 P1[5] I2C Serial Data (SDA) 12 P1[3] 13 P1[1] Crystal Input (XTALin), I2C Serial Clock (SCL) 14 Vss Ground connection. 15 P1[0] Crystal Output (XTALout), I2C Serial Data(SDA) 16 P1[2] 17 P1[4] Optional External Clock Input (EXTCLK) 18 P1[6] 19 Input XRES Active high external reset with internal pull down. 20 P2[0] Direct switched capacitor block input. 21 P2[2] Direct switched capacitor block input. 22 P2[4] External Analog Ground (AGND) 23 P2[6] External Voltage Reference (VRef) 24 P0[0] Analog column mux input. 25 P0[2] Analog column mux input and column output. 26 P0[4] Analog column mux input and column output. 27 I P0[6] Analog column mux input. 28 Vdd Supply voltage.

45

2.7 ARTIFICIAL NEURAL NETWORK

2.7.1 Introduction

Neural networks are being applied to an increasing large number of real world

problems. The human brain consists of about ten billion neurons and a neuron is, on

average, connected to several thousand other neurons. By way of these connections,

neurons both send and receive varying quantities of energy. One very important feature

of neurons is that they don't react immediately to the reception of energy. Instead, they

sum their received energies, and they send their own quantities of energy to other neurons

only when this sum has reached a certain critical threshold. The brain learns by adjusting

the number and strength of these connections. This biological fact is sufficiently powerful

to serve as a model for the neural net. The primary advantage of neural network is that

they can solve problems that are too complex for conventional technologies; problems

that do not have an algorithmic solution or for which an algorithmic solution is too

complex to be defined. In algorithmic approach, the computer follows a set of

instructions in order to solve a problem. Unless the specific steps that the computer needs

to follow are known, the computer cannot solve the problem. That restricts the problem

solving capability of conventional computers to problems that we already understand and

know how to solve. The versatility of the problems [2 - 5] in which Artificial Neural

Networks have been used are yielding promising results. ANN implementations have

been widely used over the last years on such applications as aerospace,

telecommunications [6], robotics [7], image processing [7, 8], applied mathematics [9,

10], financial analysis [11], intrusion detection systems and others.

The architecture of ANN (Figure 2.7) generally consists of three layers. An input

layer with input neurons, where the data are introduced, the hidden layer with hidden

neurons, where the data are processed and the output layer with output neurons, where

the results for the given inputs are produced. It also consists of weights between neurons,

a transfer function that controls the generation of output in a neuron, and learning laws

that define the relative importance of weights for input to a neuron [12]. Each neuron

receives a signal from the neurons in the previous layer, and each of those signals is

multiplied by a separate weight value. The weighted inputs are summed, and passed

46

through a transfer function which scales the output to a fixed range of values. The output

of the limiter is then broadcast to all of the neurons in the next layer. So, to use the

network to solve a problem, we apply the input values to the inputs of the first layer,

allow the signals to propagate through the network, and read the output values.

Figure 2.7 Architecture of Neural Network

2.7.2 Transfer Function

The behavior of an ANN depends on both the weights and the input – output

function (transfer function) that is specified for the units.

This function typically falls into one of three categories:

Linear: For linear units, the output activity is proportional to the total weighted output.

Threshold: For threshold unit, the output is set at one of two levels, depending on

whether the total input is greater than or less than some threshold value.

Sigmoid: For sigmoid units, the output varies continuously but not linearly as the input

changes. Sigmoid units bear a greater resemblance to real neurons than linear or

threshold units, but all three must be considered rough approximations.

47

2.7.3 Training of Artificial Neural Networks

A neural network has to be configured such that the application of a set of

inputs produces (either 'direct' or via a relaxation process) the desired set of outputs.

Various methods to set the strengths of the connections exist. One way is to set the

weights explicitly, using a priori knowledge. Another way is to 'train' the neural network

by feeding it teaching patterns and letting it change its weights according to some

learning rule.

We can categorise the learning situations in two distinct sorts. These are:

Supervised learning or Associative learning (Figure 2.8) in which the network is trained

by providing it with input and matching output patterns. These input-output pairs can be

provided by an external teacher, or by the system which contains the neural network

(self-supervised).

Figure 2.8 Training of Neural Network with supervised learning algorithm

Unsupervised learning or Self-organisation in which an (output) unit is trained to

respond to clusters of pattern within the input. In this paradigm the system is supposed to

discover statistically salient features of the input population. Unlike the supervised

48

learning paradigm, there is no a priori set of categories into which the patterns are to be

classified; rather the system must develop its own representation of the input stimuli.

Reinforcement Learning is another type of learning may be considered as an

intermediate form of the above two types of learning. Here the learning machine does

some action on the environment and gets a feedback response from the environment. The

learning system grades its action good (rewarding) or bad (punishable) based on the

environmental response and accordingly adjusts its parameters. Generally, parameter

adjustment is continued until an equilibrium state occurs, following which there will be

no more changes in its parameters. The self organizing neural learning may be

categorized under this type of learning.

Once the relationship between the input pattern and prediction is “learned”, an

expert system might be constructed around the neural network to provide a frame work

for the rules necessary to deduce the final prediction from the “interpreted” pattern. For

example the output from a neural network is numerical, not textural. These numbers need

to be translated into ‘n’ predictions. The rule based expert system provides for

incorporation of the rules needed to interpret the numerical output so as to present the

final predictions in a readily usable form.

Learning during the training stage consists of modifying the values of the synaptic

weights between neurons in such a way that ANN’s output confirms to the targets

suggested by the specific problem. Learning rules are the algorithms according to which

ANNs are trained. Unless the correlation between input data and desired outputs is high,

ANN will not converge [13]. Combining ANN architectures with different learning

schemes, results in a variety of ANN systems.

The neural network model having the steps of database collection, training of the

neural network which includes the choice of architecture, training functions, training

algorithm and parameters of the network; testing of the trained network and using the

network for prediction.

49

2.8 Back propagation algorithm (BPA)

The standard back propagation algorithm is the most thoroughly investigated

Artificial Neural Network algorithm. The success of this algorithm is solving large scale

problems critically depends on user specified learning rate and momentum parameters

and, there are no standard guideline for choosing these parameters.

When the training pattern is given, the network produces some output based on

the current state of it’s synaptic weights. This output is compared to the known output,

and a mean squared error is calculated. The error value is then ‘back propagated’ through

the network and small changes are made to the weights in each layer. The weight

changes are calculated to reduce the error signal. The cycle is repeated until the desired

output is reached. The learning process, or training, forms the interconnection between

neurons and is accomplished by known inputs and outputs, and presenting these to the

ANN in some ordered manner. Due to the interconnection, signals are sent from the

input layer to the output layer through the hidden layer. The intensity of the transmitted

signal is determined by the weight of the interconnections. It is used to properly obtain

the model by iteratively adjusting the values of interconnections between the neurons

while the sum of squared residuals between calculated and expected values are

minimized [14, 15]. The BPA is a supervised learning algorithm that aims at reducing

overall system error to a minimum.

2.8.1 Derivation for Back Propagation Algorithm

1. Propagates inputs forward in the usual way

All outputs are computed using sigmoid there holding of the inner product of the

corresponding weight and input vectors.

All outputs at stage n are connected to all the inputs at stage n+1.

2. Propagates the errors backwards by apportioning them to each unit according to the

amount of error.

Let, xj = Input vector for unit j (xji = ith input to the jth unit)

wj =Weight vector for unit j (wji = weight on xji)

zj = wj .xj , the weighted sum of inputs for unit j.

50

oj = Output of unit j. [oj = (zj)]

tj = target for unit ]

Downstream (j) = set of units whose immediate inputs include the outputs of j.

Outputs = set of output units in the final year.

To update the output for each training, let us consider the error can be denoted by E.

Now we have to calculate the value of for each input weight wji regardless of

where in the network unit j is located

= .

= ji

Furthermore, is the same regardless of which input weight of unit j we are trying to

update so we denote this quantity by δj

Consider the case when j outputs.

We know

Since the outputs of all units k j are independent of wji, we can drop the summation and

consider just the contribution to E by j.

δ j = =

=

51

=

=

=

Thus,

Now consider the case when j is a hidden unit Like before, we make the following two

important observations.

1. For each unit K downstream from j, zK is a function of zj

2. The contribution to error by all units l in the same layer as j is independent of

wji

We want to calculate for each input weight wji for each hidden unit j. Note that

wji influences just zj which influences Oj which influences zk downstream (j)

each of which influence E. So we can write,

Again note that all the terms except in the above product are the same regardless of

which input weight of unit j we are trying to update.

Like before, we denote this common quantity by j . Also note that

, , .

52

Substituting,

Thus,

Formal statement of the algorithm:

Back propagation (training examples, ,ni, nn , no).

Each training example in of the form < x, t >where x is the target vector.

rate (e.g. 0.05) ni nh, no, are number of input, hidden and output

nodes respectively. Input from unit I to unit j is denoted xji and its weight is denoted by

wji

Create a network with ni inputs, nh hidden units, and no output units.

Initialize all the weights to small random values (e.g.9 between – 0.05 and 0.05).

Until termination condition is met, Do.

For each training example < x, t >, Do

1. Input the instance x and compute the output ou of every unit.

2. For each output unit k, calculate

3. For each hidden limit h. calculate

53

4. Update each network weight wji as follows:

2.9 Genetic Algorithm

Genetic algorithms (GAs) are search algorithms based on the mechanics of

natural selection and genetics as observed in the biological world [16]. They use both

direction (``survival of the fittest'') and randomisation to robustly explore a function.

Importantly, to implement a genetic algorithm it is not even necessary to know the form

of the function; just its output for a given set of inputs (Figure 2.9).

Once a neural network model has been created, it is frequently desirable to use

the model backwards and identify sets of input variables which result in a desired output

value. The large numbers of variables and non-linear nature of many materials models

can make finding an optimal set of input variables difficult.

Figure 2.9 Genetic algorithm with inputs and output

Evolution - using genetic algorithms

To start with, it is necessary to encode the parameter set for a model into a

chromosome, Xi. This is a way of expressing the information that allows for various

54

forms of mutation to occur in the parameter set, and consists of a set of genes, [xi1, xi2,

xi3, xi4, ...]. This set of genes, when given to the model as inputs, will give the output fi.

The chromosomes are then ranked according to a fitness factor, Fi, describing how well

they perform relative to expectation and each other. The chromosomes are then allowed

to breed (with likelihood proportional to fitness) and mutate. In practice, evolution occurs

in two ways - crossover and random variation [17, 18]. In this example, mutation would

be represented by flipping a randomly chosen bit. In a neural network optimisation GA,

mutation would involve a small variation - plus or minus - in a randomly chosen gene.

To summarise, the procedures are:

1. Convert the chromosomes to a set of neural network inputs by combining them

with the fixed inputs.

2. Make predictions on each set of inputs, and convert the predictions and

uncertainties to fitnesses.

3. Rank the chromosomes by fitness.

4. Preserve the best chromosome (elitism).

5. Create new chromosomes by uniform crossover.

6. Create one new chromosome at random (within appropriate limits for each gene).

7. Mutate one randomly chosen gene in the entire population by adding or

subtracting a small amount (1% of the training database range for that input). If

the mutation makes the gene non-physical (less than zero in this case), set it to a

default physical value (zero in this case).

8. Return to (1) with the new population.

This Genetic Algorithm is used for the selection of Input nodes to get the best

predicted output.

55

References

1. www.datasheet.com.

2. Dayhoff J., DeLeo J., Cancer Supplement, 91- 8 (2001), 1615-1635.

3. Nayak R., Jain L., Ting B., Proc. 1st Asian-Pacific Congr. Computational Mechanics,

(2001), 887-892.

4. Perantonis S.J., Ampazis N., Varoufakis S., Antoniou G., Neural Processing Lett.,

l.- 7(1998), 5–14.

5. Huang D.S., Ma S.D., J. Intelligent Syst., 9(1999), 1–38.

6. Murillo-Fuentes J.J., González-Serrano F.J., Proc. of the IEEE Int. Conf. on

Acoustics, Speech, and Signal Processing (ICASSP’99). V(1999), 2575-2578.

7. Carnimeo L., Proceedings of the 3rd WSEAS Int.Conf. on Neural Network and

Applications, (2002), 107-111.

8. Rowley H., Baluja S., Kanade T., IEEE Transactions on Pattern Analysis and Machine

Intelligence, 20-1(1998), 23 – 38.

9. Kinderman L., Lewandowski A., Protzel P., Proc. Neural Information Processing,

ICONIP’01, 2(2001), 1075–1078.

10. Huang D.S., IEEE Transactions on Neural Networks, 15-2(2004), 477 – 491.

11. Papadourakis G.M., Spanoudakis G., Gotsiass A., Proceedings of 1st International

Workshop Neural Networks in the Capital Markets, (1993).

12. Suen, J.P., Eheart J.W., Asce M., Journal of Wat. Res. Plan. And Man, 129(2003),

505-510.

13. Anderson D., McNeil G., Kaman Science Corporation, (1992).

14. Kartalopoulos, S.V, Understanding Neural Networks and Fuzzy Logic- Basic

Concepts and Applications, Prentice Hall, New Delhi, ( 2000).

15. Ramana K.V.S, Anitha T., Sumantra Mandal, Kaliappan S., Shaikh H., Sivaprasad

P.V., Dayal R.K., Khatak H.S., Materials and design, 30(2009), 3770-3775.

16. Goldberg D.E., Genetic algorithms in search, optimisation, and machine learning.

Addison Wesley, 1989.

56

17. Shah I., Tensile Properties of Austenitic Stainless Steel, I. Shah, M.Phil. Thesis,

University of Cambridge. (2002)

18. Delorme A., Genetic Algorithm for Optimization of Mechanical Properties,

Technical report, University of Cambridge.(2003)

57

CHAPTER III

MEASUREMENT AND ANALYSIS OF CHLORIDE IN BIOLOGICAL

SAMPLES

This chapter presents the basic principle, design and development of

microcontroller P89C668 based bio-analyser to measure Chloride concentration in human

urine samples. The performance of the developed instrument is investigated by analytical

parameters and the results obtained which includes high (hyperchloremia) and low urine

chloride (hypochloremia) is also discussed. This chapter also explains the measurement

of concentration of Chloride in pharmaceutical sample of Oral Rehydration Salts using

the microcontroller P89C51RD2 based Chloride analyzer. The results are verified with

the WHO (World Health Organisation) certified values and it is found that the values are

well agree with the charted values.

3.1 Renal Physiology

Urine is a liquid product of the body that is secreted by the kidneys by a process

called urination and excreted through the urethra. Cellular metabolism generates

numerous waste compounds, many rich in nitrogen that require elimination from the

bloodstream. This waste is eventually expelled from the body in a process known as

micturition, the primary method for excreting water-soluble chemicals from the body.

These chemicals can be detected and analysed by urinalysis. Amniotic fluid is closely

related to urine, and can be analyzed by amniocentesis. A typical nephron, the functional

unit of the kidney is composed of a capillary bed for filtration, called the glomerulus and

tubule segments located in the cortex and medulla of the kidney. Chloride is both actively

and passively transported in various segments of the tubules. The kidneys are responsible

for the maintenance of total body Chloride balance. Several studies have suggested that

the Chloride ion may play a more active and independent role in renal function,

neurophysiology and nutrition. Body Chloride concentrations are regulated by excretions,

primarily via the kidneys [1, 2]. Normal urine density or specific gravity values vary

between 1.003–1.035 (g cm−3) and any deviations may be associated with urinary

58

disorders [3]. The presence of specific clinical disorders can affect the ability of the

kidneys to maintain Chloride balance. The result is hyperchloremia or hypochloremia.

Colorimetry is one of the simplest and best techniques, which is widely used for

the clinical routine assays compare to that of other analytical methods. It requires the

maintenance, portable and allows on-site testing. The amount of sample required is small.

If a unit cell is utilized through which light is passed, it is generally simple, little in size

and low in cost. Sample testing in the laboratory can take minutes to hours for sample

preparation testing to be run, but colorimetry using hand-held devices can take minutes or

even seconds to run. Hence, an attempt is made to develop a simple, accurate and low

cost embedded based bio-medical analyser based on colorimetry principle.

3.1.1 Colorimetry principle

According to this principle, a colorimeter measures the intensity of light shining

through a coloured solution compared to the intensity of light passing into the solution.

A detector measures the transmittance (T) (% of light passing through) of the solution.

This is mathematically converted to absorbance (A= -log 10 T). The absorbance is

directly proportional to the concentration (Beer-Lambert law) [4, 5]. Beer’s law describes

that the plot of absorbance ‘A’ against concentration, a straight line passing through the

origin should be obtained. The presence of small amount of colorless electrolytes, which

do not react chemically with the colored components, normally does not affect the light

absorption.

3.1.2 Proportionality between color and concentration

For, colorimeters, it is important that color intensity should increase linearly with

concentration of the compound to be determined. This is not necessary for photoelectric

colorimeters or spectrophotometers. Since, a calibration curve may be constructed

relating the instrumental reading of the color with the concentration of the solution. It is

desirable that the system follows Beer’s law even when photoelectric colorimeters are

used.

59

3.1.3 Stability of the color and clarity of the solutions

The color produced must be stable so as to allow accurate readings to be taken.

The periods over which maximum absorbance remains constant must be long enough for

precise measurement to be made. The solution must be free from precipitate if

comparison is to be made with a clear standard. Turbidity scatters as well as absorbs the

light.

3.1.4 Analytical studies of the Instrument system

The following parameters are the important analytical factors to be considered for

the instrument analytical performance.

(i) Accuracy and precision

The accuracy and precision of colorimetric method instrument set up depends on

three major factors.

a) Instrumental limitations

b) Chemical variables

c) Operator’s skill

Instrumental limitations are often determined by the quality of the instruments,

optical, mechanical and electronic systems. Chemical variables are determined by purity

of standards, reagents and reaction rates. These factors are usually determined by the

methodology chosen for the analysis. Under ideal conditions it is possible to achieve

relative standard deviations in concentrations as low as about 0.5%, which enables the

determination of micro quantities of components.

The precision of colorimetric method also depends on concentration of the

determinant. Visual methods generally give results with a precision of 1-10%. The

precision of the method is of course, higher and varies from 0.5 – 2% under suitable

measuring conditions.

Precision describes the reproducibility of results where accuracy denotes the

nearness of a measurement to its accepted value. The precision attainable is a function of

the absorbance measured. The error observed is, as expected very large on lower side of

concentrations. When intensely colored solutions are measured, only an insignificant

part of the radiation is transmitted and on logarithmic absorbance scale the gradations are

so close that the reading error is very high.

60

Precision is conveniently expressed in terms of the average deviation from the

mean or in terms of standard deviation. The standard deviation is the most reliable

estimate of the indeterminate uncertainty. When the standard deviation turns out to be

approximately proportional to the amount present in the formation on the precision can

be expressed in percent by using the coefficient of variation. Mathematical equation for

the coefficient of variation is

C.V = S × 100 / x

Where s – standard deviation

x - Arithmetic mean of a series of measurement.

(ii) Detection Limit

Detection limit is the smallest concentration of a solution of an element that can

be detected with 95% certainty [6, 7]. This is the quantity of the element that gives a

reading equal to twice. The standard deviation of a series of any least ten determinations

taken with solutions of concentrations are close to the level of the blank. Several

approaches for determining the detection limit are possible, depending on whether the

procedure is a non – instrumental or instrumental. Based on the standard deviation of the

bland samples and the slope of the calibration curve of the analyte, the detection limit (D)

may be expressed as:

D = 3.3σ / S

Where σ – standard deviation of the reagent blank.

S- Slope of the calibration curve.

The slope S may be estimated from the calibration curve of the analyte. The estimate of

σ may be measured based on the standard deviation of the reagent blank.

(iii) Comparison of the Results

The comparison of the values obtained from a set of results with either a) the true

value or b) other sets of data makes it possible to determine whether the analytical

procedure has been accurate and / or precise, or if it is superior to another method. There

are two common methods for comparing results: i) t- Test [8, 9] and ii) the variance test

[F-test].

61

(a) t- Test: t- test is used to compare the mean from a sample with some standard values

and to express some level of confidence in the significance of the comparison [9]. It is

also used to test the difference between the means of the two sets of data.

(b) F- test: F – Test is used to compare the precisions of two sets of data of two different

analytical methods are calculated from the following equations [8, 9].

F= SA2 / SB

2

The larger value of S is always used as the numerator so that the value of F is

always greater than unity. The value obtained for F is then checked for its significance

against values in the F- table calculated from the F-distribution [8, 9] corresponding to

the numbers of degrees of freedom for the two sets of data

(iv) Linear regression analysis

Linear Regression Analysis attempts to model the relationship between two

variables by fitting a linear equation that closely fits a collection of data points. All the

points fall into the linear range, and there is sufficient precision in the data to continue

with the linearity study. There will be no outliner for the data sets. The strength of the

linear association between two variables is quantified by the correlation coefficient. The

regression line equation arrived is,

y - y = bxy ( x – x )

Where bxy = r (σx / σy ); x and y are data sets.

x and y are mean of the σx and σy are standard Deviation of the data sets.

(v) Recovery

Recovery is the test performed to check the reproducibility of the instrument. It is

performed by adding the known concentration solution with the known concentration

sample. The resultant concentration should be the addition of those two concentration

values.

(vi) Quantification limit

The quantification limit is generally determined by the analysis of samples with

known concentrations of analyte with those of blank samples and by establishing the

minimum level at which the analyte can be quantified with acceptable accuracy and

precision [10, 11]. Based on the standard deviation of the blank samples and the slope of

the calibration curve of the analyte, the quantification limit (QL) may be expressed as:

62

QL = 10 σ / S

Where σ – the standard deviation of the reagent blank

S – the slope of the calibration curve.

The slope S may be estimated from the calibration curve of the analyte. The estimate of

σ may be measured based on the standard deviation of the reagent blank.

3.2 Measurement of Chloride in human urine using Microcontroller P89C668

3.2.1 System Architecture

The functional block diagram of Microcontroller based Instrument set up to

measure and analyse urinary Chloride with different blocks is shown in figure 3.1. Light

Emitting Diodes (LEDs) as quasi monochromatic light source have been used for

absorbance measurements. Block 1 represents Green LED, which acts as an illumination

source with a dominant wavelength of 480nm. It optically illuminates the sample solution

to measure the Chloride concentration. The photo detection assembly is well insulated

from outer light. Block 2 indicates the sample holder to hold the solutions of blank,

standard, and sample solution tube with a diameter of 1cm. Block 3 is a Photo Diode

GASPG1124 used to detect the amount of light falling on the sample, which acts as a

photo detector. This ideal detector has the characteristics of long term stability, short

response and high sensitivity to allow the detection of low level radiant energy [12]. The

output signals are fed to op-amp LM108 which is kept in Block 4. The LM108 is a

precision operational amplifier from National semiconductor having maximum input bias

current of 3.0 nA, Offset current less than 400 pA and supply current of only 300 μA.

The temperature sensor LM35D kept in Block 5 is a precision semiconductor temperature

sensor giving an output of 10mV per degree centigrade. It is capable of measuring

temperature between +2°C and +100°C. The output is proportional to degree centigrade.

It has low current drain and low self-heating. Block 6 indicates the multiplexer CD4051

to receive the signals from the op-amp and the temperature sensor. Block 7 is heater

controller to control and maintain temperature at 37°C for the incubation of sample. ADC

MCP3201 is represented by the Block 8 to convert Analog to Digital values [13]. IC

MCP3201 is a successive approximation type 12 bit serial A/D converter compatible with

the SPI protocol, sample rate of 100ksps at clock rate of 1.6 MHz and operates over

63

broad voltage range of 2.7-5.5 V. The Microcontroller P89C668, which is kept in Block

9, reads data from ADC for processing. Block 10 represents the keypad to give data for

processing and to compute the Chloride concentration. Block 11 is LCD, used to display

the output of microcontroller (Chloride concentration in Urine samples) [14]. The data

are transmitted from the microcontroller to the computer represented by Block 13 via

RS232 denoted by Block 12.

3.2.2 Instrumental

The microcontroller P89C668 and interfacing circuit to measure urinary Chloride

is shown in figure 3.3. Signal conditioning circuit which is used to isolate, convert and to

amplify the transducer signals is shown in figure 3.2. The photo diode used as a sensor is

connected to the input of op-amp LM108 to measure the amount of light absorbed by the

sample. The current output signal from the photo diode is converted to voltage. The

voltage output of op-amp is connected to the pin 13 of multiplexer CD4051. The

temperature sensor LM35D used to measure the temperature of the sample is connected

to the pin 14 of CD4051 (figure 3.2). The multiplexer (pin 3) is connected to pin 2 of

ADC MCP3201, to convert analog signals to 12 bit digital value. The pin 10 (A) and 11

(B) of multiplexer are connected to pin 18 (P2.0) and 19 (P2.1) of Microcontroller

P89C668 to select either temperature or absorbance of solution. The ADC is interfaced

with pin 42, 43, 44 of Microcontroller P89C668 to convert analog voltage into

corresponding digital values for processing and to compute urinary Chloride

concentration. The heater controller circuit is connected to pin 23 (P2.5) of

microcontroller and it is implemented with opto isolator (IC MOC3041), Triac

(GDA40AT6), to control and maintain the Temperature of sample at 37ºC. A special zero

crossing detector circuit in the optocoupler ensures that the connected triac is only

triggered when the alternating mains voltage goes through zero. During zero crossing

time, commands are given to the heater circuit to switch on/off. Hence, the desired

temperature is achieved. The opto isolator IC MOC3041 (Global suppliers of opto

electronic solutions) has a reverse leakage current of 10 μA, forward voltage of 1.5v and

LED triggering current of 30mA. The triac BTA126 has on-state RMS current of 40

amps and storage temperature range of -40 to 125 ºC. A keypad is interfaced with Port2

64

of the microcontroller as shown in figure 3.3. The LCD is interfaced to display the

results. The data lines (D7 to D0), RS (Register Select) and EN (Enable) of LCD are

connected with Port 0 and Port 3 of microcontroller as shown in figure 3.3. Data from

microcontroller is transferred to PC for further processing through the TXD and RXD

lines of the microcontroller by interfacing RS232 peripheral to achieve the necessary

level shifting for communication between PC and microcontroller. After the power on

reset the program will be executed from memory address 0000 onwards. A 24C16

EEPROM (Electrically Erasable Programmable Read Only Memory) of 16k bit is

interfaced with microcontroller to store the patient i.d and test result (Chloride

concentration). The 24C16 features a low power standby mode which is enabled, upon

power-up and after the receipt of the STOP bit and the completion of any internal

operations. It is a low cost and low voltage 2 wire serial EEPROM. The SCL (Serial

Clock Line) is connected to pin 2 and the SDA (Serial Data Line) is connected to pin 3 of

the microcontroller so that the data can be transmitted and received.

3.2.3 Software

Using C language, software is developed to configure the serial and parallel port to

read data from keyboard, to initialize LCD, to initialize timer1 interrupt, to initialize

enable interrupt, to select light intensity or temperature using multiplexer, to start A/D

conversion, to receive 12 bit data from A/D converter for temperature and light intensity,

to measure and control the temperature for incubation, to measure the voltages for blank,

standard, and sample solutions, to compute absorbance and hence concentration, to store

the data, and to display user information and the results (Keil software is used for the

development of programs). The flowchart for performing the above tasks is shown in

figure 3.4.

3.3 Materials and Method

Samples are collected from the patients whose urinary Chloride has to be

measured. Urine specimen is collected in a container without preservative. The samples

are maintained at 37°C temperature. The solution of blank, standard and sample are

prepared to measure the concentration of electrolyte.

65

3.3.1 Principle of color complex

Chloride is mixed with a solution of undissociated mercuric thiocyanate, the

Chloride preferentially combines with the mercury to form mercuric Chloride. The

thiocyanate that is released combines with ferric ions present in the reagent to form ferric

thiocyanate, which can be measured spectrophotometrically. The mercuric nitrate binds a

fixed amount of Chloride ion and therefore makes them unavailable for reaction with

mercuric thiocyanate. Only the Chloride present in excess of that bound by the mercury

from mercuric nitrate is reacted with mercuric thiocyanate and produced a color complex

(red ferric thiocyanate) that absorbs light at 480 nm. According to Beer-Lambert’s law,

the intensity of the color produced is directly proportional to the Chloride concentration.

[15].

2Cl- + Hg(SCN)2 HgCl2 + 2SCN- (1)

3SCN- +Fe +++ 4Fe (SCN)++ + (2)

3.3.2 Reagents

Two reagents R1 and R2 are used in this method. The reagent R1 consists of

mercuric thiocyanate (2m mol/l), ferric nitrate (20m mol/l) and nitric acid (29m mol/l).

The reagent R2 is Chloride standard solution [16, 17]. All the solutions are prepared in a

well cleaned dried test tube of same diameter. Blank solution is prepared by mixing 1 ml

of reagent R1 with 10 µl of distilled water. To prepare standard, 1 ml of reagent R1 is

added with 10 µl of standard (R2). For sample preparation, 1 ml of reagent R1 is added

with 10 µl of urine sample. The above solutions are thoroughly mixed and left for

incubation for 5 min at 37 ° C, before the absorbance is measured at 480nm.

3.3.3 Measurement

The test tube labeled blank is placed in a sample holder and the measured voltage

is noted as Vo. By holding the standard solution test tube in a sample holder, the voltage

Vstd is taken. To find the absorbance of sample solution, the sample solution is placed in

a sample holder and voltage measured is noted as Vt. The concentration of urinary

Chloride is determined using the formula,

Concentration of Chloride ion = log (Vo/Vt) / log (Vo/Vstd) x 100 (3)

66

Where, log (Vo/Vt) = Absorbance of sample, log (Vo/Vstd) =Absorbance of standard

100 is the concentration of standard Chloride.

The concentrations of urinary Chloride measurements are made for 40 urine

samples using the developed instrument. The same urine samples are tested using the

commercial clinical analyser. The absorbance of sample solution is measured and

repeated for five times to check the reproducibility.

3.4 Results and Discussion

The performance of the Microcontroller P89C668 based electrolyte bio-analyser

using colorimetry principle is investigated by comparing its results with the results

obtained by other clinical analyser (ST 100 analyser) which is given in table 3.1. It can

be seen that there is no obvious difference between the results obtained by two analysers.

3.4.1 Clinical Significance

The normal range of urinary excretion is 20 to 250 milliequivalents per day

(mEq/day). [18]. From table 3.1, it is observed that for patient i.d No, 20, 35 and 39, they

are suffered by hyperchloremia (Increased Chloride levels). This is because of

adrenocortical insufficiency, increased salt intake, inflammation of the kidney that results

in salt loss and Production of an unusually large amount of urine. It is evident that they

are suffered by vomiting (or nasogastric suction), diuretic therapy, or diarrhea due to a

villous adenoma. In this setting, the most likely diagnosis is surreptitious vomiting or

diuretic therapy or one of the causes of mineralocorticoid excess (such as primary

hyperaldosteronism) [19]. The first two disorders induce effective volume depletion,

whereas hyperaldosteronism is usually associated with mild volume expansion due to the

stimulatory effect of aldosterone on sodium reabsorption. It is noted that the drugs that

increases the level of Chloride in the urine are corticosteroids and diuretics (patient i.d

20). It is observed that there are no patients suffered from hypochloremia and it may due

to that the people are taking food regularly with adequate amount of Chloride in their

(NaCl – table salt) daily diet.

67

3.4.2 Statistical Analysis

Statistical analysis is carried out for the results obtained from the developed

instrument to check the accuracy of the instrument. The table 3.2 represents the statistical

reports for the developed instrument and the commercial clinical analyser. It is noted that

the values of standard deviation, mean value, median value, mean dev, Co-efficient of

variation and standard error of Co-efficient of variation for the designed analyser is close

to the clinical analyser. The less residual between the two analysers confirms the

accuracy of the microcontroller based instrumentation set up.

3.4.3 Detection and quantification limits

The detection and quantification limits are calculated as sb + 3s, where sb is the

average signal of blank solutions and s is the standard deviation. For the wavelength of

480nm, the absorbance change of 0.1119 typically corresponds to Chloride concentration

of 30 m mol/l of the sample solution, which gave the sensitivity of the electrolyte bio-

analyser. As the absorbance increases the Chloride concentration also increases, which

shows the linearity of the instrument up to 300 m mol/l.

3.4.4 Precision

To determine the reproducibility and accuracy of the instrument run to run and

within run tests are investigated. Run-to-run precision is obtained by assaying

commercial human control serum AccutrolTM Normal (Sigma – certified value – 102

mmol/l) gave the results of Mean 104.4, S.D. 4.0, C.V (%) 3.3 and elevated results for a

period of thirty (30) days produced the results of Mean 91.7, S.D.3.8, C.V(%)4.1. Within

run precision is obtained by assaying control normal serum twenty (20) times having

Mean 86.9 (104.4), S.D. 1.3 (4.0), C.V. (%) 1.0(3.3).

3.4.5 Recovery

To determine the feasibility of the developed instrument recovery test is

performed in pooled serum by the addition of known amount of Sodium Chloride

solution. Recovery of Chloride added to pool human urine is given in table 3.3. Pooled

urine is diluted approximately two-fold with distilled water. To 0.5ml of diluted urine,

various amounts of 0.1N Sodium Chloride (NaCl) solutions are added to bring the total

Chloride concentration within normal range. Using the developed instrument the

68

recovery values of added Chloride ranged from 98.1% to 99.3% with an average recovery

of 98.65%, which indicates the suitability of the designed instrument for biomedical tests.

3.4.6 Linear regression analysis

Linear regression analysis attempts to model the relationship between two

variables by fitting a linear equation that closely fits a collection of data points. Figure

3.5 shows a linear regression between the designed electrolyte bio-analyser and the

commercial clinical analyser which is used to determine the correlation between the two

instruments. The value of slope 0.97 and the intercept 5.2 (close to ideality) indicated

that the developed instrumentation system is well correlated with the clinical analyzer.

The correlation coefficient between the two method is R=0.99. The implemented

instrument is well suited to determine the concentration of Chloride in urine sample.

69

Table 3.1

Urinary Chloride Concentration (m mol/day)

S.No of

patients

By

developed

instrument

By

clinical

analyzer

S.No of

patients

By developed

instrument

By clinical

analyser

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

102.79

106.55

96.85

82.99

108.58

111.15

120.43

150.12

92.81

130.86

180.14

120.52

200.11

104.09

98.71

182.35

123.53

156.18

230.28

260.92

105

103

101

92

110

120

128

162

102

142

193

134

194

110

92

176

133

142

225

258

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

89.42

156.62

186.87

96.23

145.78

210.45

232.98

88.32

154.84

190.17

100.74

98.15

101.36

115.64

290.85

210.92

84.74

168.12

301.56

142.28

95

144

190

95

140

218

225

85

158

182

93

105

108

111

292

202

78

172

305

152

70

Table 3.2

Statistical Reports for the two analysers

Variable Mean Value

Median Value

Mean Deviation

Coeff of Variance

SE of Coeff of Variation

Sample Std Deviation

Developed Instrument Clinical analyzer

145.75 149.05

127.19

137

44.28 45.46

37.47 37.53

4.18 4.19

54.61 55.94

Table 3.3

Recovery of added Chloride from pooled urine Chloride (mmol/day)

S.No In pooled

Serum

Added Total

Content

Total

Determined

Difference Recovery %

1. 82 40 122 120 2.0 98.3

2. 86 45 131 130 1.0 99.2

3. 90 50 140 138 2.0 98.5

4. 96 55 151 150 1.0 99.3

5. 104 55 159 156 3.0 98.1

71

Figure 3.1 Block diagram of Microcontroller P89C668 based Bio-analyser

72

Figure 3.2 Signal conditioning circuit of the developed Bio-analyser

73

Figure 3.3 Microcontroller and interfacing circuit for the developed Bio-analyser

74

Figure 3.4 Flow chart

75

Figure 3.5 Linear regression between the Microcontroller P89C668 based developed

instrument and clinical analyser

76

3.5 Analysis of Chloride in pharmaceutical sample- Oral Rehydration Salts

3.5.1 Introduction

The human body needs various kinds of salts to be healthy and to function

normally. Serious salt imbalances occur with dehydration, may lead to heart and nervous

system problems that, unless they are rapidly resolved, can result in a medical

emergency. Dehydration is a major cause of electrolyte imbalance in human beings

which occurs whenever water is lost from the body. When fluids are lost, electrolytes in

those fluids are lost too, increasing the risk of electrolyte imbalance. Dehydration can be

caused by heavy exercise, especially in hot weather, severe vomiting and diarrhea. Large

amounts of water and many electrolytes that would normally be absorbed in the intestines

are lost with diarrhea and vomiting. Dehydration is a real threat to children, especially

infants and toddlers. Small children with diarrhea can become seriously dehydrated in

less than one day. Infants can become dehydrated within hours, and Severe burns. Parents

should be alert to dehydration caused by illness or athletic activity and begin oral fluid

and electrolyte replacement therapy immediately. Athletes and trainers know that the

body loses salt through sweat and that it's important to replace it. Hence, many athletes

drink sports drinks that contain salts before, during and after exercise to minimize things

like muscle cramps that are associated with salt imbalance.

3.5.2 Electrolyte supplements

Most people get all the electrolytes from a normal diet. Short-term therapy often

quickly restores electrolyte balances. Persons suffering from dehydration are recovered

by the electrolyte replacement supplements by prescription which can be given by mouth

or intravenously under supervision of a physician [21, 22, 23, 24].

The Dietary supplements are popular among athletes who participate in endurance

sports which can be in the form of tablets and powders. Some also contain herbs and

flavorings. They are regulated by the United States Food and Drug Administration (FDA)

as dietary supplements. Electrolyte replacements for children are Pedialyte, Naturalyte, or

77

Rehydralyte and are available in supermarkets and pharmacies. Children should not be

given sports drinks for vomiting and diarrhea.

Medicinal rehydration sachets and drinks are available to replace the key

electrolyte ions lost during diarrhea and other gastro-intestinal distresses. The science of

Oral Rehydration Salts (ORS) is advanced when Phillips and colleagues determined the

composition of fluid lost in diarrhea [24, 25]. Oral rehydration therapy is proposed as a

viable alternative for cholera in areas of the world with short supplies of intra-venous

fluids and needles forcing clinicians to deliver oral solutions to those with cholera. This

reduced mortality rates to only 3% compared to 30% of those treated in other camps with

intravenous fluids. Based on this evidence, WHO and UNICEF recommended a single

standard ORS formula for all ages. There are lot of brands of ORS therapy is available in

local pharmacy. The accurate analysis of minerals in drugs is very important in

medications.

Hence, a study is undertaken to analyse Chloride in ORS therapy using a

Microcontroller P89C51RD2 and the results are compared with the other analytical

technique (Ion Selective Electrode method).

3.6 Instrumental

3.6.1 Description of the Microcontroller based system

The Microcontroller P89C51RD2 and interfacing circuit is shown in figure 3.6.

The output signals from photodiode are connected to the input of op-amp LM108 (Figure

3.2) for current to voltage conversion and for amplification. The output of op-amp is

connected to the pin 35 of ADC 7109 through 1 MΩ resistance to convert analog to

digital value. ADC 7109 is a low power integrated device providing high accuracy, low

drift and dual slope integrator with 12 bits parallel outputs. The lower and higher bytes

of ADC 7109 are interfaced with port 2 and Port 0 of Microcontroller P89C51RD2 as

shown in figure for computing Chloride concentration. LBEN and HBEN which are used

to select lower and higher byte are connected to pin 36 and 37 of microcontroller. The

RUN and STATUS pins of ADC are connected to 38 and 39 of microcontroller. The pin

29 of ADC 7109 generates the internal reference voltage. A tap from preset is given to

78

pin 36 of ADC 7109, the reference input pin. By adjusting this preset it is possible to get

a full scale, which means that inputs between –4 and +4 can be converted. A reset switch

is provided at pin 9 of the microcontroller, so that the program can be executed from

0000 after the power is switched on. A two row Alphanumeric LCD is interfaced to Port

1 to display the measured concentration of Chloride.

3.6.2 Software

Fully dedicated Software for the data acquisition and computing the Chloride

concentration in different ORS samples is developed in C and assembly language and

linked to the application program. The structure of the software is elaborated as

flowchart in figure 3.7. Software for the implemented system is written to initialize LCD,

to start ADC (Analog to Digital conversion), to check EOC (End of Conversion) to read

lower byte enabling LBEN signal, to read higher byte enabling HBEN signal, to measure

the readings for blank, standard, and sample, to compute absorbance and Chloride

concentration, to display the result in the LCD and to get data from the keyboard.


Oral Rehydration Salts of six different brands are collected from local

pharmaceuticals in Thanjavur, Tamil Nadu, South India. The samples of ORS are

dissolved in 1L of deionized water without any precipitation. The colorimetry principle is

used for the measurement of absorbance and concentration of Chloride electrolyte

The principle and the reagent used to measure Chloride in ORS are same as 3.3.1

and 3.3.2. The sample is prepared by adding 1 ml of reagent R1 with 10 µl of prepared

ORS sample.

3.7.1 Measurement

The prepared solutions of blank, standard and sample are placed in sample holder

to measure the voltages of blank, standard and sample. The absorbance and hence

Chloride concentration is determined using the formula (3) in 3.3.3. The concentration of

Chloride for six different brands of ORS samples is made using the developed

instrument. The same samples are tested using the Chloride Ion Selective Electrode

(ISE). The absorbance of sample solution is measured and repeated for five times to

check the reproducibility.

79


The Microcontroller P89C51RD2 based Instrument set up to measure the

concentration by colorimetry method in ORS samples is designed and developed. The

table 3.4 shows the readings for blank and standard of Chloride reagent. The measured

readings of absorbance and concentration of Chloride in different ORS samples are given

in table 3.5. The table 3.6 gives the concentration of Chloride measured using the

developed instrument and ISE method in different ORS samples. The concentration of

Chloride is varied from (61.87-64 mmol/l). The certified concentration of Chloride in

ORS is 65mmol/l [26]. It is found that the range is well within the safe limits and also it

is observed that there is no significant difference between the concentration values of

different samples, which depicts that they are prepared by following the ORS formula

given by W.H.O. The people who have suffered by dehydration having symptoms like

dry mouth, loss of body weight greater than 10%, extreme thirst, sunken eyes, no tears

when crying, decreased urination, fussiness, weakness, skin that stayed compressed and

pinched should have the habit of in taking ORS to avoid some chronic condition. The

dosage of ORS depends on age and severity of dehydration. The dosage for infants and

children is 1-2 litres over a period of 24 hours. For adults it differs from 2-4 litres over a

period of 24 hours.

3.8.1 Linearity and Sensitivity

As the absorbance increases the Chloride concentration also increases, which

shows the good fit of colorimetry principle. To check the linearity of the developed

instrument set up various samples having different Chloride concentration have been

measured at the wavelength of 480nm. The absorbance change of 0.1 typically

corresponds to Chloride concentration of 42 m mol/l of the sample solution, which gave

the sensitivity of the developed Instrument.

3.8.2 Recovery

To test the feasibility of the procedure and instrument, the recovery of the

developed Instrument is studied by the standard addition method. To 0.5ml of ORS

sample, various amounts of 0.1N Sodium Chloride (NaCl) solutions are added to bring

the total Chloride concentration within normal range. The table 3.7 shows the recovery

80

values of added Chloride ranged from 98.13% to 99.15% with an average recovery of

98.42%, which indicates the suitability of the designed instrument for bulk drugs and

assay tests.

3.8.3 Linear regression analysis

Linear regression is plotted for the results obtained using designed Instrument and

the ISE method which is shown in figure 3.8. The strength of the linear association

between two variables is quantified by the correlation coefficient. The regression line

equation arrived is y = 0.92X+4.43, the value of slope and the intercept indicated that

the developed instrumentation system is well suited to determine the Chloride in ORS

samples. The correlation coefficient R=0.99 (n=6), shows that the designed instrument

is well correlated with the Chloride ISE method.

3.8.4 Statistical Analysis

The statistical reports of Chloride electrolyte in different brands of ORS samples is

given in table 3.8. It is noted from the table that the mean value 63.30 and median value

63.43 are also within the safe limits. The data reported in this study refers that the

concentration levels of electrolytes are fairly within the recommended levels, which

confirms the pharmaceutical integrity. There is no considerable difference between the

results obtained (Mean value, Std Err of Mean value, Median value, Std Err of Median

value, S.D, Std Err of S.D, Mean Deviation, Coefficient of Variation, Std Err of Coeff of

Variation) using the designed instrument and the ISE method which corroborates the

validity of the implemented instrument set up. The accuracy of the microcontroller based

instrument is confirmed by the less residual between the two methods.

81

Table 3.4

Voltage of blank and standard

S.No Blank Vo (mV) Standard (mV) 1

0.22

0.32

Table 3.5

Absorbance and concentration of Chloride in Oral Rehydration Powder

S.No Types of

sample Sample

(VT) Absorbance Chloride

Concentration m mol/l

1 ORS 1 0.175 0.099 61.87 2 ORS 2 0.174 0.101 63.12 3 ORS 3 0.174 0.101 63.12 4 ORS 4 0.173 0.104 64 5 ORS 5 0.173 0.104 64 6 ORS 5 0.175 0.099 61.8

82

Table 3.6

Comparison of results obtained using the developed instrument and Chloride ISE

Table 3.7

Recovery of added Chloride from ORS Chloride (mmol/l)

S.No In ORS sample

Added Total

Content

Total

DeterminedDifference

Recover %

1. 62 40 102 100.5 1.5 98.52

2. 62 45 107 105 2.0 98.13

3. 63 50 113 111 2.0 98.23

4. 63 55 118 117 1.0 99.15

5. 63 45 108 107 1.0 98.07

Chloride concentration (m mol/l) Sample id Developed

Instrument Chloride ISE

ORS 1 61.87 62 ORS 2 63.12 63 ORS 3 63.12 63 ORS 4 64 64 ORS 5 64 64 ORS 6 61.87 63

83

Table 3.8

Statistical analysis for the data arrived using developed instrument and the ISE

Variables Developed Instrument Chloride ISE

Mean 62.99 63.16

Std err of mean 0.356 0.280

Median 63.12 63

Std err of median 0.445 0.350

S.D 0.873 0.687

Std err of S.D 0.252 0.187

Mean deviation 0.751 0.555

Coefficient of variation 1.38 1.08

S.E of Coeff of Variation 0.400 0.314

84

Figure 3.6 Microcontroller P89C51RD2 and interfacing circuit

85

Figure 3.7 Flow chart

86

Figure 3.8 Linear regression between the developed instrument and the Chloride

Ion Selective Electrode

87

References

1. Weast R.C., 3 ed: CRC handbook of Chemistry and Physics 67th ed Boca Raton

FL CRC press (1986).

2. Kuleita T.A., Pediatrics, 78(1986), 714-5.

3. Narins R.G., Emmett M., Medicine., 50(1980), 161–87.

4. Wolfbeis O.S., Fiber Optic Chemical Sensors and Biosensors Analytical Chemistry,

72(2000), 81-89.

5. Rakow N.A., Suslick K.S.A., Nature, 406(2000), 710-712.

6. Green J.M., Anal.Chem., News& features, (1996), 305.

7. Renger B., Jehle H., FisherM., Funk W., J.Planar Chrom., (1995), 269.

8. Skoog D.A., West D.M., Holler F.J., Fundamentals of Analytical chemistry,

Saunders college publishing, Philadelph1ia, 7th Edn., (1996).

9. Jeffery G.H., Bassett J., Mehdham J., Denney R.C., Vogel’s Text book of

quantitative chemical analysis, 6th Edn., (2000).

10. Vessman J, J. Pharm. Biomed. Anal., 14(1996), 867.

11. Marr D., Horvoth P., Clark B.J., Fell A.F., Anal Proceed., 23(1986), 254.

12. Cooper W.D., Electronic Instrumentation and Measurement techniques, Practice hall

of India private Ltd, New Delhi.(1987).

13. Wobschall D., In Circuit design for Electronic Instrumentation -Analog and Digital

devices from sensor to display, Second edition, McGraw-Hill Book Company, NY,

(1987), 367-368.

14. Morvis Alan S., Principles of Measurement and Instrumentation, New York. (1988).

15. De Jong E.B., Goldschmidt H.M., Van Alphen A.C. , Loog J.A., Clin Chem, 26-8

(1980),1233-1234.

16. Yokoi K., Biol Trace Elem Res., 85-1(2002), 87-94.

17. Feldkamp, J Chem Clin Biochem., 12(1974), 146-150.

18. Bishop M. L., Clinical Chemistry: Principles, Procedures, Correlations

(4th ed.). Philadelphia, PA: Lippincott Williams & Wilkins. (2000).

19. Rose B.D., Post T.W., Clinical Physiology of Acid-Base and Electrolyte Disorders,

5th ed, McGraw-Hill, New York, (2001), 565-567.

20. Kotchen T., Hypertension, 45(2005), 849–850.

88

21. Armon K., Stephenson T., MacFaul R., Arch Dis Child., 85(2001), 132-142.

22. Lieberman S., Bruning N., The Real Vitamin & Mineral Book, Avery Publishing,

Garden City, NY, (1990), 91-181.

23. Harrison H.E., Pediatr Clin North Am, 1(1954),335-348.

24. Phillips R.A., Ann Rev Med, 19(1968),69-80.

25. Hirschhorn N., Greenough W.B., III., Cholera. Sci Amer., 225(1971),15-21.

26. World Health Organization. Oral Rehydration Salts (ORS), A new reduced

Osmolarity formulation (Version current at September 12 (2006)).

119

CHAPTER V

ELECTRICAL CONDUCTIVITY AS A SURROGATE FOR CHLORIDE

DETERMINATION

This chapter presents the design and development of Microcontroller

ATmega32 based Instrument set up to determine the Electrical Conductivity in soil

samples. The Electrical Conductivity of the soil samples measured by the implemented

embedded based Electrical Conductivity instrument is comparable with that of the

commercial instrument (ELICO CM 180). Since, Electrical Conductivity can be used as

a surrogate for Chloride concentration measurement, a linear regression model is

developed between Electrical Conductivity and Chloride concentration of soil samples.

The Chloride concentration of the soil samples collected at various samples and their

plant cultivation is also discussed.

5.1 Introduction

Electrical conductivity is an inherent property of most materials, and ranges from

extremely conductive materials like metals to very non-conductive materials like plastics

or glass. In metals, the electrical current is carried by electrons, while in water it is

carried by charged ions. In both cases, the conductivity is determined by the number of

charge carriers, how fast they move and how much charge each one carries. Conductivity

(G), the inverse of resistivity (R) is determined from the voltage and current values

according to Ohm's law. Using Ohm’s Law, V= iR and knowing conductivity G=

(1/R)*k, where k=cell constant =length (d, spacing between two electrodes)/area of

electrodes then G can be determined as G= (1/R)*k = (i/V)*k. When resistance is

measured in ohms, conductance is measured in siemens (formerly known as a mho).

Since 1 siemens is a very large unit, aqueous samples are commonly measured in micro

siemens μS. Metals are extremely conductive because electrons move almost with the

speed of light, while in water ions move much slower and the conductivity is much

lower. Raising the temperature makes water less viscous and the ions can move faster.

Because the ions are of different sizes and carry different amounts of water with them as

they move, the temperature effect is different for each ion.

120

5.1.1 Electrical Conductivity as a surrogate for Chloride concentration

Electrical Conductivity (EC) is usually a representation of salinity and it can

be measured with a simple device. Chloride ion is an important element among dissolved

solids which can limit plant growth, decrease yields and reduce quality of drinking water.

Chloride is highly soluble and remains in the soil solution, while other ions such as

sulphate and bicarbonate combine with calcium and magnesium, are present, to form

calcium sulphate and calcium carbonate, which are sparingly soluble compounds.

Chloride concentration typically is measured by titration of aqueous samples using

standard AgNO3

solution. Chloride analysis thus is time consuming and expensive,

compared to EC measurement which is fast and inexpensive (Hajrasuliha, S et al) [1].

Since Chloride is a major constitute of saline waters and soils and it directly affects EC, a

close correlation between EC and Chloride can be obtained. Maas et al., [2] suggested

that if Chloride is the predominant anion in a soil solution, the Chloride concentration in

molm-3

would be approximately equal to 10 times the EC

measured in dSm-1

.

Observations of Chloride concentration and Electrical Conductivity delivered the

possibility of a generelised empirical relationship between these two factors. Therefore it

is conceivable that the Chloride concentration can simply be estimated from Electrical

Conductivity measurement. The concept of computation of Chloride concentration is, to

correlate Electrical Conductivity with the Chloride Concentration of the soil samples

using regression model, which validates Electrical Conductivity as a surrogate for

Chloride estimation.

5.2 Agricultural soil

All soils contain some water soluble salts which include essential nutrients for

plant growth. When the level of water soluble salts exceeds a certain level, harmful

effects on plant growth occur. A soil with excess total soluble salts is referred to as a

saline soil. The influence that a certain level of soluble salt will have on crop growth

depends upon several factors such as climatic condition, soil texture, distribution of salt

in the profile, salt composition and plant species ( Milne, R.A et al) [3]. The areal extent

and depth of a salt problem is usually irregular. Soil sampling on a grid system may be

necessary to map the extent of the problem. Soluble salts are most commonly detected by

measuring the soil solution’s ability to conduct an electric current, referred to as

121

Electrical Conductivity. The common unit of measurement for EC has been mmhos/cm.

The official international unit of measurement is siemen/ m (S/m). One mmhos/cm is

equal to 0.1S/m or 1.0 ds/m.

5.3 Design and development of Electrical Conductivity measurement set up using

Microcontroller Atmega32

Soil Chloride analysis has been conducted primarily for the purpose of salinity

and irrigation management. Presently, it has become highly advantageous to carry out

information processing and control using microcontroller. It is also a well known fact that

the microcontroller system can offer high accuracy and high speed response. Hence, these

reasons infuse a strong motivation for the design and implementation of the automatic

measurement system based on microcontroller. An embedded systems controlled by

microcontrollers consist of not only a digital part, used for control and data processing,

but also an analog part mostly used for adjustment of input signals e.g. from sensors. In

this experimental study, to decrease the test cost, it is proposed to use ATmega32

microcontroller mounted in the system and the developed system is used for in-situ

measurement of the conductivity.

5.3.1 Design of the measurement system

The block diagram of microcontroller based Electrical Conductivity measurement

set up is shown in figure 5.1. The conductivity cell made up of platinum which is used to

measure the conductivity of the samples is kept in Block 1. The cell constant (K) is

related to the physical characteristics of the measuring cell. K is defined for two flat,

parallel measuring electrodes as the electrode separation distance (d) divided by the

electrode area (A). Thus, for a 1 cm cube of liquid, K = d/A = 1 cm–1. Its determination is

much more convenient by calibration with pattern solutions (Braunstein et al.,) [4].

Polarizing the conductance cell by an external DC potential VDC produces some

undesirable effects (double-layer capacitance, electrolysis, ohmic resistance and

electrolytic saturation). On the other hand, it is proved that the electrolytic saturation is

reduced considerably if the AC polarization frequency is high enough. Hence, a fixed

sinusoidal excitation voltage of 1V is applied to the bridge. The Conductivity cell is

connected to one arm of a modified Wheatstone’s bridge network. The Block 2 consists

of precision rectifier to rectify the output of Bridge network. Block 3 represents the

122

temperature sensor Chromel alumel thermocouple to measure the temperature of the

sample. The effect of temperature is important when an electric conductivity of a liquid

or solution must be done. A solution at a higher temperature will present higher quantity

of ions dissociated, therefore the concentration of electric charges will raise and as a

consequence, conductivity will be higher. On the contrary, the same solution at low

temperature will have a low conductivity due to the low quantity of ions present, which

results in lower electric conductivity. From an application point of view, conductivity is

given at a certain temperature, which has been stated as a reference to better compare the

measurements at different times and locations. This temperature is usually 25°C. Block 4

indicates keypad to give input data to the Microcontroller for processing. Block 5 consists

of ATmega32 microcontroller from Atmel company, is a low power, high performance 8

bit AVR microcontroller. Block 6 is a four rows twenty characters LCD (Liquid Crystal

Display) from Hitachi, to display the experimental results. Block 7 consists of MAX232

(dual RS232 transmitter/receiver interface), which is used to communicate with PC kept

in Block 8.

5.3.2 AC modified Wheatstone bridge network

A modified AC Wheatstone bridge network is shown in figure 5.2. The most

accurate methods of measurement of unknown impedance are the bridge methods, whose

accuracy is basically limited only by the accuracy of the known values of the various

elements constituting the bridge. A modified approach of the balancing techniques of AC

Wheatstone’s bridge network has been reported to achieve high accuracy in

measurement. In the developed Instrument, two high gain operational amplifiers

(CA3041) IC1 and IC2 are connected with the bridge network with the non-inverting

terminal connected to the ground circuit. The bridge output nodal points B and D almost

at the same potentials with respect to the ground and hence the effect of stray capacitance

that will exist between them and also between them and ground is assumed to be

minimized. Since, B and D are at virtual ground, the sinusoidal supply voltage,

V= V sin ωt, the current through the bridge impedances are Z1, Z2, Z3 and Z4

respectively. The output voltage of the circuit is [5]

Vo= Rf [Z2Z3 – Z1 Z2] V (1)

123

At balance condition of the bridge, Vo=0 which is identical with the conventional bridge

network. The conductivity cell is connected instead of Z3. The conductivity of a sample

is determined by,

Vo= Bridge output Voltage, Vi= input excitation voltage, Z1, Z2, Z4=known resistances,

Rf =Feed back resistance and Gc = Conductivity of a sample.

The output of the Amplifier IC2 is given to input of the precision rectifier constructed

with operational amplifiers IC3 and IC4 as shown in Figure 5.2.

5.3.3 Microcontroller and interfacing circuit

The circuit diagram of the Microcontroller based instrumentation set up to

measure the Electrical Conductivity of the sample is shown in figure 5.3. In the designed

circuit, the output from the modified Wheatstone’s bridge network (figure 5.2) is given to

pin 38 (ADC 2) of microcontroller. The Thermocouple which is used to measure the

temperature of the sample is connected to pin 39 (ADC 1) of Port A. The signal

generated by the junction of the thermocouple due to thermal changes is fed to an

amplifier circuit specially designed for very low signal amplification as shown in figure

5.4 (Temperature measurement). The output signal is amplified to a suitable level by

using an instrumentation amplifier read by the microcontroller through A/D converter. A

semiconductor temperature sensor AD590 is used to simulate a reference junction

(Neelamegam et al., 1992) [6]. A crystal oscillator of 8MHz is connected between pin 12

and 13 of microcontroller as shown in figure 5.3. Three keys are connected to PC0, PC1,

and PC2 Port C. A four rows twenty characters LCD is connected with Port D, to display

the measured data and the computed results. MAX232 (dual RS232 transmitter/receiver

interface) is connected with pin 14 and 15 of Port D to transmit/ receive data from PC.

5.3.4 Software

Software is developed in C and assembly language to initialize LCD, to start

ADC, to check End of Conversion, to read 10bits of data from ADC, to measure the

temperature, to compute Conductivity, to compute Chloride concentration using

124

regression model, to display the results in LCD and to send data to PC for further

processing. The flowchart for performing the above tasks is shown in figure 5.5.


5.4.1 Sampling Field

Soil samples are collected from thirty paddy field sites originated from

Thanjavur- Nagapattinam Delta districts, TN, South India, where rice is the main crop of

several agricultural products (Figure 5.6). The field chosen is located on the eastern coast

of TN, between 9o 50’ and 11o 25’ of the north latitude and 78o 45’ and 70o 25’ of the

East. The samples are collected at every 3 kms from Thanjavur to Nagapattinam over

90kms, during the major cropping season of spring-summer (March- June), which

produces about 56% of the Nations total production.

5.4.2 Sample Collection

Sampling areas of paddy fields are selected by avoiding tracks, drainage lines,

sheep camp, or influences other than effluent irrigation. During collection, the size of the

sample (Volume or weight), identification of sample (unique labeling), special packaging

and storage are noted. After collection of the samples, they are air-dried to remove

moisture. Samples are commonly collected from the soil surface or from boreholes

drilled with a hollow stem auger equipped with a split-spoon or core barrel sampler. The

sample is placed in a mixing bowl and organic matter such as roots discarded. Rock and

gravel larger than small pebbles are commonly removed. Homogenize the sample by

thoroughly mixing it prior to weighing or placement in a sample jar (if laboratory

analysis is to be performed). To the extent possible the material placed in a sample jar for

laboratory analysis should be as much like the sample selected for field determination. In

addition coordinate with the analytical laboratory to ascertain if they have a standard

protocol for selection of small volume samples (e.g. a maximum size of pebbles in the

sample).

5.4.3 Sampling procedure

The collected soil samples are assigned a number, transferred to a paper bag,

and then placed in a metal tray. These samples are dried overnight in a cabinet equipped

with a heating element and an exhaust fan to remove moisture. The temperature in the

cabinet does not exceed 36oC in order to approximate air drying conditions. Samples are

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crushed with a mechanical grinder equipped with porcelain mortar and stainless steel 10

mm mesh sieve to remove larger clods and unwanted debris. Since the material from the

particle sizing 2mm and smaller are most important in making an inventory of the

mineral constituents of the soil and in evaluating EC, the sample is crushed again and the

particles sizing 2mm and less than 2mm are sieved using 2mm mesh. The sample is

prepared as given below to measure the conductivity. Three 10 gram scoops of soil and

30 ml of deionized water are taken in a large test tube and shaken well for 30 min to get

1:1 suspension of soil sample. After initial shaking, the suspension is allowed to stand,

with intermittent shaking for 30 minutes [7]. The supernatant solution is then filtered and

it is used for the measurement of Electrical Conductivity.

5.4.4 Measurement

To measure the Electrical conductivity of the sample, the conductivity cell is

connected at one arm of the modified AC Wheatstone bridge and selecting the resistance

value Z1 (100 Ω or 1 kΩ), Z2 (1 kΩ), and Z4 (1 kΩ). A fixed sinusoidal excitation

voltage of 1V with frequency 1 KHz is applied to the bridge. For the calibration of the

instrument, the known concentrations of NaCl are prepared and the Conductivity is

measured before using the Soil samples. The solution is maintained at 25° C. Initially, the

Conductivity cell is kept in the solution of NaCl having concentration of 0.1N, and the

Electrical Conductivity is measured using the developed Instrument. Then the probe is

washed with deionised water and the Electrical Conductivity for various concentrations

of NaCl (0.2N, 0.3N, 0.4N and 0.5N) is measured using the microcontroller based

instrument. The calibration curve is drawn between NaCl concentrations versus

Conductivity (figure 5.7). Then, the conductivity cell is immersed in the prepared soil

sample and the measurements are made for Electrical Conductivity. A graph is drawn

between soil samples and their corresponding Electrical Conductivity as shown in Figure

5.8. At the same time, Chloride concentration of the prepared soil samples is determined

using the titration method for the development of regression model.

5.4.5 Development of regression Model

Regression analysis is the statistical technique that identifies the relationship

between two or more variables. The technique is used to find the equation to evaluate the

relation between the two and to predict the unknown value from the developed equation.

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A simple regression analysis can show that the relations between an independent variable

X and a dependent variable Y is linear, using the simple linear regression equation.

Y= a + mX (where a is an intercept and m is a slope).

The Chloride concentration of the sample is strongly related to the Electrical

Conductivity of the sample, a relation between them is evaluated using linear regression

model (using software ULTIMACALC). It can be seen that from the figure 5.9, as the

Chloride concentration increases the Electrical Conductivity of the soil sample also

increases. The regression line equation is arrived by using the following statistical

equation,

y - y = b xy ( x- x )

where b xy = r ( x / y ).

The regression line equation y = -517.80+14.02x is obtained for that line, using the above

relation. The concentration of Chloride for the soil sample can be determined if the

conductivity of the soil sample is known. The correlation coefficient between the

Electrical Conductivity and the Chloride concentration is R= 0.96 (n=30) which enables

the value of Electrical Conductivity can be used as a surrogate for Chloride estimation.

A regression line also drawn between the Electrical Conductivity measured using

the developed instrument and the commercially available ELICO CM 180 instrument to

check the correlations between the two, which is shown in figure 5.10. The regression

line equation arrived is y= 5.85 + 0.94x, and correlation coefficient R = 0.97 (n=30).

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The developed microcontroller based instrument is used to measure the

Conductivity of the soil samples. The empirical relationship between the Electrical

Conductivity and the Chloride concentration has been developed using linear regression

model to determine the Chloride concentration of the sample. The performance of the

designed instrument is investigated by comparing the results with the standard instrument

(ELICO CM 180).

5.5.1 Analytical performance of the system

The calibration curve is obtained by plotting the concentration of Chloride against

the Electrical Conductivity of prepared NaCl Solutions at various concentrations as

shown in figure 5.7. The reproducibility of the instrument is tested by taking five

replicate readings for soil sample and it is found to agree well within the limits. The

statistical analysis is made for the results obtained using the developed instrument and the

standard instrument to compare the relative accuracies in average Electrical Conductivity

for soil samples. The values (n=30) of Mean=72.06, Median value=62, Standard

Deviation= 20.45, Coefficient of Variation =28.38 and Standard Error of Coefficient of

variation= 3.66 for the Conductivity measured using the designed instrument and for the

standard one, the values of mean=73.83, Median value=64, S.D=19.69, Coeff of

Variation= 26.68 and Std Err of Coeff of variation= 3.44 which shows that there is no

significant difference between the two methods. The accuracy of the developed

instrument is confirmed by the absence of large exceptional errors which allows the

suitability of microcontroller based instrument for Electrical Conductivity measurement.

5.5.1 Analysis of Chloride concentration at various locations and plants

In this study, it is observed (Figure 5.8) that the range of Electrical Conductivity

of soil samples varies from 45mS/cm to 109mS/cm and the Chloride concentration

(Figure 5.11) is maximum at Ramarmadam (1010 ppm) and minimum at Kattuthottam

(140ppm). It is also to be noted that all the collected paddy field soil samples are having

the Chloride concentration within the maximum tolerable limit (1050 ppm).

The concentration of Chloride varies from 140ppm to 210ppm for the particular

places like Kattuthottam (140ppm), Pulavarnatham (210ppm), Athanur (210ppm),

Needamangalam (210ppm), and Kilerium (175ppm). It is observed from the literature

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survey (Jing et al., 1992) [8], that the places having Chloride concentration from

100ppm-200ppm are suitable for the cultivation of white potato, peanut, tomato and

sugarcane. Hence, the research report suggests that the above places are suitable for the

cultivation of the above plants with other necessary minerals.

The Chloride concentrations are ranged from 820ppm to 1010ppm for the places

like Aandipalayam (985ppm), Kurukkathi (930ppm), Koothanur (820ppm), Kilvelur

(860ppm), Aazhiyur (980ppm), Ramarmadam (1010ppm), Sikkal (965ppm) and

Nagapattinam (985ppm). These places are suitable for paddy field which is confirmed by

the literature survey (Zhu, Q.S., and Yu,B.S,1991)[9]. It is also observed that the above

places are already cultivating rice only. Hence, it is concluded that the Chloride

concentration is also very important for the paddy growth in addition to other necessary

minerals (rice).

Tomatoes are sensitive to salinity. The test has been conducted to know the effect

of Chloride on tomato plant which is planted at Thanjavur (Tamil Nadu, South India)

having deep, loamy, well-drained soil. The value of soil pH is about 6.2 to 6.8 and the

direct sun light on plant is around 6 hours. The test is performed on the plant during the

ripening stage of tomato fruit. It is observed that irrigating tomato plant with 1N of NaCl

once in a day for the period of 1 month (1.09.2010 to 30.09.2010) increases the total

soluble solids rather than the normal irrigation.

Chloride plays a vital role in stomatal movement in the palm. It is observed that

healthy coconut palms along seashores (Nagapattinam, Tamil Nadu, South India)

Chloride at a concentration of 7-10mg/g DM in their foliage. The optimal Chloride

concentration is usually in the range of 3.8 to 6.4 mg/g.

An embedded based Electrical Conductivity measurement set up has been

designed and developed and a regression model is developed between Conductivity and

Chloride concentration of soil samples. The Chloride concentration of soil samples at

various locations is measured and their plant cultivation is also discussed.

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Figure 5.1 Block diagram of Microcontroller ATmega32 based Electrical

Conductivity instrument set up.

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Figure 5.2 AC modified Wheatstone’s bridge network with precision rectifier

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Figure 5.3 Microcontroller ATmega32 and interfacing circuit

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Figure 5.4 Temperature measurement circuit

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Figure 5.5 Flowchart

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Figure 5.6 Site map

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Figure 5.7 Electrical Conductivity measured for known concentration of NaCl

Solution using developed instrument.

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Figure 5.8 Graph drawn between collected soil samples versus Electrical

conductivity

137

Figure 5.9 Linear regression drawn between Electrical Conductivity versus

Chloride concentration

138

Figure 5.10 Linear regression for Electrical Conductivity of the soil samples drawn

between the developed instrument and standard instrument.

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Figure 5.11 Graph drawn between collected soil samples versus Chloride

concentration

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References 1. Hajrasuliha S., Cassel D. K., Rezainejad Y., 49(1991), 117-127.

2. Maas E. V., Chloride and crop production. (Eds.: T.L. Jackson). Potash and

Phosphate Institute. Atlanta, GA, (1986), 4-20.

3. Milne R.A, E.Rapp, Soil salinity and drainage problems, Canada

Dept. of Agric, Ottawa, (1968), 1314

4. Braunstein J., Robbins G.D., Journal of Chemical Education, 48 -1 (1981), 52–59.

5. Rajendran A., Neelamegam P., Measurement, 35(2004), 59–63.

6. Neelamegam P., Padmanabhan K., Selvasekarapandian S., Meas. Sci. Technol,

3(1992) 581.

7. Recommended Chemical Soil Test Procedures for the North Central Region,

North Central Regional Publication No. 221. NDSU Bull. No. 499, (1988).

8. Jing A.S., Guo B.C., Zhang X.Y., Chin.J.Soil Sci, 33(6) (1992), 257-259.

9. Zhu Q.S., Yu B.S., Wubei Agric. Sci. 5(1991), 22-26. (Chinese)