Ijsrtm vol 2 (1) january 2014

Vol 2 (1) January 2014

International Journal of Students

Research in Technology & Management

(IJSRTM)

International Journal of Students Research in Technology & Management

Vol 2 (01), Jan Feb 2014, ISSN 2321-2543, pg 19-25

www.giapjournals.com/ijsrtm/ 19

A RESEARCH ON ANDROIDS TEXT TO SPEECH ENGINE Sabyasachi Patra Prathamesh Patel Karishma Velisetty

Department of Computer Science, NMIMS Institute, Mumbai -India

[email protected], [email protected], [email protected]

Abstract

This paper basically walks through the development of Text-less safe driving, an application that auto

responds to text messages you receive while youre driving. The driving and texting epidemic which has

engulfed the people right since the inception of telephones is not really a good thing when it comes to

safety. This basic application deals with this issue by auto responding according to the users need. It

basically responds automatically (and hands-free) to any text with a message such as Im driving right

now, Ill contact you shortly. Its also an implementation of how Android provides access to some great

working features of the phone, including SMS text processing, database management, text-to-speech, and

the location sensor.

Keywords Text to Speech, Android API, Safe Driving, Mobile Application, Custom-user-response.

1. INTRODUCTION This application basically deals with the use of an Android API and the injection and manipulation of its

programmatic to make an application which benefits the society. The three most important features of this

application are that the User can change the response for different situations, the application speaks the text

aloud and the response message can contain the current location of the recipient of the message (the one

driving). The aforesaid mentioned features are just outlines which have been described and explained in detail in

the paper.

2. WORKING FEATURES 2.1. The user can change the response for different situations

2.2. The app speaks the text aloud

2.3. Auto Response

3. THE PATH TO CODING 3.1. The Texting component for sending texts and processing received texts.

3.2. An input form for submitting the custom response message.

3.3. The MySQL DB database component for saving the customized message even after the app is closed.

3.4. The Screen.Initialize event for loading the custom response when the app launches.

3.5. The Text-to-Speech component for speaking the texts aloud.

3.6. The Location Sensor component for reporting the drivers current location.

4. GETTING STARTED For this app to work, you need a text-to-speech module, Text-To-Speech Extended, on your phone. This

module is included in Android version 2 or higher, but if you are running an Android 1.x operating system,

youll need to download it from the Android Market. On your phone:

4.1. Open the Market app.

4.2. Search for TTS.

4.3. Select the app Text-To-Speech Extended to install.

Once the Text-To-Speech module is installed, open it to test its features. When it opens, set the default language

as desired. Then select Listen to Preview. If you dont hear anything, make sure the volume on your phone is

turned up.

Table 1: Components and Behaviors

Label Basic PromptLabel1 Let the user know how the app works.

Label Basic ResponseLabel1 The response that will be sent back to the sender

of original text.

Textbox Basic NewResponseTextbox The user will enter the custom response here.

Button Basic SubmitResponseButton The user clicks this to submit response.




Texting Social Texting1 Process the texts.

MySQL DB Basic MySQL DB1 Store the response in the database.

TextToSpeech Other stuff TextToSpeech1 Speak the texts aloud.

Location Sensor Sensors LocationSensor1 Sense where the phone is.

5. APPLICATION TECHNOLOGIES AND ALGORITHMS USED

5.1. The Auto response functionality As discussed before our application has an auto-response functionality which enables it to automatically send a

text message (The response indication) to the person who sends the text while the application is running. Every

component of Android has certain related events/event handlers associated with it due to which it can perform

the functions as the user desires. To enable seamless understanding of the components and their usage to the

reader, we have associated every component with certain behaviour and tabularized its functions such that there

is a better readability and understand ability of the working and flow of our application.

We have also designed the required behavioural models using a User Interface application for Android, STAR

LOGO. The models and behaviours are depicted wherever necessary. Our modelling involves the

COMPONENT, BEHAVIOUR, CATEGORY (whether its logic, an acceptance, a Math function etc.) and the

USAGE. Block-wise components and what they actually do have been depicted below:

Table 2: Block - wise Components

Block type Drawer Purpose

Texting1.MessageReceived Texting The event handler that is triggered when the phone receives

a text.

setTexting1.PhoneNumber to Texting Set the Phone Number property before sending.

value number My Definitions The phone number of the person who sent the text.

setTexting1.Message to Texting Set the Message property before sending.

ResponseLabel.Text Response Label The message the user has entered.

Texting1.SendMessage Texting Send the message.

5.2. Custom Response Functionality The custom response which the user (the person driving) sends to the user has a set of Android events which can

be coded as needed.

Table 3: Block types


SubmitResponseButton.Click SubmitResponseButton The user clicks this button to submit a new response

message.

setResponseLabel.Text to ResponseLabel Move (set) the newly input value to this label.

NewResponseTextbox.Text NewResponseTextbox The user has entered the new response here.

5.3 Storing The Response To A Database While the application is running on the mobile device and f the user enters a custom response, and then closes

the app and re - launches it, the custom response will not appear (instead, the default one will). This behaviour is

not what your users will expect; theyll want to see the custom response when they restart the app. To make this

happen, we needed to store that custom response persistently.




To store data persistently, we need to use the MySQL DB component, which stores data in a database thats

already on the Android device (internal storage). We basically play around with two functions which we have

outlined below:

Store Value and Get Value. The former allows the app to store information in the devices database, while the

latter lets the app retrieve information that has already been stored.

5.3. Our Scheme Of Events 1. Store data to the database each time the user submits a new value.

2. When the app launches, load the data from the database into a variable or property.

We start by modifying the SubmitResponseButton.Click event handler so that it stores the data persistently.

5.4. Retrieving The Custom Response When The App Opens The reason for storing the custom response in the database is so it can be loaded back into the app the next time

the user opens it.

Table 4: Block - types for Custom Response


def variable (response) Definition A temporary variable to hold the retrieved data.

text () Text The initial value for the variable can be anything.

Screen1.Initialize Screen1 This is triggered when the app begins.

set global response to My Definitions Set this variable to the value retrieved from the database.

MySQLDB1.GetValue MySQLDB1 Get the stored response text from the database.

text (responseMessage) Text Plug this into the tag slot of MySQL DB.GetValue, making sure the

text is the same as that used in MySQL DB.StoreValue earlier.

If Control Ask if the retrieved value has some text.

> Math Check if the length of the retrieved value is greater than (>) 0.

5.5. Speaking The Incoming Texts Aloud Android devices provide robust Text to Speech capabilities which have been elaborately explained in the

Android Developers website. This API can be used and manipulated as we want so as to get our texts and related

messages spoken out aloud.

For the working of this app, we need to provide a more complicated message to be spoken, one that includes

both the text received and the phone number of the person who sent it. An incredibly useful function, make text

allows you to combine separate pieces of text (or numbers and other characters) into a single text object.

Table 5: Block - types for Incoming Texts


TextToSpeech1.Speak TextToSpeech1 Speak the message received out loud.

make text Text Build the words that will be spoken.

text (SMS text received from) Text The first words spoken.

value number My Definitions The number from which the original text was received.

text (.The message is) Text Put a period in after the phone number and then say, The

message is.

value messageText My Definitions The original message received.

5.6. Adding Location Information To The Response




In our application, location tracking can be used to convey a bit more information in response to incoming texts.

Instead of just Im driving, the response message can be something like Im driving and Im at XYZ Lane 3.

For someone awaiting the arrival of a friend or family member, this extra information can be helpful.

Android provides the Location Sensor component for interfacing with the phones GPS (or geographical

positioning system). Besides latitude and longitude information, the Location Sensor can also tap into Google

Maps to provide the drivers current street address.

Table 6: Block - types for Location Information


def variable (lastKnownLocation) Definitions Create a variable to hold the last read

address.

text (unknown) Text Set the default value in case the phones

sensor is not working.

LocationSensor1.LocationChanged LocationSensor1 This is triggered on the first location

reading and every location change.

set global lastKnownLocation to My Definitions Set this variable to be used later.

LocationSensor1.CurrentAddress LocationSensor1 This is a street address such as 254, Street

13, Mumbai.

5.7. Sending The Location As Part Of The Response Using certain global variables and the Android APIs this implementation can be achieved.

Table 7: Block - types for Location Response

6. FUTURE SCOPE AND VARIATIONS 6.1. The application can let the user define custom responses for particular incoming phone numbers. Youll

need to add conditional.

6.2. The application can send custom responses based on whether the user is within certain latitude/longitude

boundaries.

6.3. An alarm can be sounded when a text is received from a number in a notify list.

7. CONCLUSION The Application Safe Driving has been developed incorporating certain important features of the Android OS.

The Texting component can be used to both send text messages and process the ones that are received. To

respond to an incoming text, the Texting.MessageReceived handler is programmed. The MySQL DB

component is used to store information persistentlyin the phones databaseso that the data can be reloaded

each time the app is opened. The TextToSpeech component takes any text object and speaks it aloud. The

make text event is used to piece together (or concatenate) separate text items into a single text object. The

LocationSensor component can report the phones latitude, longitude, and current street address.

ACKNOWLEDGEMENT

This research paper is made possible through the help and support from many people, and in essence, all

sentient beings. Especially, please allow me to dedicate my acknowledgment of gratitude towards our college

Librarian Mr. Pradip Das and his team. I would like to thank Mr. Anand Gawadekar of the NMIMS IEEE


make text Text If there is a location reading, build a

compound text object.

ResponseLabel.Text Message Textbox This is the (custom) message in the text

box.

text (My last known location is) Text This will be spoken after the custom

message.

global lastKnownLocation Location Sensor This is a street address such as 254,

Street 13, Mumbai.




Committee due to which we could get all requested references seamlessly without any trouble. Finally, sincere

thanks to our college and Computer Science department for allowing us to enter the field of research in our so

important final year of B.Tech. Special thanks to our H.O.D, Prof. Dhirendra Mishra.

IMAGES INDEX

Fig. 1: Blocks integration diagram for Texting functionality

Fig. 2: Blocks integration diagram for DB Storage functionality




Fig. 3: Blocks integration diagram for Response functionality

Fig. 4: Blocks integration diagram for Location response functionality

Fig. 5: Home Page screenshot




REFERENCES 1. http://www.nsc.org/pages/nscestimates16millioncrashescausedbydriversusingcellphonesandtexting.aspx

2. InstaMapper LLC, GPS Tracker. URL:http://www.instamapper.com

3. F. Wilson and J. Stimpson,Trends in fatalities from distracted driving in the United States, 1999 to 2008. Am J Public Health,100.11 (2010):2213-2219

4. Google Mobile. http://www.google.com/mobile/voice-actions/.

5. Android SDK: http://developer.android.com/sdk/

6. Alienman Tech, Wheres My Droid. URL:http://wheresmydroid.com

7. Maryam Kamvar and Shumeet Baluja. A large scale study of wireless search behavior: Google mobile search. In CHI,

pages 701{709, 2006.

8. http://www.statefarm.com/aboutus/newsroom/20100819.asp

9. T.Dutoit, An Introduction to Text-to-Speech Synthesis. TTS Research team, TCTS Lab, pp. 2-6.

Author Biographies:

Sabyasachi Patra is currently a student of NMIMS Mukesh Patel School of Technology

Management and Engineering. His background throughout school as well as Junior College

has been Science. He has a keen interest in Research, both in the domain of computers and

philosophy; apart from which, Physics and Creative writing are also his primary pursuits of

interest. His areas of interest in Computer Science broadly lie in the fields of Mobile

Computing/implementations, Client server web application development and Computer

Architecture.

Karishma Velisetty is currently a student of NMIMS Mukesh Patel School of Technology

Management and Engineering. Her background throughout school as well as Junior College

has been Science. She has an interest for the process of Software development and Computer

Science theories. Apart from Computers, she has interest in finance.

Prathamesh Patel is currently a student of NMIMS Mukesh Patel School of Technology

Management and Engineering. His background throughout school as well as Junior College

has been Science. His interests in the field of Computer Science lie in the field of application

development and Websites.

International Journal of Students Research in Technology & Management Vol 2 (01), Jan Feb 2014, ISSN 2321-2543, pg 01-05


AUTOMATIC VEHICLE STABILIZATION SYSTEM Gaurav Pednekar

1, Raunak Borwankar

2 and Purva Sawant

3

1, 2, 3 Students, Department of Electronics & Telecommunication,

Don Bosco Institute of Technology, University of Mumbai, India [email protected],

[email protected],

[email protected]

Abstract

The document gives information on controlling the load of heavy duty vehicles while taking turn in order

to avoid tumbling of vehicle due to inertia. Also this system is to control the speed of vehicle during turn

as excess speed during sharp turn can also result in rollover of vehicle, i.e. vehicle flips around its rolling

axis. The system consists of the closed loop control mechanism, in which the on board computer works as

an error detector, which detects any deviation of certain parameters responsible for stability of the

vehicle. Then the necessary control action is performed by the actuators to compensate the deviation in

those parameters.

Keywords- Passive suspension, Active suspension, ABS, hydraulic actuators, Auto-stabilization

1. INTRODUCTION

With the advancement in the automobile technology and development of road networks, travelling with the IC

(Internal Combustion) engine based vehicle is getting much faster as compared to earlier days. But with the

increasing capability of the vehicles to travel with higher speed, risk of road mishap is also increasing.

According to the report published by Ministry of Road Transport & Highway transport research wing of Govt.

Of India in 2012, chart 18 shows that the heavy vehicles like buses, trucks, and tempos share 36.6% of human

causalities in road accidents in the year 2011. Reports have also showed that most of the accidents occur due to

over speeding & hence losing the control on the vehicle.

When we have a look on the data, it is observed that the heavy vehicles are involved more in road accident since

they are not so easy to handle as compared to small vehicles. After understanding the seriousness of the topic, it

becomes necessary to look upon the road safety standards ways to improve it. Education, Enforcement,

Engineering & Emergency care of road victims can save lives of many.

Since we are concerned about the Engineering solution which improves the safety of the heavy vehicle while

driving, we introduce here the Automatic Vehicle Stabilization System.

Figure 1: % Share of persons killed in road accidents Source: Report published by Ministry of Road Transport & Highway Transport

research wing of Govt. Of India, 2012, chart 18, page20

2. CURRENT TECHNOLOGY With the invention of new types of suspensions, travelling through modern vehicle is getting safer. Most of the

automobile manufacturers use the improvised conventional passive suspensions. Still, in certain cases, for heavy

vehicles these suspensions will not work. For example if a truck is loaded with more than its maximum capacity,

and if the truck travels with considerable velocity at sharp turn, its centre of gravity will get shifted away from

its normal position and hence disturbing the equilibrium state. As a result, truck gets pulled away from centre of

curvature (Due to centrifugal force) and truck rollovers on one side since the suspension system has failed.

In spite of having limitations, the passive suspension system is used almost everywhere (exceptions:

MiniMaglev used in Japan & F1 cars which use semi active suspension.) due to its simplicity, cost effectiveness

& ruggedness.

Current technology does not have the capacity to automatically limit the speed if the vehicle is travelling along

the steep turn. There, the current system totally depends upon the driver. If the speed limit is crossed at turn,

then there are high risks of vehicle getting flipped on the other side.



3. REPLACEMENT NEEDED IN CURRENT TECHNOLOGY

The current technology is reliable and tested against harsh circumstances. Still it requires improvement so that

technology can back up the driver. With the development in the microelectronics, a small computer on board

can be installed in the vehicle to monitor all parameters which play a vital role to maintain the stability &

handling of the vehicle. When certain parameter is exceeding the limit, actuators can be driven to compensate

that error. Simultaneously, an alarming system can be incorporated within it.

4. WHAT IS AUTOMATIC VEHICLE STABILIZATION SYSTEM

The system for heavy-duty vehicle will have 3 main blocks:

a. Sensors b. Microcontroller c. Active suspension & Actuators

Figure 2: Block diagram of the Automatic Vehicle Stabilizing System (AVSS)

a. Sensors:

The sensors used in this system are basically active transducers which work on tachometric principle i.e. these

sensors are mounted near shaft of wheels of vehicle. These sensors generate the train of pulses, which has

frequency proportional to the speed of vehicle. These tachometric transducers are available in various types

such as Hall-effect tachometers, & optoisolated tachometers. Considering the ruggedness, Hall Effect sensor is more popular. The above block diagram is of Hall Effect sensor or tachometer. It has one significant

component, called as the Hall probe, and mounted near the wheel of the vehicle, very close to the toothed

structure on the shaft. This probe has solenoid made up of Copper with the magnetic material rod at the centre

of the probe. When supply voltage is applied to the probe, the current starts flowing in the solenoid, developing

the magnetic field around it. The probe carries the magnetic flux & radiates it in the small air gap between the

probe & the toothed gear. Due to the toothed structure & its rotation, probe develops the train of pulses at its

output. The electrical signal developed by the transducer is in the form of the pulses. But, it may contain some

irregularities. So, some signal shaping circuits are required. For this purpose, active Schmitt Trigger can be

used. Now, this clean train pulse is applied to the input of the counter circuit, which will count these pulses and

give the digital output word. The electrical signal developed by the transducer is in the form of the pulses. But,

it may contain some irregularities. So, some signal shaping circuits are required. For this purpose, active

Schmitt Trigger can be used.

Now, this clean train pulse is applied to the input of the counter circuit, which will count these pulses and give

the digital output word.

Figure 3: Block schematic of Hall-effect tachometer Figure 4: The Hall probe

b. Microcontroller (On Board Computer)

The microcontroller AT89C51 is programmed in such way that it monitors on the parameters like velocity of the

vehicle, degree by which vehicle is turning. According to the data, it will take actions to move actuators in such

a way that vehicle is stabilized properly. The controller will also monitor & the speed of vehicle, & control it by



actuators. Considers the digital equivalent value of the parameters like speed & rate of change of angle of front

wheel shaft. If the vehicle is taking the turn, then according to mathematical formula in program, the controller

calculates the amount of force to be delivered to the load which is the function of speed & derivative of angular

displacement with respect to time. The output received from microcontroller will be just 0 or 1 at any of its port

pins according to availability & requirement. The 8051 family has 8 bit ALU, four 8-bit addressable ports, on

chip timer, counter with control word, and interrupt registers. The data from the signal processing circuits is

allowed to enter into the controller. Controller will have program stored in its EEPROM. But, microcontroller

gives out current in the range of 50 to 650 A, which is not sufficient to drive the actuators. So, we use current

amplifiers to drive actuators which are high power devices like motors in actuators & active suspensions.

Figure 5: The AT89C51 Microcontroller

c. Active suspension & Actuators-

i. Active suspension (To manipulate load position)

Active suspensions are electronically controlled suspensions which have capability to vary their stiffness &

damping even if physical dimensions are not changed.

Figure 6: Active suspension systems used for car

On the basis of working principle of operating principle, types of suspension may vary. Such as the heavy

vehicle will need hydraulic suspension, while medium scale can be handled with pneumatic suspension. For

heavy duty vehicle, we consider hydraulic suspensions & actuators. In hydraulics, we use uncompressible &

viscous fluid such as oil, to move piston in or out of the cylinder. So this fluid is pressurised by using electric

pump or motor in the actuator. The motor will start or stop applying pressure according to signal received by

microcontroller. Now, these actuators will regulate their piston movement in such a way that it stabilizes the

vehicle.

Figure 7: The mathematical model and transfer function of Active suspension



Figure 8: Root locus for the active suspension system using above transfer function.

Figure 9: Response of Active suspension system to step input

ii. Actuators (For active breaking) Similar to the active suspension, active breaking uses the same operating principle, i.e. the hydraulics. It consists

of components like master cylinder, piston, valves, & a pump (motor). When vehicle is about to take turn, the

sensor from steering wheel gives triggering input to the system, by which microcontroller in the system

monitors the values of various parameters such as cornering angle, lateral force on the load etc. & if any value

exceeds than its limit, then gradual breaking of wheels is applied. Simultaneously, the electronically operated

fuel injector valve trips engine from fuel supply. Thus, even if operator hits accelerator, engine will not be able

to generate any increase in output. The vehicle hence slows down and becomes steadier. Still if the system is not

able to stabilize the load & chassis of the vehicle, the high power suspension system applies reaction force

against the weight of the vehicle. A similar system is existing in the form of ABS i.e. Anti-locking breaking

system, but it does manipulate the load of vehicle.

5. APPLICATIONS

The system can be used for safe transport of goods & people with more safety than earlier.



In country like India, where road safety standards are not strictly followed, successful design of this system can be used to test reliability & safety of existing vehicles.

The same system is applicable for high speed railways with some changes in configuration.

6. CONCLUSION:

The paper presents the concept of closed loop control system used to maintain stability of heavy vehicle. The

AVSS can improve the handling of heavy vehicles even better. More importantly, it is a solution over the

problem of rollover of heavy vehicles which takes almost thousands of precious lives over the globe.

REFERENCES

1. http://www.designboom.com/technology/worlds-first-fully-regenerative-active-suspension-system/ 2. http://en.wikipedia.org/wiki/Anti-lock_braking_system 3. Principles of Control Systems by M. Gopal 4. http://www.engineershandbook.net/suspension-system-with-matlab-simulink/ 5. http://www.frpatentsonline.com/6338012.html 6. http://en.wikipedia.org/wiki/Rollover

International Journal of Students Research in Technology & ManagementVol 2 (01), Jan Feb 2014, ISSN 2321-2543, pg 14-18

COST OPTIMIZATION OF POST-TENSIONED I- GIRDERDevashree U. Sawant, N.G.Gore, P.J.Salunke, V.G.Sayagavi

[email protected]

Abstract

Nowadays Post-tensioned simply supported pre-stressed concrete (PC) I-girder bridges are widely used bridge system for short to medium span (20m to40m) highway bridges due to its moderate self-weight, structural efficiency, ease of fabrication, low maintenance etc. In order to compete with steel bridge systems, the design of PC I-girder Bridge system must lead to the most economical use of materials.In this present study, cost optimization approach of a post-tensioned PC I-girder is presented. The objective is to minimize the total cost in the design process of the bridge system considering the cost of materials. For a particular girder span and bridge width, the design variables considered for the cost minimization of the bridge system, are top flange width, girder depth, bottom flange thickness, number of cables (i. e. X1, X2, X3,X4 resp.) Design constraints for the optimization are considered according to AASHTO (American Association of State Highway and Transportation Officials) Standard Specifications and IS: 1343-1980. The optimization problem is characterized by having a combination of continuous, discrete and integer sets of design variables. For An optimization purpose Matlab Software with SUMT (Sequential Unconstrained Minimization Technique) is used that is capable of locating directly with high probability the minimum design variables.

1. INTRODUCTIONIn conventional structure design process, the design method proposes a certain solution that is validated by mathematical analysis in order to verify that the problem requirements or specifications are satisfied. If such requirements are not satisfied, then a new solution is proposed by the designer based on his intuition experience .The process undergoes many manual iterations before the design can be finalized making it a slow and very costly process. There is no formal attempt to reach the best design in the strict mathematical sense of minimizing cost. The process of design is relied exclusively on the designers experience, intuition and creativity resulting in high cost in terms of times and human efforts.An alternative to the conventional design method is optimum design. An optimum design normally implies the most economic structure without harming the functional purposes of the structure. An optimization technique transforms the conventional design process of trial and error into a formal and systematic procedure. Pre-stressed concrete (PC) I-girder bridge systems are ideal as short to medium span (20 to 40 m) highway bridges because of their moderate self-weight, structural efficiency, ease of fabrication, fast construction, low initial cost, long life expectancy, low maintenance, simple deck removal, and replacement. Large and important projects containing I-girder bridge structures have the potential for substantial cost reduction through application of optimum design methodology and thus, will be of great value to practicing engineers.Rana et al. (2010) presented Cost optimization approach of a post-tensioned PC I-girder bridge system. A global optimization algorithm named EVOP (Evolutionary Operation) was used. The comparison was made between optimum design and a real life project named Teesta Bridge. This comparison leads up to 35% saving. Ahsan et al. (2012) presented an optimization approach to the design of simply supported, post-tensioned, pre-stressed concrete I-girder bridges. Sami et al(1996) presented a systematic procedure for selecting the most economic girder type and spacing for a given span. They conclude that it is more economical to space the girders at their maximum spacing. Macrae et al(1984) presented a computer approach to the optimal design of structural concrete beams. An optimal design program called OSCON was used for this purpose. The main objective of this study was Minimizing the amount of reinforcement for a given concrete-section, evaluating the effect of steel cost ratios on optimal solutions, Optimizing the section shape of partially pre-stressed concrete beams, Analysis of given designs with respect to all relevant criteria.

2. PROBLEM FORMULATIONA. Design variablesFor a particular girder span and bridge width, a large number of parameters control the design of the bridge such as girder spacing, cross sectional dimensions of girder, deck slab thickness, number of strands per tendon, number of tendons, deck slab reinforcement, configuration of tendons, anchorage system, pre-stress losses, concrete strength etc. the design variables consider in this study are tabulated in Table 1. A typical cross-section of the PC I-girder is illustrated in Fig. 1 to highlight several of the design variables reflect in this study.

Figure 1: Girder composite section with design variables

1www.giapjournals.com/ijsrtm/


Pre-stress is considered to be applied in two stages, a percentage of total pre-stress at initial stage to carry only the girder self-weight and stress produced during lifting and transportation and full pre-stress during casting of deck slab.

Table1. Design variables with constraints

Sr No. Design Variables Constraints

1 Top flange width (X1) 300 X1 S

2 Girder depth (X2) 1000 X2 3500

3 Bottom flange thickness (X3) a X3 600

4 Number of cables (X4) 1 X4 20

a= clear cover + duct diameter, S= Girder spacing

B. Objective functionThe objective function in the present optimization problem is the cost of PSC slab bridge deck whose main components are cost of concrete, and pre stressing steel. It is assumed that cost of steel, launching and casting formwork etc. are directly proportional to volume of concrete, hence all these cost are included in the rate of concrete. It is also assumed that cost of anchor, sheathing etc. are directly proportional to volume of pre-stressing steel, hence all these cost are included in the rate of pre-stressing steel.Objective function can be expressed as:

TOTALcost = Qconc*Ccost+Qsteel*ScostRate of objective function can be expressed as,

Qconc = Quantity of concrete in m3Qsteel = Quantity of steel in Kg

Ccost = Cost of concrete/ m3Scost = Cost of steel/ Kg

C. Constraints These are specified limitation (upper or lower limit) on design variables which are derived from geometric requirements (superstructure depth, clearances etc.), minimum practical dimension for construction, code restriction etc. The constraint is defined as

XL X XU (1)WhereX = Design variable.XL = Lower limit of the design variable.XU = Upper limit of the design variable.The constraints imposed in the design of pre-stressed concrete flexural member are generally the following:1. Stresses developed at top n bottom fibres of the critical section at the stage of transfer of pre-stressed and under service loads. These conditions yield four inequalities expressed as:

.(2)

... (3)

... (4)

.. (5)2. Code requirements for the limit state of collapse to ensure desirable load factors against flexural failure which can be written as:

.. (6)D. Deflection constraint at the limit state of serviceability which takes the form,

(7)Where, and are the actual and permissible deflection, which is usually a small fraction of the span.E. Limitation on the minimum ratios of reinforcement in the section is expressed in the form,



Where, is the ratio of reinforcement provided, is the minimum ratio required to prevent failure by fracture of steel in tension, and is the maximum permissible ratio to ensure failure of the section by yielding of steel. The constraint equations used for this study are tabulated in Table 2.

Table 2 Constraint equation

Check for minimum section modulus G1=(Zb2/Zb1)-1

Pre-stressing force G2=((p/Psf)-1)

Permissible tendon zone at support section G3=((es/J)-1)

Permissible tendon zone at support section G4=((es/K)-1)

Stresses at transfer stage at top G5=((Ftt/Sgtr)-1)

Stresses at transfer stage at bottom G6=(Sgbt/Fct)-1

Stresses at working load stage at top G7= (Sgtw/Fcw)-1

Stresses at working stage at bottom G8=(Ftw/Sgbw)-1

Check for ultimate flexural strength G9= (Mu1/Mu2)-1

Check for shear stress G10=((Tv/Tc)-1)

3. OPTIMIZATION METHOD

Mathematical Stochastic Statistical MethodsProgramming ProgrammingTechniques TechniquesMathematical Programming Techniques1. Calculus Method2. Calculus Of Variations3. Nonlinear Programming4. Geometric Programming5. Quadratic Programming6. Linear Programming7. Dynamic Programming8. Integer Programming9. Stochastic Programming10. Separable Programming11. Multi Objective Programming12. Cpm & Pert13. Game TheoryStochastic Programming Techniques1. Stastical Decision Theory2. Markov Processes3. Queing Theory4. Renewal Theory



5. Simulation Methods6. Reliability TheoryStatistical Method1. Regression Analysis2. Cluster Analysis3. Design of Experiments4. Discriminate Analysis

The optimum cost design of I-Girder formulated in is nonlinear programming problem (NLPP) in which the objective function as well as constraint equation is nonlinear function of design variables. The various methods available for the solution of NLPP are compared in brief and the advantages and limitation of the chosen method is discussed. The various subroutines used in the program are also discussed.Methods for the Solution of the NLPP1. Method of Feasible Directions2. Sequential Unconstrained Minimization Technique (SUMT)3. Sequential Linear Programming (SLP)4. Dynamic Programming

A. The sequential unconstrained minimization technique (SUMT)In SUMT the constraint minimization problem is converted into unconstraint one by introducing penalty function. In the present work is of the form, f(x, r) is the penalty function f(x) is the objective function r is the non-negative penalty parameter, and m is the total number of constraints. The penalty function (x, r) is minimized as an unconstrained function of x and r, for a fixed value of r. The value of r is reduced sequent rained and the sequence of minima obtained converges to the constrained minimum of problems as r 0.

A. Computer programThe present optimization problem is solved by the interior penalty function method. The method is used for solving successive unconstrained minimization problems coupled with cubic interpolation methods of on dimensional search. The program developed by S. S. Rao for SUMT is used for the solution of the problem. The program is written in Matlab language.

4. CONCLUSIONSThe objective of this study is to investigate the appropriate optimization method to the minimum cost of a PSC I Girder. In view of achieving this objective it is decided to develop a computer code in MATLAB. After validating this computer code by comparing the results with analytical results, it is planned to carry out the economical and safe design. 1. It is possible to formulate and obtain solution for the minimum cost design for PSC I-Girder.2. It is possible to obtain the global minimum for the optimization problem by starting from different starting

points with the interior penalty function method.3. Significant savings in cost over the normal design can be achieved by the optimization. However the actual

percentage of the saving obtained for optimum design for PSC I-Girder depend upon the span of slab and grade of material.

4. Maximum cost saving of 14.25% for conventional design is achieved in PSC I-Girder 30m span of M50 grade concrete. Maximum cost saving of 9.81% for conventional design is achieved in PSC I-Girder 30m span of M60 grade concrete.

5. The cost of PSC I-Girder decreases with the increase in the girder depth.6. The cost of girder is directly proportional to grade of concrete.ACKNOWLEDGEMENT

I take this opportunity to thank staff members of Civil Engineering Department, Mahatma Gandhi Missions College of Engineering and Technology, Kamothe, library staff for their assistance useful views and tips. A word of thanks is also reserved for all my batch mates for their selfless help, support and entertaining company.

REFERENCES



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2. James Tyvand, A. M. and Arthur T. Blachly (1986) Foamed Plastic Absorbs Staged Post-Tensioning Deflections Journal Of Constructions Engineering, American Society of Civil Engineering, Pp.582-587.

3. Hany Abdalla and John B. Kennedy (1995) Design Of Pre-stressed Concrete Beams With Openings Journal Of Structural Engineering, American Society of Civil Engineering, Pp.894-898.

4. Sami M. Fereig (1996) Economic Preliminary Design Of Bridges With Pre-stressed I-Girders Journal Of Bridge Engineering, American Society of Civil Engineering.

5. Nabil F. Grace and Brian Ross (1996) Dynamic Characteristics of Post-tensioned Girders with Web Openings Journal Of Structural Engineering, American Society of Civil Engineering, Pp.643-650.

6. Charles G. Schilling. (1996) Yield-Interaction Relationships For Curved I-Girders Journal Of Bridge Engineering, American Society of Civil Engineering, Pp. 26-33.

7. Z. Lounis, M. S. Mirza, M. Z. Cohn, and Fellow, (1997) Segmental And Conventional Precast Pre-stressed Concrete I-Bridge Girders Journal Of Bridge Engineering, American Society of Civil Engineering, Pp.73-84

8. Bradley N. Robsonl and Issam E. Harik (1998) Pullback Testing Of Seismically Isolated P/C I-Girder Bridge Journal Of Structural Engineering, American Society of Civil Engineering, Pp.643-650.

9. Jeffrey A. Laman (1999) Load Distribution and Impact Factors For I-Girder Bridges Journal Of Bridge Engineering, vol 2.

10. Paul J. Barr, Marc O. Eberhard, and John F. Stanton (2001) Live-Load Distributions Factors In Pre-stressed Concrete Girder Bridges Journal Of Bridge Engineering, American Society of Civil Engineering, Pp.298-306.

11. N. Robson, I. E. Harik, and V. Gupta (2001) Effectiveness Of Seismic Isolation Of Highly Skewed P/C I-Girder Bridge Journal Of Bridge Engineering, American Society of Civil Engineering, Pp.211-214.

12. D. Linzell1, D. Hall, and D. White. (2004) Historical Perspective on Horizontally Curved I Girder Bridge Design in the United States Journal Of Bridge Engineering, American Society of Civil Engineering,

13. Peter Kocsis, and P.E., (2004) Evaluation of AASHTO Live Load and Line Load Distribution Factors for I-Girder Bridge Decks, Practice Periodical On Structural Design And Construction American Society of Civil Engineering, Pp.211-215.

14. Joseph Yura, Todd Helwig, Reagan Herman, and Chong Zhou, A.M. (2008) Global Lateral Buckling of I-Shaped Girder Systems Journal Of Structural Engineering, American Society of Civil Engineering, Pp.1487-1494.

15. Benjamin A. Graybeal (2008) Flexural Behavior of an Ultrahigh-Performance Concrete I-Girder, Journal Of Bridge Engineering, American Society of Civil Engineering,vol.13.

16. Benjamin Z. Dymond, Carin L. Roberts-Wollmann, and Thomas E. Cousins (2010) Shear Strength of a Lightweight Self-Consolidating Concrete Bridge Girder Journal Of Bridge Engineering, American Society of Civil Engineering, Pp.615-618

17. S. Rana & R.Ahsan (2010) Design of pre-stressed concrete I-girder bridge superstructure using optimization algorithm Journal Of Bridge Engineering.

18. David A. Petty, Paul J. Barr, G. Parry Osborn, Marvin W. Halling, and Travis R. Brackus (2011) Carbon Fiber Shear Retrofit of Forty-Two-Year-Old AASHTO I-Shaped Girders Journal Of Composites For Construction, American Society of Civil Engineering, Pp.773-781.

19. Raquib Ahsan, Shohel Rana, and Sayeed Nurul Ghani (2012) Cost Optimum Design of Posttensioned I-Girder Bridge Using Global Optimization Algorithm Journal of Structural Engineering, American Society of Civil Engineering, Pp.273-284.

20. Se-Jin Jeon, Myoung-Sung Choi, and Young-Jin Kim (2012) Graphical Assessment for Span Ranges of PSC Girder Bridges Journal Of Bridge Engineering, American Society of Civil Engineering, Pp.343-352.

21. N. Krishna Raju (2007) pre-stressed concrete Tata mcgraw hill publication Pp. 613-653.




QSAR STUDIES ON A SERIES OF 9-THC ANALOGUES AS CANNABINOID

RECEPTOR MODULATORS Neerja Shukla

Nehru Gram Bharati University, Jamunipur Kotwa, Allahabad, India

[email protected]

Abstract

QSAR studies were performed on a series of 9-tetrahydrocannabinol ( 9-THC) analogues as

cannabinoid receptor modulators. 9-tetrahydrocannabinol ( 9-THC) analogues have been analyzed in

relation to their physicochemical and molecular properties. The activities of the compounds were found to be

significantly correlated with the physicochemical parameters such as index of refraction (Ior) , equalized

electro-negativity (Xeq), first, second and third order of connectivity indices (

1,2,3) and Mean weiner Index (WA). It was found that the presence of OH group at R1 position was conducive for the inhibitory activity. The results are critically discussed on the basis of regression data and cross validation techniques. Poglani

factor Q and the results of LOO (leave one out) method confirms the reliability and predictability of the

proposed models.

Keywords: QSAR; cannabinoid receptor modulators; physicochemical property; regression analysis, LOO

1. INTRODUCTION

The cannabinoid receptor type 1(CB1) and cannabinoid receptor type 2 (CB2) are G-protein coupled receptors.The

exact location of CB1 receptors is on the central and peripheral neurons and that of CB2 receptors is in the

periphery. The cannabinoid receptors are having great therapeutic potential.Treatment of pain,neurodegenerative

disease and glaucoma can be done by CB1 agonists and neuropathic pain,asthma,allergies,rheumatoid arthritis and

autoimmune diseases can be done by CB2 agonists.Currently Nabilone is under clinical trials for neuropathic

pain.The current study represents the QSAR analysis of a series of 9-tetrahydrocannabinol ( 9-THC)

analogues as cannabinoid receptor modulators ( Burdick et al., 2010)using a number of structural parameters along

with indicator parameter at different substitution sites. The aim of our study was to discover selective CB2 agonists

for treatment of neuropathic pain by making modifications in 9-THC(1) at the C-1 position.

2. EXPERIMENTAL

2.1. Physicochemical Parameters- The physicochemical parameters such as Index of refraction (Ior) used as steric

parameter was calculated by ACD Lab Chem Sketch ( Acd Lab Software), first,second and third order of

connectivity indices (1,2,3) and Mean weiner Index (WA)(DRAGON Software) are the topological Parameters

calculated by DRAGON Software .

(i) Index of refraction (IOR) The index of refraction (IOR) of a medium is the ratio of the speed of light in

vacuum to its velocity in the medium.

(ii) Molecular Connectivity Index () - Molecular connectivity is a method of quantifying the structure of chemical groups, which has found wide application in chemistry (Gutman 1994; Khadikar et al 2003; Randic 1975;

Randic 1993) . It is based upon an algorithm to encode the ranking of alkanes relative to the attribution of branching.

The term molecular connectivity was adopted by Kier and Hall in 1975, in connectivity with a mathematical

algorithm proposed by Randic. This index is one of the most widely used topological indexes in QSPR/QSAR

analysis. In its original form, it is defined as below:

2/111 )( jiji

G

.. (1)

Where i is the valence of a vertex i, equal to the number of bonds connected to the atom i, in G, representing the

graph of the compound. Meaning of i is analogous.

(iii) The first order Connectivity Index (1) -The First order connectivity index follows from the following

expression.

ji

ji DDG,

2/111 )(

.. (2)

Where Di and Dj are the valences of a vertex i and j, equal to the number of bonds connected to the atoms i and j, in

G.

(iv)The second Order Connectivity Index (2) - The second order connectivity index (2R ) is obtained from the



following equation:

SL

SSkji

vv DDDG1

2/122 )(

.. (3)

i = j = k

Where S stands for a path of length two while SL is the number of paths of length in G. Each path of length two has

in this case a weight Di, Dj, Dk.

(v) Mean Wiener Index (WA) -It is interesting to note that the number of element in a triangular of diagonal sub

matrix is equal to -

1/2 N (N-1) .. (4) Where, N is the number of vertices in Graph G. In terms of this, the mean wiener index (WA) (Weiner 1947; Weiner

1947) is defined as -

WA (G) = 2 W/N (N-1) .. (5)

(vi) Equalized electronegativity (Xeq)18

- Electronegativity was first defined by Pauling

(1939) as the power of an

atom in a molecule to attract electrons to it. He found an empirical relation between the energy of a bond the

electronegativities of the bonded and from this relation he estimated the electronegativity of several atoms.

Xeq is an electronic parameter and is based upon Sandersons Principle. It was calculated using the following formula:

X|V

NXeq

Where N is the total number of atom and V is the particular number of atom and X is the electronegativity of that

element.

(vii) Indicator Variables Indicator variables (parameters), sometimes called dummy variables, are used in multiple linear regression analysis to account for certain features which cannot be described by continuous variables.

The indicator parameters (variables) are generally assigned only two values zero and one. It is taken as 1 (one) for

that particular group or substituent and at a specific site in the molecule and 0 (zero) for all such cases where that

group or substituent is absent( Recantint et al., 1986).

2.2. Regression Analysis- The regression analysis is done using SPSS 7.5 version software.

3. RESULTS AND DISCUSSION

The success of QSAR studies mainly depends whether or not the molecular descriptors chosen are appropriate to

explain the biological activity(pKi).The present work deals with the QSAR studies on a series of 23 compounds of

9-tetrahydrocannabinol ( 9-THC) analogues with steric and topological parameters along with indicator

parameter and activity. It is envisioned that this study will produce models that can be used for future designing of

new analogues with higher potency..Here the biological activity (pKi) is a measure of inhibitory activity. In an

attempt to determine the role of structural features, which appears to influence the observed activity of reported

compounds, QSAR studies were undertaken using linear free energy relationship (LFER) model of Hansch and

Fujita( Hanch et al., 1964) The multiparametric regression analysis used to derive the correlation was executed with

the SPSS (7.5) programme. For QSAR studies, different analogues of parent structure and values of biological

activity (pKi) along with, calculated physicochemical parameters are reported in Table-1. The autocorrelation

between all the structural parameters and biological activity (pKi) is shown in Table-2. A close study of this table

makes it clear that those parameters, which are orthogonal, can be used together in multiparametric regression

studies. These parameters have already been found to be useful in various QSAR studies performed earlier

(Srivastava et al., 2011-2007; Chaurasia et al., 2007; Agarwal et al., 2006, Agarwal et al., 2005, Agarwal et al., 2003

;Khadikar et al.,2005, Khadikar et al.,2003, Khadikar et al.,2002; Jaiswal et al., 2004; Thakur et al., 2004).

The aim of current study is mainly to study the different physicochemical parameters linear models and their

stepwise regression analysis by using indicator variable and different parameters (According to correlation matrix).

In this series, some significant QSAR models have been obtained which are given below:

pIC50 = 0.482 (0.202) Log P 0.069 (0.051) St 0.833 (0.917) I1 + 0.636 (0.731) I2 + 12.959 ------ (1) n = 24, R = 0.884, R

2 = 0.782, R

2A = 0.736, SE = 0.426, F (4, 19) = 17.007, Q = 2.364



pIC50 = 0.519 (0.204) Log P 2.826 (2.313) J 0.830 (0.941) I1 + 0.555 (0.752) I2 + 5.828

------ (2)

n = 24, R = 0.878, R2 = 0.770, R

2A = 0.722, SE = 0.437, F (4, 19) = 15.925, Q = 2.364

pIC50 = 0.509 (0.205) Log P 8.301 (6.874) Ior 0.833 (0.943) I1 + 0.681 (0.755) I2 + 23.621

------ (3) n = 24, R = 0.877, R

2 = 0.769, R

2A = 0.720, SE = 0.438, F (4, 19) = 15.803, Q = 2.364

pIC50 = 0.446 (0.234) Log P 3.538 (3.754) D 0.887 (0.991) I1 + 0.568 (0.793) I2 + 14.349

------ (4) n = 24, R = 0.862, R

2 = 0.744, R

2A = 0.690, SE = 0.461, F (4, 19) = 13.784, Q = 2.364

Here and hereafter n is the number of compounds, R is the correlation coefficient, R2 is the coefficient of

determination, R2

A is the adjusted R2, F is the Fischer statistics (Diudea ,2000; Bikash et al., 2003)

S.E. is the

standard error of estimate and Q is the quality of fit (Pogliani,1994; Pogliani,1996)

The resulting models are not statistically good, they are not sufficient for QSAR modeling, for further improvement

in statistical terms we must go through stepwise regression analysis. In above shown models the negative sign of

coefficient of St, Ior, D, LogP indicate that less bulkier, less denser and lesser hydrophobic groups are preferable for

the activity. The coefficient of J was found to be positive which clearly indicates that this parameter has positive

influence in determining the activity. The negative sign of coefficient of I1 makes it that the

group

NH

N

Cl

Cl

Cl

CH3

at R1 position should be strictly avoided in the future drug

modeling while positive coefficient of I2 explains that the group

N

NN

N

Cl

CH3

CH3

CH3

at R1 position. is

beneficial for the activity. In order to examine the relative potential of models, predictive correlation coefficient

(R2Pred) were estimated by plotting graphs between observed and calculated pIC50 values obtained with the help of

model no. 1. The comparison between observed and calculated activities is listed in Table-3. Such correlations are

shown in figure 1. From the figure 1 R2Pred values obtained for equation 1 is 0.782 and this is fairly high indicating

the quality of the models. The calculated F value for these models is greater than F theoretical value [F(4, 19) = 2.90]

for all the models and this fact also indicates towards significant correlation level.

Predictive ability was also evaluated by the LOO (Leave one out) (Cramer et al., 1988;Podlgar et al., 2000) cross-

validation procedure. This method systematically removes one data point at a time, a model is constructed on the

basis of this reduce data set and is subsequently used to predict the removed sample. This procedure was repeated

for all the points until complete set of predicted values were obtained. Various cross-validation parameters

calculated for the proposed models are presented in Table- 4.

3.1. Cross Validation The cross validation analysis was performed using leave one out (LOO) method ((Cramer etal., 1988; Podlgar et al.,

2000)) in which one compound is removed from the data set and the activity is correlated using the rest of the data

set. The cross-validated R2

in each case was found to be very close to the value of R2 for the entire data set and

hence these models can be termed as statistically significant. Cross validation provides the values of PRESS, SSY

and R2cv and PSE from which we can test the predictive power of the proposed model.



It is argued that PRESS, is a good estimate of the real predictive error of the model and if it is smaller than SSY the

model predicts better than chance and can be considered statistically significant. Furthermore, the ratio PRESS/SSY

can be used to calculate approximate confidence intervals of prediction of new compound. To be a reasonable

QSAR model PRESS/SSY should be smaller than 0.4. Also, if PRESS value is transformed in a dimension less term

by relating it to the initial sum of squares, we obtain R2

cv i.e. the complement to the traces on of unexplained

variance over the total variance. The PRESS and R2cv have good properties. However, for practical purposes of end

users the use of square root of PRESS/N, which is called predictive square error (PSE), is more directly related to

the uncertainty of the predictions. The PSE values also support our results. The calculated cross-validated

parameters confirm the validity of the models. All the requirements for an ideal model have been fulfilled by model

no. 1, thats why, we have considered as the best model.

R2

A takes into account the adjustment of R

2. R

2A is a measure of the percentage explained variation in the dependent

variable that takes into account the relationship between the number of cases and the number of independent

variables in the regression model, whereas R2

will always increase when an independent variable is added. R2

A will

decrease if the added variable doesnt reduce the unexplained variable enough to offset the loss of decrease of freedom.

3.2. Predictive error of coefficient of correlation (PE)

The predictive error of coefficient of correlation (PE) (Chattergee et al., 2000) is yet another parameter used to

decide the predictive power of the proposed models. We have calculated PE value of all the proposed models and

they are reported in Table 4. It is argued that if

(i) if R < PE, then correlation is not significant;

(ii) if R > PE; several times (at least three times), then correlation is indicated; and if

(iii) if R > 6PE, then the correlation is definitely good.

For all the models developed the condition R > 6PE is satisfied and hence they can be said to have a good predictive

power.

4. CONCLUSIONS

On the basis of the above discussions some general conclusions can be drawn: -

1. Less denser, less bulkier and lesser hydrophobic groups should be used in the future drug modeling.

2. The indicator I1 for

NH

N

Cl

Cl

Cl

CH3

group at R1 position should be avoided.

3. The group

N

NN

N

Cl

CH3

CH3

CH3

at R1 position is preferentially favorable.



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Table-1 Biological activities and physicochemical parameters of

O

R1

R2

Table-2 Correlation matrix showing correlation among various physico-chemical parameters and inhibitory

activity

pKi Ior Xeq 1 2 3 WA

I

pKi 1.000

Ior -0.444 1.000

Xeq -0.688 0.348 1.000 1 -0.109 0.112 0.346 1.000 2 0.014 -0.024 0.231 0.949 1.000 3 -0.004 0.119 0.252 0.976 0.905 1.000

WA 0.154 -0.018 0.131 0.956 0.953 0.945 1.000

I 0.625 -0.319 -0.263 -0.259 -0.196 -0.189 -0.122 1.000



TABLE- 3 Comparisons between Observed and Predicted Activities and Their Residual Values

S. No. Observed Predicted Residual

1. 7.444 7.972 -0.528

2. 5.660 5.763 -0.103

3. 5.900 6.259 -0.359

4. 5.917 6.311 -0.394

5. 5.699 5.110 0.589

6. 6.668 6.550 0.118

7. 6.412 5.977 0.435

8. 5.854 5.539 0.315

9. 5.764 5.312 0.452

10. 5.688 5.851 -0.163

11. 5.854 6.065 -0.211

12. 5.752 5.529 0.223

13. 6.107 5.942 0.165

14. 5.824 6.485 -0.661

15. 6.329 6.756 -0.427

16. 5.714 6.024 -0.310

17. 9.699 9.171 0.528

18. 8.276 7.149 1.127

19. 8.538 7.661 0.877

20. 6.666 7.159 -0.493

21. 6.590 6.975 -0.385

22. 5.963 6.717 -0.754

23. 6.593 6.635 -0.042



Table-4 Cross-validated parameters and predictive error of coefficient of

correlation (PE) for the proposed model

Eq.

no.

n

Parameter

Used

PRESS SSY

PRESS/SSY

R2cv PSE R 1-R

2 PE 6PE

1. 23 Xeq +Ior +WA+ I 5.571 19.116 0.291 0.709 0.492 0.880 0.226 0.031 0.186

2. 23 Xeq +Ior + 1 + I 6.124 18.563 0.330 0.670 0.516 0.867 0.248 0.034 0.204

3. 23 Xeq +Ior + 2 + I 6.171 18.516 0.333 0.667 0.518 0.866 0.250 0.034 0.204

4. 23 Xeq +Ior + 3 + I 6.306 18.381 0.343 0.657 0.524 0.863 0.255 0.035 0.210



THE IMPACT OF VARIOUS COAGULATION TEMPERATURES AND LEVELS OF

SALT ON CHEMICAL PROPERTIES OF CHHANA SPREAD PREPARED FROM

BUFFALO MILK Yadav Nishu

3, Paul Virginia

1 Roli

3, Rathore Alka

3, Chandra Ramesh

2, Khandelwal M.S

4 and Paul Ajit

5

1Advisor, Associate Prof,

3Ph.D Scholar, Dept of Foods & Nutrition, Ethelind School of Home Science

2Co-advisor, Prof & Dean, Warner School of Food & Dairy Technology

5Associate Prof, Head, Dept of Mathematics & statistics, College of Basic Sciences Institution,

Sam Higginbottom Institute of Agriculture, Technology & Sciences, Allahabad, India 4Senior Research Associate, Indian Agriculture Statistics Research Institute, New Delhi

[email protected]

Abstract

Among milk products Chhana is gaining momentum in its production and consumption. Chhana is heated

and acid coagulated indigenous milk product which forms the base of several popular Indian sweets like

Rasogulla, Sandesh, Rasamalai and Chumchum etc Chhana or paneer, means the product obtained from cow

or buffalo milk or a combination of them by precipitation with sour milk, lactic acid or citric acid. It shall not

contain more than 70% of the moisture, and the milk fat not be less than 50% of the dry matter (PFA,

1976).The study was conducted in the Students Training Dairy and Research Laboratory of Warner School of Food and Dairy Technology, SHIATS Allahabad. The buffalo milk as (M2) were used for making chhana

spread and three different coagulant temperature 600C, 65

0C, 70

0C asT1 T2 T3 and three salt levels 1%,1.5%,

2% S1, S2, S3 were used in the present experimental work. 9 treatment combinations used in the experiment

namely M2T1S1, M2T1S2, M2T1S3, M2T2S1, M2T2S2, M2T2S3, M2T3S1, M2T3S2, M2T3S3 and replicated three times.

.The experiment treatment combination (M2T1S3) chhana spread contained highest percentage of

moisture(54.66). The experiment treatment combination (M2T3S1) contained highest percentage of fat

(25.98).The highest percentage of protein (15.29) was found in the treatment combination (M2T2S1).The

highest percentage of lactose (3.06) was found in the treatment combination (M2T2S1). The treatment

combination (M2T3S1) contained highest percentage of ash (3.07).The highest percentage of calcium (479.63)

was found in the treatment combination (M2T1S3). The highest energy value of (253.26) was found in the

treatment combinations (M2T2S1). Chhana spread is still in its infancy and needs various parameters to be

fixed and tested before its commercial use and large scale productions by organized sector

Key words: Chhana Spread, Coagulation Temperature, Salt Levels, Storage Periods

1. INTRODUCTION

Among milk products Chhana is gaining momentum in its production and consumption. Chhana is heated and acid

coagulated indigenous milk product which forms the base of several popular Indian sweets like Rasogulla, Sandesh,

Rasamalai and Chumchum etc. It is also used as base material for the preparation of large number of culinary dishes.

It originated in eastern part of the country particularly in West Bengal, but is now popular in north and north western

regions of India. Its preparation is mainly confined to the cottage sector, largely in the eastern parts of India, and

more recently, in Bikaner district of Rajasthan. Indias total production of chhana is estimated at 200,000 tones, and the value of chhana- based sweets, around Rs. 70,000 million. However, chhana based sweets are gaining popularity

in other parts of country. Chhana or paneer, means the product obtained from cow or buffalo milk or a combination

of them by precipitation with sour milk, lactic acid or citric acid. It shall not contain more than 70% of the moisture,

and the milk fat not be less than 50% of the dry matter (PFA, 1976).Shelf life of milk products is also one of the

most important factors for its production on commercial scale. Shelflife of chhana has been reported to be about 12

days and 3 day at 70

C and 240

C respectively (De and Ray 1954). It was also reported that 10 minutes steaming and

sterilization of chhana at 1.05 kg/15 minutes in the presence of 1%lactic solution improved its shelflife up to 6 &8

days respectively 300 +

10C. Chhana could be preserved for 4 days at the same temperature by treating it with 2%

lactic acid solution (kulkarni et al. 1984).The chemical composition of the products (chhana spread) was depends

on types of milk (buffalo, cow and admixed milk)used to preparation of chhana.Buffalo milk contains all the

nutrients in higher proportions than cow milk. The compositional differences between buffalo and cow milk are

reflected on their physic chemical properties. Buffalo milk is the more preferred for preparing milk and dairy of

western and indigenous. The inherent properties of buffalo milk like high total solids content, superior whiteness

and viscosity render it eminently suitable for the manufacture of traditional milk products. On the other hand cow

milk yields soft coagulum making it suitable for preparing chhana and chhana based sweets. Higher total solids in

buffalo milk also provide more calories (100calories/100gm) than cow milk (70calories/100gm). Higher proportion

of Beta casein in buffalo milk makes it easier to prepare humanized. and buffalo milk high in calcium a better



calcium: phosphorous ratio and less sodium and potassium than in cow milk which makes it a better nutritional

supplement for infants. But buffalo milk contains less cholesterol compare to cow milk. The buffalo milk contains

more fat, solids not fat and total solids and hence yield of products will be always higher than cow milk. The higher

fat content in buffalo milk helps in increasing the quantity of toned milk. Production of chhana involves

precipitation of casein along with entrapped fat and water-soluble components of milk (Lactose, whey, proteins,

minerals, vitamins) by addition of acidulate to milk at near- boiling temperatures, followed by removal by whey

from the curd. Acidification of boiled/ hot cow milk yields chhana as an off-white, mildly acidic, spongy coagulum.

Chhana retains about 90 per-cent of fat & protein, 50 per-cent ash &10 per-cent lactose of the original milk. The

energy value of cow chhana ranges from 2866 to 3748 calories per kg and chhana also retains appreciable

proportion of fat soluble vitamins like A & D (Ray and De 1954). On an average calcium, Phosphorus, Vitamins,

A, B, B2, and C contents per 100g of chhana samples were reported to be 208 mg, 138 mg, 3.66 IU, 73g, 15g and

2.8 mg respectively. Chhana was reported to contain practically no nicotinic acid. The loss of ascorbic acid during

chhana making was reported to be about 57 % as compared with the figure of boiled milk (Mani et al.1955). The

average biological value and digestibility coeffcient of chhana were found to be 67 and 97 respectively

(Balasubramaniam 1955).

2. MATERIAL AND METHODS

The present investigation was conducted in the Students Training Dairy and Research Laboratory of Warner School of Food and Dairy Technology and Nutrition Research Laboratory of Ethelind school of Home science, Sam

Higginbottom Institute of Agriculture, Technology and Sciences (Deemed - to be University), Allahabad, U.P.

a. Preparation of chhana spread: Chhana was prepared from buffalo milk standardized to 6 % fat & 9% SNF as per method suggested by Ray and

De (1953). buffalo milk was heated at 90oC for 15 minutes and cooled down to 60

oC and therefore, added warm

coagulant solution (1%) at 60oC to effect proper coagulation. Traditional method was used to drain the free whey

from the coagulated mass. The curd along with whey was transferred on a muslin cloth and whey was allowed to

drain by hanging technique till trickling of free whey was stopped. The curd sample obtained by this method was

subjected for chhana spread making. The curd from traditional method was converted into chhana spread by using

method suggested by Tiwari and Sachdeva (1991). In this case chhana was broken into pieces and blended in

domestic blender along with 10 percent whey and specified salt level.

S.No

.

Treatments Combinations

1 M2T1S1 Chhana spread prepared from buffalo milk containing 6 % milk fat and 9 %SNF and

coagulation of milk at 600C and using 1 % salt level

2

M2T1S2 Chhana spread prepared from buffalo milk containing 6 % milk fat and 9 %SNF and

coagulation of milk at 600C and using 1.5 %salt levels


coagulation of milk at 600C and using 2 % salt levels



5 M2T2S2 Chhana spread prepared from buffalo milk containing 6% milk fat and 9 %SNF and

coagulation of milk at 650C and using 1.5 % salt levels





b. Treatment Combinations: The buffalo milk as (M2) chhana spread and three different coagulant temperature

600C,65

0C,70

0C as T1, T2, T3 and three salt levels 1%,1.5%, 2% as S1, S2, S3, respectively, were used in the present

experimental work. Channa prepared from different treatment combinations were compared with each other.The

different combinations used in the experiment were represented as follow

c. Chemical testing of chhana spread: The procedure given in Manual in Dairy Chemistry, ICAR(1972), for

sampling was followed.

d. Analytical technique - Chhana spread was analyzed for moisture content using AOAC (1980) method. Total

nitrogen was determined by micro-kjedahl method. For fat content in milk, Gerbers method of BIS(1981) was used. The fat, ash and content was determined using AOAC (1980) procedures. Lactose, calcium content was estimated

by AOAC (1980.

e. Determination of total energy: Kcal/100gm = (4 X protein %) + (4 X CHO %) + (9 X fat %)

f. Statistical Analysis: The order to study the effects of milk, various coagulation temperatures and levels of salt of

chhana spread, a laboratory experiment was conducted and required data were collected. Analysis of variance of

these data was worked out on the basis of factorial completely randomized design (Federer, 1963).

3. RESULTS AND DISCUSSION:

Table:1.1 Average moisture content of chhana spread in percent on account of milk various coagulation

temperatures and different levels of salt

Buffalo milk (M2)

Nutrient Coagulation

Temperatures

Different levels of Salt (S) Mean

S1 (1%) S2 (1.5%) S3 (2%)

Moisture T1 (600C) 54.24 54.45 54.66 54.45

T2 (650C) 53.23 53.52 53.71 53.48

T3 (700C) 54.04 54.24 54.45 53.24

Mean 53.83 54.07 54.27 53.72

Factors M T S MxTxS

SE(m) + 0.012 0.012 0.012 0.038

C.D. at 5% 0.036 0.036 0.036 0.180

In above table 1.1buffalo milk on an average moisture content of chhana spread with respect of different coagulation

temperatures were recorded as 54.45, 53.48 and 53.24 % at 600C, 65

0C and 70

0C, respectively. Likewise levels of

salt were 53.83, 54.07 and 54.27% at 1%, 1.5 and 2% , respectively. The milk, coagulation temperature and levels of

salt changes in the experiment of moisture content of chhana spread was also changed in prepared product i.e.

variation of milk, temperatures and levels of salts was found significant at 5% level of significance. The interaction

effect between milk, coagulation temperatures and levels of salts (MxTxS) was found significant.

7 M2T3S1 Chhana spread prepared from buffalo milk containing 6% milk fat and 9 %SNF and


8 M2T3S2 Chhana spread prepared from buffalo milk containing 6% milk fat and 9%SNF and

coagulation of milk at 700C and using 1.5 % salt levels

9 M2T3S3 Chhana spread prepared from buffalo milk containing 6 % milk fat and 9%SNF and




Table:1.2 Average fat content of chhana spread in percent on account of milk various coagulation


Buffalo milk (M2)


Temperatures


S1 (1%) S2 (1.5%) S3 (2%)

Fat T1 (600C) 25.46 25.35 25.23 25.34

T2 (650C) 25.55 25.48 25.37 25.46

T3 (700C) 25.98 25.86 25.75 25.86

Mean 25.66 25.56 25.45 25.55

Factors M T S MxTxS

SE(m) + 0.002 0.002 0.002 0.006

C.D. at 5% 0.006 0.006 0.006 N.S

The effect of milk different coagulation temperatures and salt levels on fat content of chhana spread in buffalo milk

presented Table 1.2 the effect of temperature was found 700C, which contained the highest content of fat which was

highest 25.86 % as compared to low temperatures 600C and 65

0C as 25.34 and 25.46 %, respectively. In salt levels

1 % showed the maximum fat content was 25.66%, while minimum as 25.45% at highest salt level i.e. 2%.

Table: 1.3 Average protein content of chhana spread in percent on account of milk various coagulation


Buffalo milk (M2)


Temperatures


S1 (1%) S2 (1.5%) S3 (2%)

Protein

T1 (600C) 14.50 14.43 14.37 14.43

T2 (650C) 15.29 15.22 15.66 15.39

T3 (700C) 14.79 14.73 14.66 14.72

Mean 14.86 14.79 14.89 14.84

Factors M T S MxTxS

SE(m) + 0.006 0.006 0.006 0.019

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Ijsrtm vol 2 (1) january 2014