CHAPTER-1 INTRODUCTION

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NOVEL UV-SPECTROPHOTOMETRIC METHOD FOR

SIMULTANEOUS ESTIMATION OF ITRACONAZOLE AND

TERBINAFINE USING CHEMOMETRIC TOOLS

Priyanka Bikkasani*, Titus Darsi and Kesaraju Shivaranjini

Pharmaceutical Analysis, Vaagdevi College of Pharmacy (Affiliated to Kakatiya University),

Hanamkonda, Warangal.

1. INTRODUCTION

Pharmaceutical Analysis may be defined as a process or sequence of

process to identify and /quantify a substance or drug, the components

of a pharmaceutical solution or mixture or the determination of the

structures of chemical compounds used in the formulation of

pharmaceutical products.

Drug analysis has an important role in the development of medicine

using various analytical and instrumental methods. The great

advancement of analytical chemistry is the corner stone in the

development of newer drug. The tedious, labour, costly time taking

process of chemical analytical and instrumental analysis has become

more advanced time to time. Chromatography techniques such as: Thin

Layer Chromatography (TLC), High Performance Liquid Chromatography (HPLC), Gas

Chromatography (GC), etc., have played a pronounced role in drug analysis. The

spectroscopic techniques such as Ultraviolet-Visible (UV-Vis) spectroscopy, Fluorescence

spectroscopy, Infrared (IR) spectroscopy, etc., are now solving qualitative and quantitative

problems in chemical analysis. The trend in analytical chemistry has become changed from

time to time and sample to sample. One of the instruments being used with excellent

precision is UV-Vis spectroscopy.

[1]

In using the case where significant overlapping of the spectra of mixtures, utilizing the

instrument traditionally is hardly possible. In order to resolve this problem and get more data

a computer assisted method called chemometric is merged in drug analysis.

[2]

The combined

World Journal of Pharmaceutical Research

SJIF Impact Factor 8.084

Volume 10, Issue 4, 1391-1454. Research Article ISSN 2277– 7105

*Corresponding Author

Priyanka Bikkasani

Pharmaceutical Analysis,

Vaagdevi College of

Pharmacy (Affiliated to

Kakatiya University),

Hanamkonda, Warangal.

Article Received on

12 Feb. 2021,

Revised on 04 March 2021,

Accepted on 24 March 2021

DOI: 10.20959/wjpr20214-20168

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uses of spectrophotometry and chemometric techniques become method of choice for the

development of better analytical procedures and quality control of many pharmaceuticals.

[3]

Fig. 1.1: Schematic diagram of analytical process.

Analytical chemistry

According to the American Chemistry Society, Analytical chemistry is a metrological science

that develops, optimizes and applies measurement process intended to derive quality

chemical information from natural or synthetic system in order to solve analytical problems.

Pharmaceutical analysis

Analytical chemistry is used to the pharmaceuticals like formulations products (intermediate

and final) and substances (additives). It also includes the analysis of raw material and

intermediates during the manufacturing process of drugs. Drug analysis has an important role

in the development of drugs using various analytical and instrumental methods. The

pharmaceutical analysis comprises, the procedures necessary to determine the “identity,

strength, quality and purity” of such compounds.

1.1 Types of analytical chemistry

The pharmaceutical analyses are generally of a quantitative as well as qualitative type.

Qualitative analysis

 Qualitative inorganic analysis seeks to establish the presence of a given element or

inorganic compound in a sample.

 Qualitative organic analysis seeks to establish the presence of a given functional group or

organic compound d in a sample.

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Quantitative analysis

 Quantitative analysis seeks to establish the amount of a given element or a compound in a

sample.

Both quantitative and qualitative analysis ensures the purity of a product i.e., whether there

are any impurities, degradation products or synthesis intermediates etc. in the sample and, if

so, how many and in what concentrations.

[4]

Many analytical technologies used to generate large amounts of data that are used in

characterizing substances (sample of any kind), and can form the basis of qualitative and

quantitative methods. Chromatographic techniques such as thin layer chromatography (TLC),

High Pressure Liquid Chromatography (HPLC), Gas Chromatography (GC), etc. have played

a pronounced role in drug analysis.

The spectroscopic techniques such as Ultraviolet- Visible (UV-Vis) spectroscopy,

Fluorescence spectroscopy, Infrared (IR) spectroscopy, etc. are now solving qualitative and

quantitative problems in chemical analysis. The trend in analytical chemical analyses has

become changed from time to time.

One of the instruments being used with excellent precision is UV-Vis spectroscopy.

[5]

The

inherent character of spectroscopic data is equal to greatly superimposed signals from various

chemical compounds jointed with a lot of related information. These characterization and

information can be complex to remove by using Univariate procedures. Therefore,

Multivariate data analysis methods have become common tools in applying modern

instruments to solve quantitative and qualitative analysis problems.

1.2 Spectroscopy

[6,7, 8]

Spectroscopy is branch that uses the interaction of energy with a sample to perform the

analysis that can provide information on the chemical composition and conformation of the

samples. It lies on the boundary between physical and chemical chemistry, its adaptation and

applications to chemical analysis is the responsibility of analytical chemistry which in turns

feeds on spectroscopic developments.

Spectroscopy is based on the dispersion of light into its component wavelengths (i.e.,

energies). The data that is obtained from spectroscopy is called spectrum. A spectrum is a

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plot of the intensity of energy detected versus the wavelength (or frequency, etc.) of the

energy and provides the information on the structure of the sample under analysis.

The spectrum is comprised of data across many wavelengths (frequencies, variables), and is

best analyzed using multivariate analysis tools which provide a means to maximize the

information from the spectral data.

Spectroscopy is a quick and non-destructive analytical technique commonly used to measure

certain parameters if interest in terms of the absorbance spectrum. In the pharmaceutical

industry, it is used in control processes to determine some variables as, for example, the

amount of active ingredient of a drug.

Spectroscopy is a premier method of performing both qualitative and quantitative analysis,

but runs into difficulties when the sample is a complex mixture of ingredients. Ingredients

typically have different spectra and when sampled individually, each is simple to recognize.

When an unknown number of them are mixed together in unknown amounts, it can be

difficult to determine what and how much of each ingredient is present.

1.3 UV-Visible spectrophotometry

[8, 9]

UV-VIS spectroscopy is the study of how a sample responds to the light. When a beam of

light passes through the sample substance or solution, some of the light may absorbed and the

remainder transmitted through the sample. The ratio of the light entering the sample (I

0

) to

that transmitting the sample (I

t

) at a particular wavelength is defined as the transmittance (T).

This is often expressed as the percentage transmittance (%T), which is the transmittance

multiplied by 100:

%T = ( I

0

/I

t

) × 100

The absorbance (A) of a sample is the negative logarithm of the transmittance:

A = -log T

The UV-VIS range of the electromagnetic spectrum covers the range 190-700nm (most of the

instruments are capable of measuring at a longer wavelengths than this, depending on their

detector type).

Electronic transitions

[10]

The absorption of UV or Visible radiations corresponds to the three types of electronic

transitions:

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1. Transition involving π, σ, and n electrons

2. Transitions involving charge-transfer electrons

3. Transitions involving d and f electrons

Possible electron transitions of π, σ, and n electrons are shown in the fig 1.2.:

Fig. 1.2: Electronic transitions.

σ → σ

*

Transitions

These transitions can occur in such compounds in which all the electrons are involved in

sigma bonds and there is no lone pair of electrons. The energy required for an electron in a

bonding σ orbital to get excited to the corresponding anti bonding orbital is large. For

example: in alkenes and saturated hydrocarbons. Methane contains only single C-H bonds

and thus undergoes σ → σ

*

transitions and exhibits absorption maximum at 125nm. Such

transitions are studied in vacuum ultraviolet region since below 200nm oxygen present in air

begins to absorb.

n → σ

*

Transitions

Saturated compounds containing atoms with unshared electron pair (non-bonding electrons)

like saturated alcohols, amines, halides, ethers etc are capable of showing N → σ

*

transitions.

Energy required for these transitions is usually less than σ → σ

*

transitions. Such compounds

absorb light having wavelength in the range 150-250nm, Eg: absorption maximum for water,

methyl chloride and methyl iodide are 167nm, 173nm and 259nm respectively.

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π →π

*

Transitions

These transitions need an unsaturated group in the molecule to provide the π electrons. E.g.:

in alkenes, alkynes, aromatics, acyl compounds or nitriles. Most absorption spectroscopy of

organic compounds is based on transitions of n or π electrons to the π

*

excited state and the

absorption peaks for these transitions fall in an experimentally convenient region of the

spectrum (200-780nm). π →π

*

transitions normally give molar absorpitivites between 1000

and 10,000 Lmol

-1

cm

10

. Unconjugated alkenes absorb 170-190nm.

n → π

*

Transitions

Bands attributed to n → π

*

transitions are also known as R-bands. In these transitions

electrons of unshared electron pair on a hetro atom such as oxygen, nitrogen and sulphur is

excited to π

*

anti-bonding orbital. This transition involves least amount of energy than all the

transitions and hence this transition gives rise to an absorption band at longer wavelengths.

These transitions exhibit a weak band in the absorption spectrum. E.g.: saturated aldehydes

and ketones exhibit absorption of low intensity around 285nm.

π → π

*

Transitions

Transitions of diene or polyene systems are essentially unresponsive to solvent polarity,

because hydrocarbon double bonds are non-polar.

1.4 Theory

[11,12]

Beer’s law

Beer observed that logarithmic relationship holds between transmittance and the

concentration of the solution, i.e., “the intensity of beam of monochromatic light decreases

exponentially with the increase in the concentration of the absorbing substance

arithmetically”.

Lambert’s law

“When a beam of light is allowed to pass through a transparent medium, the rate of decrease

of intensity with the thickness of medium is directly proportional to the intensity of light”.

Beer-Lambert’s law

When a beam of light is passed through a transparent cell containing a solution of absorbing

substance, reduction of the intensity of light may occur. Mathematically, Beer-Lambert’s law

is expressed as

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A α c and A α b

So, A α b × c

A = a b c

Where, A = absorbance or optical density

a = absorptivity or extension coefficient

b = path length of radiation through sample (cm)

c = concentration of solute in solution

Both b and c are constant so a is directly proportional to the concentration c

When c is in gm/100ml, then the constant is called A1

%1cm

A = a b c

Deviations from Beer-Lambert’s law

According to Beer-Lambert’s law, a straight line passing through the origin should be

obtained if we plot absorbance (A) against concentration. But usually there is no deviation

from a linear relationship between concentration and absorbance and an apparent failure of

Beer-Lambert’s law may occur.

Deviation from the law is reported as positive if the curve deviates upwards or negative if it

deviates downwards.

Deviations from Beer-Lambert’s law can arise due to the following factors:

I. If concentration of colored ion changes when dissolved in solvent.

II. If colored solute ionizes or associates or disassociates in solution.

III. Due to the presence of impurities that fluoresce or absorb at absorption wavelength of

sample.

IV. If monochromatic light is used.

V. If undesirable light (stray light) fall on the detector due to improper slit width.

VI. If the solution species undergo polymerization.

VII. This law cannot be applied to suspensions.

1.5 Instrumentation and Working of VU-Visible spectrophotometer

[13]

A spectrophotometer is an instrument for measuring the transmittance or absorbance of a

sample as a function of the wavelength of electromagnetic radiation. The key components of

spectrophotometer are:

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Radiation source/light source

A continuous source of radiation energy covering the region of spectrum in which the

instrument is designed to work. Some of the sources are Tungsten lamp, Xenon arc lamps, D

2

lamps etc.

Tungsten filament lamp and Hydrogen-D

2

lamps are most widely used and suitable light

source as they cover the whole UV region. Tungsten filament lamps are rich in red radiations,

more specifically they emit the radiations of 375nm, while the intensity of Hydrogen-D

2

lamps fall below 375nm.

Monochromators

Monochromators are generally composed of prism and slits. The most of the

spectrophotometers are double beam spectrophotometers. The radiation emitted from the

primary source is dispersed with the help of the rotating prisms. The various wavelengths of

the light source which are separated by the prism are then selected by the slits such the

rotation of the prism results in a series of continuously increasing wavelengths to pass

through the slits for recording purpose. The beam selected by the slit is a monochromatic and

further divided into two beams with the help of another prism.

Sample and Reference cells

One of the two divided beams is passed through the sample solution and the second beam is

passed through the reference solution. Both the sample and the reference solutions are

contained in the cells. These cells are made up of either Silica or Quartz. Glass can’t be used

for the cells as it also absorbs light in the UV region.

Detector

Generally two photocells serve the purpose of detector in the UV Spectroscopy. One of the

photocells receives the beam from the sample cells and the second detector receives the beam

from the reference. The intensity of the radiation from the reference cell is stronger than the

beam of sample cell. This result in the generation of pulsating (or) alternating current in the

photo cells.

Amplifier

The alternating current generated in the photocells is transferred to the amplifier. The

amplifier is coupled to a small servo meter. Generally current generated in the photocells is of

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very low intensity, the main purpose of the amplifier is to amplify the signals many times so

we can get clear and recordable signals.

Recording devices

Most of the time amplifier is coupled to a pen recorded which is connected to the computer.

Computer stores all the data generated and produces the spectrum of the desired compound.

Solvent system

The solvent system must obviously be chosen for its spectroscopic properties, it must

miscible with compound to be analyzed and quantitative and qualitative determination of

mixture as fast and as efficiently as possible. As a general rule, range of solve any particular

problems, so selection must be based on different criteria.

1. Viscosity.

2. Solubility.

3. UV transparency.

4. Refractive index.

5. Boiling point.

6. Purity.

7. Inert with respect to sample compounds.

8. Corrosion resistance.

9. Toxicity.

10. Price.

A large number of solvents in order of their polarity are available .Many of them are not

suitable as solvents for different reasons. The table below makes clear why a certain

compound is not a good choice; e.g. because UV absorption or the viscosity is too high.

Some solvents are used as additives in low concentration foe certain applications: with a

small amount of an amine the solvent become basic; with an acidic compound.

Table 1.1: Minimum wavelengths at which different solvents.

Solvent

Cut off Wavelength(nm)

Water

191

Ethanol

203

Methanol

204

Ether

215

Chloroform

237

Carbon tetra chloride

257

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Solvent selection

Selection of the solvents is based on the miscibility of solvent with the compounds. The

excited states of most * transition are more polar than their ground states because a

greater charge separation is observed if the excited state reduces the degree of hydrogen

bonding.

The transitions are n and the shift of the wave length is due to the lesser extent than the

solvent can hydrogen bond to the excited state. Carbonyl groups in particular hydrogen bond

to their solvent.

For example changing from hexane to water as the solvent for propane, the absorption

maximum moves from 280 to 257nm.

Care must be taken when choosing a solvent, because many solvent absorb in the UV region.

1.6 Applications of UV Visible spectrophotometry

In research, UV-Visible spectroscopy is used more extensively in assaying than

identification. The trace metal content of an alloy, such as manganese in steel, can be

determined firstly by reacting the sample to get the metal ion into the solution as an ion. The

ion is then complexed to react so as that it is in the form that can be measured.

E.g.: Manganese as the magnate (VII) ion.

When the spectrum is recovered, the most useful piece of information is the absorbance

because if the absorption coefficient of the chromophore is known the concentration of the

solution can be calculated, and hence the mass of the metal in the sample. The same principle

can be applied to drug metabolites. Samples are taken from various sites around the body and

their solutions are analyzed to determine the amount of drug reaching those parts of the body.

A useful feature of this type of analysis is the ability to calculate very small concentrations

with extreme accuracy. It is important that the absorbance of the solution remains below two

for quantitative measurements because of the limitations of the instrument and solute-solute

interactions that can cause deviations from Beer-Lambert law. The absorption of UV light is

a feature of optical whiteners put into washing powders. The whitener absorbs radiation in

the near UV and re-emits it in the visible range.

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UV/VIS spectroscopy is routinely used for quantitative determination of analytes solution in

transition metal ions highly conjugated organic compounds.

 Solutions of transition metal ions can be colored (i.e., absorb visible light) because the

electrons within the metal atoms can be excited from one electronic state to another.

 While charge transfer complexes also give rise to colors are often too intense to be used

for quantitative measurements.

 The absorbance of the solution id directly proportional to the concentration of the

absorbing species in the solution and the path length, UV/VIS spectroscopy can be used

to determine the concentration of the absorber in the solution.

The main applications are

 Detection of functional groups.

 Detection of extent conjugation.

 Identification of unknown compound.

 Determination of configurations of geometrical isomers.

 Determination of purity of a substance.

 Determination of dissociation constant for acids and bases.

 To study the chemical kinetics of drugs.

 To study the tautomeric equilibrium.

1.7 UV Spectroscopy method Development and Validation

[14,15]

Best solvent, best detection and wavelength selection, efforts in their selection can make a

world of difference while developing method for routine analysis. Determining the ideal

combination of these factors assures faster delivery of desired results- a validation method for

quantitative determination.

To have an efficient method development process, the following two questions must be

answered

1. What are the critical components for a spectroscopic method?

The three critical components for a spectroscopic method are: sample preparation, spectral

analysis and standardization (calculations). During the preliminary method development

stage, all individual components should be investigated before the final method optimization.

This gives the scientist a chance to critically evaluate the method performance in each

component and streamline the final optimization.

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2. How should a method development experiment be designed?

A properly designed method development should consider the following important questions;

 What sample should be selected at each stage?

 What should the researcher look for in this experiment?

 What are the acceptance criteria?

According to the ICH Guidelines method validation can be defined as “Establishing

documented evidence, which provides high degree of assurance that a specific activity will

consistently produce a desired results or product meeting is predetermined specifications and

quality characteristics”.

Validation parameters

[14,15]

I. Accuracy

The accuracy of an analytical method is defined as the degree to which the determined value

of analyte in a sample corresponds to the true value. Accuracy may be measured in different

ways and the method should be appropriate to the matrix. The accuracy of the analytical

method may be determined by:

a) Analyzing of the sample of known concentration and comparing the measured value to

the true value. However, a well characterized sample (e.g., reference standard) must be

used.

b) Spiked-placebo (product matrix) recovery method : In the spiked-placebo recovery

method, a known amount of pure active constituent is added to formulation blank [sample

that contains all other ingredients except the active(s)], the resulting is assayed, and the

resulting obtained are compared with the expected results.

c) Standard addition method: In the standard addition method, a sample is assayed, a known

pure active constituent is added, and the sample is again assayed. The difference between

the results of two assays is compared with the expected results.

II. Precision

The precision of an analytical procedure expresses the agreement between a series of

measurements obtained from multiple sampling of the same homogeneous sample under the

prescribed conditions.

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Precision may be considered at three levels

a) Repeatability

Repeatability expresses the precision under the same operating conditions over a short

interval of time. Repeatability is also termed as intra-assay precision or intra-day precision.

b) Intermediate precision

Intermediate precision expresses within- laboratories variations; different days, different

analysts, different equipments, etc.

Reproducibility

Reproducibility expresses the precision between the laboratories. For these guidelines, a

simple assessment of repeatability will be acceptable. The precision of an analytical

procedure is usually expressed as the variance, standard deviation or coefficient of variation

of a series of measurement. A minimum 5 replicate sample determinations should be made

together with a simple statistical assessment of the results, including the percent relative

standard deviation.

III. Limit of detection (LOD)

The detection limit of an analytical procedure is the lowest of an analyte in the sample that

can be detected, but not necessarily quantified as an exact value. The lowest calibration

standard which produces response corresponding to the analyte should be measured.

LOD can be calculated by the formula;

LOD = 3.3 x σ/S

IV. Limit of Quantification (LOQ):

The limit of quantification is the lowest amount of analyte in the sample that can be

quantitatively determined with defined precision under the stated experimental conditions.

LOQ can be calculated by the formula;

LOQ = 10 x σ/S

V. Linearity

The linearity is the ability of an analytical procedure to the concentration (amount) of analyte

in sample within a given concentration ranges, either directly or by means of a well defined

mathematical transformation. Linearity should be determined by using a minimum of six

standards whose concentration spam 80-120%of the expected concentration range. The result

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should not show a significant deviation from linearity, which is taken to the mean that the

correlation coefficient, r

2

> 0.99, over the working range (80-120%).

VI. Range

The specified range is normally derived from the linearity studies. The range of analytical

procedures is the interval between the upper and lower concentration (amounts) of analyte in

the sample for which it has been demonstrated that the analytical method has suitable levels

of precision, accuracy, and linearity.

VII. Ruggedness

Ruggedness is the degree of reproducibility of the test results obtained by the analysis of the

same samples under different instruments, different lots of reagents, different elapsed assay

times, different assay temperatures, different days, etc.

VIII. Robustness

It is the capability of analytical method to remain unaffected by small deliberate variations in

the method parameter and provides an indication of its reliability during normal range.

Robustness testing is normally restricted to methods that are to be used repetitively in the

same laboratory.

1.8 Chemometry

[16, 17, 18]

Introduction

Chemometrics is a branch of science that is used for the extraction of the data related to

chemical and physical phenomena involved in the analytical process by the application of

statistical and mathematical methods that possesses and use mathematical tool to increase the

amount and the quality of the information that can be derived from the primary data provided

by the analytical instruments. Chemometric is an interdisciplinary field that combines

statistics, mathematical methods (high information models, ad hoc models, and analogy

models), computer science and analytical chemistry to solve multivariate problems of data

analysis Encyclopedia of analytical chemistry and others also agree to this definition.

This task demands knowledge of statistics, numerical analysis, operation analysis, etc. and in

all, applied mathematics. The Chemometry is a field which is related with various disciplines.

It is used as a guide to the chemist in extraction of maximum chemical information from

complex observation.

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Fig. 1.3: Chemometrics-spectrophotometrics.

The name chemo metric can be divided into chemo (from chemistry) and metric (meaning

measurement). Chemometric thus deals with chemical data and how to obtain information

from it.

According to the international chemometrics society, “chemometrics is a science relating

measurements made on a chemical system or process to the state of the system via

application of mathematical or statistical methods”.

The art of extracting chemically relevant information from data produced in chemical

experiments is given the name of “chemometrics” in analogy with biometrics, econometrics,

etc.

Understanding of chemistry and statistics both are necessary in chemometrics because of the

combination of both in their analytical purposes. The role of chemometry in the process of

analytical techniques is quit broad which provides complete information and knowledge

about chemicals.

In analytical chemometrics, the statistics applied to analytical and chemical data is a part of

the multidisciplinary entity which aims to expand and enhance the information by using less

material, time and human sources with aid of computers.

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However, as in all applied branches of science, the difficult and interesting problems are

defined by the applications; in chemometrics the main issue is to structure the chemical

problem to a form that can be expressed as a mathematical relation.

Chemometrics is an approach to analytical and measurement science based on the idea of

indirect observations. Measurement related to the chemical composition of a substance is

taken and the value of property of interest is inferred from them through some mathematical

relations. So, after processing of the product, measurements followed by the collection of

data chemometrics are used to gather information and to gain the real knowledge about the

product information and to gain the real knowledge about the product.

Fig. 1.4: Chemometric strategies.

Typical chemometrics strategy comprises

[19-24]

The sucessful purpose of chemometrics involves systematically design of experiments,

proper application of preprocessing (pre-treatment), calibration, diagnostics and rigorous

prediction validation. Chemometrics shows its application in the multivariate data colleection

and analysis. Various algorithms and analogous ways are avilable for processsing and

evaluating the data.they can be implemented to varaious fields, like medicine, pharmacy,

agricultural chemistry, forensic descrimination, foood control, and environmental sciences.

1.9 Experimental design

[25]

The objectives of experimental design is to plan and conduct experimentts in order to extract

the maximum amount of information from the collected data in the smallest number of

experimental runs and the selection of the points where the response should be evaluated.

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The basic ida is to change all relevant factors simultaneously over a set of planned

experiments and then connect and interpret the results using the mathametical models.

Weather the objective is screening of process factors or optimization of a chemical reaction,

the variables that influence the responses are center of attention.

There are several key reasons why the chemist can be more productive if He/She understands

the bases of designincluding the following four main areas:

i. Screening

This types of experiment involve seeing which factors are important for the success of a

process. An example may be the study of a chemical reaction, dependant on prportion of

solvent, catalyst cooncentration, temperature, PH, stirring rate, etc. typically 10 or more

factors might be relavent. This can be eliminated.

ii. Optimization

Systamatic methods can result in a better optimum, found more rapidly. Simplex is a clasical

method for optimization although several designs such as a mixture designs and central

composit desiigns can also be employed to find optima.

iii. Saving time

Fromstructural data, of existing molecule, it is possible to predict a small number of

compounds for further testing, represantative for a large set of molecules.

This allows enormous saving in time. Fractional factorial designs are good examples,

although almost all experimental designs have this aspects in mind.

iv. Quantitative modeling

Almost all experiments, ranging from simple linear calibration in analytical chemistry to

complex physical processes, where a series of observations are requiredto obtain a

mathematical model of the system, benefit from good experimental design. Many such

designs are based around the central composite design, although calibration designs are also

useful.

Principles of experimental design

[26-27]

ED first principle: Random assignment

 In planning an experiment, any assigning that would otherwise be haphazard (without an

obvious plan) should be done using a chance device, random premutation.

 Example: choose mice from cages to assign them to hyper or hypo caloric diets.

 But methods that attempt at making the allocation as “fair”as possible.

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ED Second principle: Blocking

 First sudivide the experimental material into groups (blocks of similar units; then assign

condition units seperately within each block. Each bllock should include (roughly) equal

numbers for each treatmrnt. Units are similar if they are likely to give similar values of

measurement.

 Because response from different units varies even if the units are treated identically, must

apply each treatment to several different units.

 Blocking converts unplanned, systematic variability into planned, systematic variability.

 REPLICATION: Replication needed to estimate the scale of random effect /

measurement errors.

ED third principle: Factorial crossing

 Compare two or more sets of conditions in the same experiment.

 Designs with factorial treatement structue allows you to measure interaction between two

or more sets of conditions influence the response.

 Factorial designes may be either obsrevational or experimental.

Different types of2- factor factorial designs

1. 2 experimental factors – randamoized treatements to each unit.

2. 2 obsrevational factors – cros -classification of populations into groups and get a sample

from each population.

3. 1 experiment and 1 observational factor – you get a sample of units from each population,

then use randamization to assign levels of the experimental factor (treatments), seperately

within each sample.

The designs of experiments as follows

[26]

A. Full Factorial and Fractional Factorial Design

B. Completely Randamoized Design (CRD)

C. Randomized Complete Block Design (RCBD)

D. Latin Square Design LSD)

E. Confounding

A. Full Factorial and Fractional factorial design

 In a full factorial experimental design all the combinations of extreme values are included

as experiments. If there are k variables, the number of experiment is 2

k

. For statistical

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validation, one experiiment, usually the center point is repeated atleast three times. The

experiments are performed in random in- order to eliminate systemic errors.

 The number of experiments In an experimental design grows rapidly with increasing

number of variables. When dealing with many number of variables for the purpose of

screening, a full factorial design is not realistic option. A reduced design, called fractional

factorial design is then more appropriate choice.

 For a factorial design, experiments are chosen to give the maximum amount of variation

with fewer experimental runs. The drawback is lost information caused by confounding

of main effects with interaction effects if resolution is lower than four.

B. Completely randamoized design (CRD)

 A completely randomised design is one where the treatements are assigned completely at

random so that each treatement unit has the same chance of receiving any one treatement.

 However, in CRD, any difference among experimental units receiving the same

treatement is considered as experimental errors. Thus, randamization givese every

experimental unit in the experimental material an equal probability of receiving the

treatement.

Advantages of CRD

 One of the chief advantage of CRD is that the number of replications may be varied from

treatment to treatment. Because of the flexibility, all the experimental material can be

used without any wastage.

 It allows for complete flexibility. Any number of treatments can be usedand different

treatements can be used unequal number of times without unduly complicating the

statistical analysis in most of the cases.

Disadvantages

 The chief disadvantages of the design is that it is usually suited only for small number of

treatements and for homogeneous experimental material.

C. Randomized Complete Block Design (RCBD)

 The radomized complete block design is one of the most widely used experimental

designs in agricultural research.

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 It is particularly so in agricultural and other field experiments. In such situations, the

principle of local control is adopted and the experimental material is grouped into

homogenous sub-groups. The sub-group is commonly termed as block.

 Since each block will consist the entire set of treatements, a block equivalent to a

replication.

 Hence, the term block and replication are used synonymously in case of complete block

design.

Advantages

 This design is more important and efficient because the amount of information got in

RBD is more than as compared to CRD.

 If large number of homogenous units are available, large number of treatements can be

included in this design.

Disadvantages

RBD is not suitable for large number of treatements or for cases in which complete block

contains considerable variability. It is because when the number of treatements is increased,

the block size will increase. If the block size is large it may be difficult to maintain

homogeneity within blocks. Consequently, the experimental error will be increased.

D. Latin square design (LSD)

 In latin square design there have to be as many replications as there are treatements. The

experimental area is divided into plots, arranged in a square, in such manner that there are

as many plots in each row, as there are ini each column, this number being also equal to

the number of treatements.

 The plots are then assigned to various treatements, such that every treatement occurs once

in each row, and once in each column.

 The two directional blocking in a latin square design, commonly reffered to as row-

blocking and column-blocking is accomplished by ensuring that every treatement occurs

only once in row-block and once in each column-block.

Advantages

 With a two way-stratification or grouping, the LSD, controls more of the variation than

the completely randamoized design, and the randamization complete block design.

Disadvantages

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 LSD is suitable for the number of treatements between 5 and 10 and, more than 10 to

12 treatements the design is used since in that case the square becomes two large and does

not remain homogenous.

With the increase in size of square, the experimental error is likely to increase.

E. Confounding

 Confounding in experimental designs is then a term of denote an arrangement of the

treatement combinations in the blocks in which less important treatement effects and

purposively confounded with the blocks.

 When only the portions of portions of treatement combinations are allocated to blocks

within a replication, the comparision between blocks in a replication represents some

treatement comparision, either a main effect or an interaction.

 In such cases, it is not possible to distinguish treatement comparisions from block

comparisions-such a mix up is termed as confounding.

 The principle of confounding depends on number of factors and the number of levels of

each factor undre trial.

Three – level full factorial designs (or) surface response methodology

[27]

 The three-level designn is written as 3

k

factorial design. It means that K factors are

considered, each at 3 levels.these are reffered to as low, intermediate and high levels.

These levels are numerically expressed as 0, 1, and 2. One could have considered the

digits -1, 0 and +1, but this may be confusing with respect to the 2-level designs since 0 is

reserved for center points. Therefore, we will use the 0, 1, 2 scheme.

 The reason that he three level design was preposed is to model possible curvature in the

reponse function and to handle the case of nominal factors at 3 levels. A third level for a

continious factor facilitates investigation of a quadratic relationship between the response

and each of the factors.

 Unfortunately, the three-level design is prohibitive in terms the number of runs, and thus

in terms o fcost and effort.

 In the same way the response surface methodology (RSM) is a combination of

mathemetical and statistical technique for emperical model building. By careful design of

experiments, the objective is to optimize a response (output variable) which is influenced

by several independent variables (input variables). An experiment is an series of tests

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(runs), in which changes are made in the input variables in order to identify the reasons

changes in the output response.

The basic strategy has four steps

1. Procedures to move into optimum region.

2. Behavior of therespose in hre optimum region.

3. Estimation of the optimum conditions.

4. Verification.

Analysis of variance (ANOVA)

[28-33]

The purpose and the reason for doing an ANOVA is to see there is any difference between

groups on some variable.

For example we have data on student performance in non-assessed tutorial exercises as well

as their final grading. You are intrested in seeing if tutorial performance is related to final

grade. ANOVA allows you to break up the group according to the grade and then see if the

performance is different across these grades.

ANOVA is avilable for both parametric (score data) and non- parametric (ranking/ordering)

data. Types of ANOVA

One-way between groups

 The example given above is called one-way between groups model.

 We are looking at the differences between the groups.

 There is only one grouping (final grade) which you are using to define the groups.

 This is the simplest version of ANOVA.

 This type of ANOVA can also be used to compare variables between different groups-

tutorial performance from different intakes.

One-way repeated measures

 One-way repeated measures ANOVA is used when we have a single group on which we

have measured something a few times.

 For example, we may have a test of understanding of clases. We give this test at the

beginning of the topic, at the end of the topic and then at the end of the subject.

 We would use one-way repeated measures ANOVA to see if students performance on the

test changed over time.

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Two-way between groups

 A two-way between groups ANOVA is used to look at complex groupings.

 For example, the grades by the tutorial analysis could be extended to see if overseas

students performed differently to local students. What you would have from this form of

ANOVA is :

o The effect of final grade

o the effect of overseas versus local

o the interaction between final grade and overseas/local

each of the main effects is one-way tests. The interaction effect is simply asking “is there

any significant difference in perfofrmance when you take final grade and overseas/local

acting togeather”.

Two-way repeated measures

 This version of ANOVA simple uses the repeated measure structure and includes an

interaction effect.

 In the example given for one-way between groups, you could add Gender and see if there

was any joint effect of gender and time of testing – i.e., do males and females differ in the

amount they remember/absorb over time.

 Finally, ANOVA produces the F statistic which is the ratio Between Group Variation to

the Within Group Variation.

 If the Between Group Variation is significantly greater than Within Grouup Variation,

then it is likely that there is a statistically significant difference between the groups.

 This statistcal package will tell you if the F ratio is significant or not.

1.10 Data trasformation

[34]

Very often, the measured spectra are not used “as-is,” but are subjected to various types of

data transformation before being introduced into any calibration algorithm to reduce or to

eliminate extraneous variations.

Typical transformations includes smothing (averaging sets of adjecent spectral data points),

computing a derivative (first or second derivative of the spectrum with respect to wavelength

are common), and any of several more specialized transformations of the data.

Typically, a careful analyst will inspect the spectral data both before and after performing any

transformation to ensure that the data from a coherent whoole, and that no spectra or samples

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appear to be outiners. Proper application of spectrocopic data pre-processing, to reduce and

correct interferences such as overlapped bands, baseline drifts, stattering, and pathlength

variation.

Multivariate analysis (MVA)

[35]

MVA is synonymous with the term chemometric. It is a method which takes into

consideratioon many variables acting together. The method is fast and efficient in

determination as well as exttraction of information.

MVA applied to spectral data provides the necessary tools foe analysis of data for qualitative

applications, such as classification and identification (i.e., raw material identification,

counterfeit detection and security screening).

Quantitative models can be developed from spectral data, providing predective models that

can be used to predict values for composition, and concentration of various analytes from

spectral data. Such models provide rappid means of obtaining results from analytical data that

can readily be collected on samples in their natural state (in the field, lab, or process).

1.11 Multivariate calibration (MVC)

[36]

Calibration process is necessary before starting quantitative analysis to do MVA. The term

MVC can be defined as the use of emperical data and prior knowledge for determining how

to predict unknown quantitative information Y from available measurements x, via a

mathematical transfer function. Calibration hence is described as the process of establishing

this mathematical function (f) between measured variable x and a dependent variable.

Y : f(x) = y…………………[1]

One of the simplest forms of calibration is linear regression expression

y = a+bx………………….[2]

where b is the regression coefficient and a is the intercept of the linear approximation, x is the

independent variable and y is the dependent variable (response parameter). In linear

regression one x-variable and one y-variable are used. In multivariate calibration, however,

numerous X and Y are used.

MVC process includes

 Selection of representative calibration sample set.

 Spectra acquisition and determination of reference values.

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 Multivariate modeling to relate the spectral validations to the reference values of the

analytical target property.

 Validation of the model by cross validation or external validation.

 A method which has being used widely for quantitative determination.

 Used to construct mathematical models at more that one wavelength.

Multivariate calibration Vs Univariate calibration

[37,38,39]

MVC method has got numerous advantages over univariate callibration (UVC) methods.

Some of these advantages are :-

a. Handling interferents: unlike in UVC, it does not need to remove background

correction. This means that the contribution from one does not affect the contribution

from other. This is the best straight forward advantage of MVA.

b. Selectivity: UVC works well as long as no other components in the sample analyzede

absorb light at the wavelength used, i.e., the wavelength is selective for the compound

under study. If this is not the case, all the interferences in the sample must be known. In

MVC, however, this is not the case scince using many x-variables automatically corrects

for each other’s selectivity problem, and the x-variables used thus donot need to be totally

selective.

c. Eliminate sample error : in MVC, 100% compliance and analytical accuracy is obtained

to eliiminate the sampling error and attaining 100% compliance is one of the further most

confronted. Sampling error is one of the error which are happened in the conventional

system, as process analytical technique (PAT) is merge eith the chemometrics which

reduce the sampling error and provide accuracy in samppling and precised analysis.

d. Outliners control : Multi-variates are important to detect outliners in all data analysis.

Errors are the rule rather than the exception due to for instance trivial errors, instrument

errors and sampling errors. If these errors are sufficiently large either in quantity or

quality, they can affect any meaningful result or interpretation. It may seem difficult to

detect outliners when complicated multivariate data are used, but in fact, the detection of

outliners is greatly enhanced from having multivariate data.

e. Robustness : MVC is more robust to small changes in the experimental or instrumental

parameters such as small changes in Ph, temperature or lamp intensity. Generally MVC

has many advantages over the UVC methhod, with respect to different analytical

parameters.

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MVA and Spectroscopy

[40]

Using chemometric algorithms, modern computer technologies and modern spectroscopy

provides the basis for the modern-day development of methods of chemical analysis with the

best rewards.

Using chemometric methods with UV-Visible spectroscopic measurements rresults in a fast

analytical method that is simple enough for technicians to perform. Basically, the user

“calibrates” or “trains” the instrument to perform the analysis. This training step should be

done by an expert to ensure accuracy.

“Training the chemometrics” requires that the user obtains samples representative of all the

future variations of those types of samples that the instrument willl analyze during routine

operation. Choosing the number and the appropriate samples is one of the key steps in

creating a good calibration model, i.e., the mathematical model describing the samples that

the computer will use in the future to do the actual analyses. Training the spectrometer

requires measuring the spectra from all the samples and then saving them to a computer file.

The science of chemometrics gives spectroscopists many efficient ways to slove the

calibration problem for analysis of spectral data. Chemometrics can be used to enhance

method development and make routine use of statistical models for data analysis.

Spectroscopists use software packages for spectroscopic data analysis, modeling,

classification and prediction to meet process monitoring and quality assurance needs. The

spectroscopists requirements are :

 Proper application of spectroscopic data of pre-processing, to reduce and correct

intrfrences such as overlapped bands ,base line drifts,scattering,and path length variations.

 Strong calibration and diagnostics means of sample selection and vairable selection

,stastic results caluclation to build representative and reliable models.

 Model validation and itegration means to supply rigorous prediction,measurement quality

control (QC) and real-time product quality and process monitoring.

Chemometrics provides some advantages which includes:

 Speed to obtain real information of the data.

 Extact high quality information from the fewer resolute data.

 Accuracy of the information that derives by the integration and probability of the data.

 Precision of data collection from one sensor to another sensor.

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 Clear set of information collected from the cell from all the possible order of data.

1.12 Calibration techniques involved in chemometrics

Fig. 1.5: Model types used in chemometric analysis.

Multivariate Calibration Analysis methods (MCA; Performing the calibration)

[34]

They are hard coreof chemometrics:the algorthims that relate the measured spectral data to

the samples properties. These are numerous algorithhms available foe both qualitative and

quntitative analysis.

MVC used to resolve the problem in analyzing multi-component mixture by allowing rapid

and simultaneous determination of each component in the mixture with minimum sample

preparation ,responsible accuracy and precision and without the need of lengthy separation.

The accuracy and precision is achieved without prior seperation of the components. This

implies that these methods are time and cost effective. Although various methods are there in

the appliction, two of them will be treated here for the convinence of the study.

Bilinear models

 In this the data is arranged in data matrices in such a way that each vertical column has

vairables and each horizontal row has the samples.

Bilinear chemometrics techniques include following:

Classical least squares regression analysis (CLS)

[40,41,42,43]

CLS is one of the traditional regression algorithms, that depends on the Beer Lambert law

with assistance of close tools mixtures of components with overlapping spectra will be

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resolved. Beer’s law describes the relation between two variables, the spectral response (A)

and the constituent concentration (C), and two constants, the intercept (a) and the regression

coefficient (b).

A λ

1

= CaKa λ

1

+ CbKb λ

1

………………………….(1).

According to Beer’s law absorbance of multiple constituent at a given wavelength is additive.

A λ

2

= CaKa λ

2

+ CbKb λ

2

…………………………(2).

A λ

1

= CaKa

1

+ CbKb

1

.....CnKn λ

1

+ E λ

1

……..(3).

A λ

2

= CaKa

2

+ CbKb

2

…..CnKn λ

1

+ E λ

2

……...(4).

Solving the equation for K matrix one can use the resulting best fit least squares lines (S) to

predict concentrations of unknown analyte.

Advantages of CLS

 Based on Beer’s law.

 Unlike to other techniques calculations are relatively simpler.

 The CLS method can be applied for moderately complex mixtures such as binary and

terinary mixtures.

 The calibration method doesnot need the selection of wavelengths necessarily. Once the

number of wavelengths to be used exceeds the number of constituents, any number can be

utilized, even upto the entire spectrum.

 Making use of large number of wavelength results CLS in giving an averaging effect to

the solution. This further leads to less susceptability to noise in the spectra.

Disadvantages of CLS :

 It needs understanding the entire composition (i.e., concentration of all constituent) of

mixtures in the calibration mode.

 This method is not applicable for which chemically interact within the mixture.

 It is exposed or succeptible to base line effects.

Principle component regression (PCR)

[40,41,42,43]

It is a factor analysis method. Problems which usually are not sloved by traditionally

regression methods will be sloved better with PCR. It is a well known pronounced and known

method.as a procedure the steps to be followed are two.

Step 1: linear combination of the original variable will be combined to optimize a certain

criterion. The explained variation in the data are also called latent variables. In short terms no

correlation is needed between regression models.

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Step 2 : In the second step, MLR (Multiple Linear Regression) is applied to the newly

obtained latent variables. When co linearity between original variable occurs, interpretation

of the variation is observed in the data set than plotts of original variable selected by MLR.

Since the scores are orthogonal, the MLR solution is stable and therefore the PCR model does

n ot suffer from collinearity effects. It is the belief of some data analysis purists that PCR is

superior to PLS since it forces analysts to better understanding their data and its

preprocessing (transformations) before the application of a regression procedure.

Advantages of PCR

 PCR doesn’t need the selection of wavelength most of the time the whole spectrum were

used.

 Averaging effect: as one uses great number of wavelenngths the averaging effect will be

attained decreasing the chance for spectral noise can be utilized for mixtures with large

constituents (highly complex). PCR also enables, sometimes to figure out samples with

constituents which are not present basically (originally) in the calibration mixture.

Disadvantages of PCR

 The caluclation is slower if compared to Classical Least Square (CLS).

 Optimization needs knowledge of PCA i.e., interpretation and understanding the model is

not a simple task.

 It needs large number of sample for the accurate calibration.

Inspite of the above inconveniences, PCR has widely been applied for the spectrophotometric

resolution of mixtures comprising two or more serious overlapping spectra.

Partial least square regression (PLSR)

In PLS technique, the regressions are caluclated with least square algorithms. The aim of the

PLS is th launch a linear link between two matrices, the spectral data X and the reference

value Y.

PLSR also sometimes reffered to as projection to latent structures or just PLS, models both

the X and Y-matrices simultaneously to find the latent (or hidden) variables in X that will

best predict the latent variables in Y. PLSR components are similar to principle component,

but will be referred as factors.

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PLSR maximizes the covariance between X and Y. in this case, convergance of the system to

a minimum residual error is often achieved in fewer factors than using PCR. This is in

contrast to PCR, which first performs PCA on X and then regresses the scores (T) vs. the Y

data. PLSR may be carried out with one or more Y variables, meaning that multiple Y

responses can be used during regression modeling. A conceptual illustration for PLSR is

shown graphically in fig hindu)

X – Y relationship outliners

[44,45,46]

X – Y relationship outliners plots the t-scores from X vs. the u-scores from Y and is used for

two main purposes, to detect possible outliners and to determine the optimal number of

factors to use in a PLSR model.

This plot is unique to the PLSR algorithm. Since PLSR attempts to maximize the covariance

between X and Y variables in the first caluclated factors, the t vs. u plot should ideally show a

straight line relationship. Samples that deviate noticebly are potential outliners.

The t – score are the new coordinates of the data points in the X-space, computed in such a

way that they capture the part of the structure in X which is most predictive for Y. the u-score

summerize the part of the structure in Y which is explained by X along a given factor, and are

related to T by a constant. The – Y relationship outliners plot for ideal and outliner shown in

the fig (hindu)

The – Y relationship outliners plot is also useful for detecting nonlinear relationships that

may exist in the data. This may suggest a different preprocessing should be considered.

Advantages of PLSR

 PLS are the combination of both full spectra coverage of CLS and the partial composition

of regression of inverse least square (ILS).

 The decomposition and the regression steps follow the one step process.

 The calibration process is more robust and accurate to caluclate the unknown sample

which make the PLS more beneficial to detrmination of the unknown sample.

 Prediction of the elements of interest that are not present in the original calibration

mixtures also be done by this PLS method.

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 PLS having the superiority to predict the elements of interest rather than other methods

that were sucessfully appplied for spectral quantitative analysis. PLSR provide much

better outcome than PCR method.

Disadvantages of PLSR

 Despite of wide variety of advantages are observed iin PLSR which also has some sort of

disadvantages.

 It is quit slower process to caluclate the data which might be slower than some classical

methods.

 The difficulty in understanding and the interpretation of data make the method more

complicated.

 A huge number of samples are required to calibrate the methods.

 Difficult to obtain the calibration sample and it might avoid the collinear constituent

concentration.

From several methods one can use the best model which fits the calibration. It is also possible

to use two or more methods to attain comparable results.

Multiway models

 Multiway models are basically used when the data is multivariate and linear in more than

two dimensions.

 Bilinear techniques could not provide sufficient data which was provided by multivariate

techniques.

 The methods like multiway principlecomponent analysis (MPCA) and multiway partial

least square (MPLS) improve the process understanding and summerizes its behavior in a

batch-wise manner and are therefore recognized as tools for monitoring batch data.

The multiway methods

Parallel factor analysis (PARAFAC)

Parallel factor analysis (PARAFAC) is a decomposition method used for the modeling of

three-way or higher data and is mainly intended for data having congruent variable profiles

within each batch.

Parallel factor analysis – 2 (PARAFAC-2)

 PARAFAC-2 can handle data variable profiles that are shifted or/are in a different phase.

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 In PARAFAC trilinearity is a fundamental condition where as PARAFAC-2 enables

trilinearity. However, it should be noted that PARAFAC may be used to fit nonlinearity

to some extent in one mode only in cases where data shifts from linearity are regular.

 Both the techniques asre mainly applied for analyzing chemical data from experiments

that form a 3-way or higher data, structure, for example, chromatographic data,

fluoresence spectroscopy measurements, temporal varied spectroscopy data with

overlapping spectral profiles, and process data.

Tucker – 3 model

 This can be used for exploring n way array data as it consists of n modes of loading

matrices.

 The generality of the Tucker – 3 models, and the fact that it covers the PARAFAC model

as a special case, has made it an often used model for decomposition, compression, and

interpretation in many applications.

N-Partial least square (N-PLS)

 For handiling a multiway data extension of PLS method namely N-PLS was introduced.

 It basically uses dependent and independent variables for finding the latent variables

which describes maximal covariance.

Chemometric software’s

Statistical software’s for data analysis, modeling, classification and prediction.

Ex : Design expert, Unscambler, Matlab, SAS and etc.,

1.13 Applications of chemometry

[47,48,49]

 Chemometry allows the interpretation of multivariate data to predict the concentration of

each drug in a mixture.

 Identification of source of chemical pollutions.

 Analytical evolution of quality and quntity of some food products.

 Evolution of the analytical potential of coupled methods.

 Evolution of effectiveness of sample pre-treatment methods for trace elements

determination.

 Quality control of laboratory results, standardization and interpretation of laboratory tests

in disease monitoring.

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 Diagnostics and detection of significant change in patients condition during medical

treatment and clinical care, prediction of the futre medical state of the patient.

 Drug synthesis and dovelopment and design, tools for drug discovery, SAR, drug

mechanism,

Merits

 Fast, cheap and non-destructive (advantages over HPLC).

 Accurate quntitative analysis in the precense of heavy intrference by other analytes.

 High selectivity.

 No need of seperation procedures in the drug determintions.

 Combination of chemometrics with analytical chemistry can enhance th signal to

noise(S/N) ratio.

 Improve selectivity to determination.

 Optimize exmerimental conditions.

 Raise analytical opereration efficiency.

The advert of rapid, in expensive computers has perimitted the prolifiration of

computationally intensive calibration methods. One must choose between many competing

methods nad algorithms for aplication to any particayular calibration challenge. The need to

develop logical rules to aid in calibration method selectiom is imperitive since,as technology

progress, technologist are moving closer to implementing multivariate and higher order

sensors capable of self-calibration. The best calibration method for futre prediction of

accuracy and precision will be the one that employs the simplest model that fits the data to

the arbitary presicion, the method that incoprates a basis set that best mimics the data will

construct the simplest model.

Applications of chemometrics in pharmaceutical field

[49,50,51]

Chemometrics method is widely used in different areas of pharmceutical fields like

manufacturing, quality evaluation and quality assurance.

Powder flow properties

The experimental results for flow properties of pharmaceutical powders obtained were

correlated with the NIR spectrum.PLSR method was used for correlation to determine the

sample density for transmission of Raman spectroscopy. A quick and accurate way to

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determine the pharmaceutical properties of granules(particle size and angle of repose) and

tablets (porosity and hardness), in the formulation of pharmaceuticals by application of

chemoinformetric and NIR spectroscopy using PSR.

Moisture content

Moisture content is very important for the pharmaceutical product. Accurate amount of

moisture should be available in the pharmaceutical product to dovelop a successful drug and

formulations which key to stability the product. The determination of moisture content is one

of the most important and several multivaraite regression method are used to analyze the

water content.PLSR widely used by several researchers to determine the water content in the

pharmaceutical products.

Drug content determination

Detemination of drug content in pharmaceuticals is the matter of concerns for its importance.

The content of the in the pharmaceutical product is challenging to determine and necessary to

obtain the pharmacological responses of the drug. Spectroscopy is the technique choice

which combined the MVR method to analyze the content of the drug. PSR is the best

prediction to determine the active substance content in the low dosage tablets by using rather

than the PCR.

Lyophilisation

Broadly used technique for the formulation of a huge range of pharmaceutical products

especially vulnerable to degradation in aqueous solutions like peptides, proteins or complex

organic molecules. The objectives of Lyothilisation are to manufacture materials with

superior self-stability and which are unaffected after reconstitution with water.

Determination of the residual moisture content from the glass vials is done by NIR spectra.

Curve fitting analysis and PLSR models have been developed to enumerate both hydrate and

surface water content in lyophilized product.

Powder blending

Blending of powder is the crucial step to manufacture the pharmaceutical product. This

blending step is mainly done among the API powders and the excipients that are necessary to

prepare a phamaceutical dosage forms. So blending steps posesses a major role in the

pharmaceutical analysis prospect of view. Without homogenous blend of the API and

excipents it is impossible to get a uniform dosage form. But the blend homogeneity is

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problematic. Methods like HPLC,NIR spectroscopy and UV-Vis are widely used in the

determination of content during the blending.

But these methods are destructive, codtly and time consuming. PSLR has the better

prediction than principal component analysis for these analytical purposes.

Drying

Drying is also a critical step for the pharmaceutical manufacturing. It is employed in

progression such as granulation. In conventional way, if the representative sample passes then

its assumed that the entire sample having same quality. But it does not guarantee that the

entire product is controlled. To minimise the uncertainly a new system is established where

quality control check is done by the design of the experiments. So the chemometrics with the

spectroscopy where PCA and PLS both are used to ressolve the analysis of product after

drying. Here PCA is used for the identification of the products and PLS is used for the

qulification of the pharmaceutical product which reveals the 100% control of the quality of

the product.

1.1.1 Drug profile

1.1.2 Itraconazole

[52, 53, 54, 55]

 Chemical structure

• IUPAC name: 4-[4-[4-[4-[{2-{2,4-Dichlorophenyl}-2(1H-1,2,4-triazol-1-methyl)-1,3-

dioxolan-4-yl]methoxy]phenyl]-1-piperazinyl]phenyl]- 2,4 Dihydro- 2-(1-methylpropyl) -

3H-1,2,4-triazol-3-one

 Molecular formula: C

35

H

38

Cl

2

N

8

O

4

 Molecular weight: 705.64 g·mol

−1

 Description: 1) It is an odorless powder with a bitter taste prepared by

2) Chemical synthesis.

3) It is an optically active compound.

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 Solubility: Soluble in water and alcohol.

 Category: It is an orally active triazole drug with a broad spectrum of Antifungal

activity.

 Melting point : 170°c

 Log P : 5.66

 pKa : 3-4

 Protein binding : 99.8%SS

 BCS Classification : Class II

 Oral bioavailability : 55%

 Oral absorption : >85%

 Plasma half-life : 20 hrs

 Metabolism : Extensively metabolized in liver (CYP 450) into a large number

of metabolites; Hydroitraconazole, the major metabolites.

 Food interactions :Milk and calcium containing dairy products, Iron, Antacids

or Aluminum salts.

 MOA : Cell membrane synthesis inhibitors.

Fig. 1.6: Schematic representation of mechanism of action of Itraconazole.

 Treatment :Dermatophytosis, Superficial candidiasis, Pityriasis versicolor,

Oculomycoses, Subcutaneous mycoses, Systemic mycoses

Mycoses (Paraccidioido mycoses, Cryptococcal menengitis,

Histoplasmosis, Coccidioidomycoses, Blastomycoses)

Pulmonary and extra pulmonary blastomycoses.

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1.1.3 Terbinafine

[52,53,54,55]

 Chemical structure

 IUPAC name : (E)-N-[6,6-Dimethyl -2-hepten-4-ynyl]-N-methyl-1-naphthalene

methanamine

 Molecular formula : C

21

H

25

N

 Molecular weight :327.90 g/mol

 Description :It is slightly bitter, white, Odorless, that is obtained by chemical

Synthesis.

 Solubility :Organic solvents, Alcohols, sparingly soluble in aqueous buffers.

 Category :Terbinafine is a synthetic Allylamine antifungal agent.

 Melting point :>202°c

 LogP : 5.53

 pKa : 8.94

 Protein binding :99%

 BCS Classification :Class II

 Oral bioavailability :50%

 Oral absorption : ~80%

 Plasma Half-life :11-17 hrs

 Metabolism :Extensively metabolized in liver (CYP2C9, CYP1A2, CYP3A4)

With the production of about 15 metabolites

 MOA :Cell membrane synthesis inhibitors

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Fig. 1.7: Schematic representation of mechanism of action of terbinafine.

 Treatment 1) Dermatophyte infections of skin and hair

2) Dermatophyte infections of finger and toenails

3) Pityriasis versicolor

4) Yeast infections of skin

2.1. UV Analytical method development for Itraconazole (ITR):

1. Poonam A. Salunke et.al., (2016):

[56]

Reported work on UV method development and

validation of Itraconazole bulk and capsule formulation. Spectroscopic method was

carried out by using acidic ethanol as solvent. Itraconazole detection wavelength was set

at 262nm.The linearity was found to be in the range of 2-12μg/ml. the results of analysis

have been validated statistically as per ICH guidelines.

2. G. Soundarya et.al., (2016):

[57]

Reported work on Method development and analytical

method validation of Itraconazole by UV. Spectroscopic method was carried out by using

methanol as solvent. The absorbance maximum was observed at 261nm. The linearity

was found to be in the range of 2.5-25 μg/ml. the results of analysis have been validated

statistically as per ICH guidelines.

3. M. Koteswara Rao et.al., (2014):

[58]

Reported work on Method development and

validation of Itraconazole by UV. Spectroscopic method was carried out by using

chloroform as solvent. The detection wavelength was observed at 267nm. The linearity

was found to be 1-10 µg/ml. the results of analysis have been validated statistically as per

ICH guidelines.

4. S. K. Sahoo et.al., (2011):

[59]

Reported work on First derivative UV method development

and validation of Itraconazole in its Bulk and Dosage form. Spectroscopic method was

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carried by using methanol as solvent. The detection wavelength was observed at 261nm.

The linearity was found to be 20-60Âµg/ml. the results of analysis have been validated

statistically as per ICH guidelines.

5. Shalin K. Parikh et.al., (2010):

[60]

Reported work on Development and validation of UV

spectrophotometric method for the estimation Itraconazole bulk drug and pharmaceutical

formulation. Spectroscopic method was carried out by using methanol as solvent. The

absorbance maximum was observed at 262nm. The linearity was found to be 4-14μg/ml.

the results of analysis have been validated statistically as per ICH guidelines.

2.2. UV Analytical method development for terbinafine (TER)

1. K. Basavaiah et.al., (2016):

[61]

Reported work on Analytical methods for determination

of Terbinafine hydrochloride in pharmaceuticals and biological materials. Determination

of TER by UV in raw materials, tablets and creams by using methanol as solvent. The

detection wavelength was observed at 223nm. The linearity was found to be 8-24 µg/ml.

the results of analysis have been validated statistically and recovery studies were

performed as per ICH guidelines.

2. K. Basavaiah et al., (2013):

[62]

reported work on Stability Indicating UV-

Spectrophotometric Assay of Terbinafine Hydrochloride in Dosage Forms. Determination

of TFH in bulk drug and tablets are based on the measurement of absorbance of TFH in i)

0.1M HCl, ii) 0.1M acetic acid. The absorption maxima 222nm and 282nm were

observed respectively. The linearity was found to be 0.2-4.0 μg/ ml and 2.0-50.0 μg/ ml

respectively. The results of analysis have been validated statistically and recovery studies

were performed as per ICH guidelines.

3. K.Krupa Patel et al., (2012):

[63]

reported work on Development and validation of the

UV-spectrophotometric method for determination of terbinafine hydrochloride in bulk

and in formulation. The method was carried by using methanol as solvent. The detection

wavelength was observed at 282nm. The linearity was found to be 8-24 µg/ml. The

results of analysis have been validated statistically and recovery studies were performed

as per ICH guidelines.

4. S.Pritam Jain et.al., (2011):

[64]

reported work on Development and validation of the UV-

spectrophotometric method for determination of terbinafine hydrochloride in bulk and in

formulation. The method was carried by using water as solvent. The detection wavelength

was observed at 283nm. The linearity was found to be 5–30 μg/ml. The results of analysis

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have been validated statistically and recovery studies were performed as per ICH

guidelines.

2.3. UV-Visible Spectrophotometry with chemometric analysis

1. T. Eticha et.al., (2018):

[65]

reported work on Chemometric-Assisted Spectrophotometric

method for the simultaneous determination of Ciprofloxacin and Doxycycline Hyclate in

pharmaceutical formulations was performed by chemometric techniques like PCR and

PLS by using Minitab 7.1 version software and Unscrambler® X 10.5 version software.

Two sets of standard mixtures, 25 as a calibration set and 9 as a validation set, were

prepared. For both drugs the linearity ranges of 1–10 μg/ml for ciprofloxacin and 5–25

μg/ml for doxycycline hyclate. Absorbance values were recorded at λ max of each drug

272nm for CIP and 275nm for DOX with r

2

of 0.998 (PCR) 0.998 (PLS), 0.994 (PCR)

0.994 (PLS), RMSEP values 0.142 (PCR) 0.143 (PLS), 0.219 (PCR) 0.208 (PLS) of

Ciprofloxicin and Doxycycline hyclate respectively.

2. Umang H. Shah et.al., (2017):

[66]

reported work on Chemometric Assisted

Spectrophotometric methods for simultaneous determination of Paracetamol and

Tolperisone Hydrochloride in pharmaceutical dosage Form by using chemometric

techniques like CLS, PLS, and PCR by using Unscrambler® 10.3.0.80, MATLAB

(R2009A) 7.8.0.347 software. Twenty five (25) mixed solutions were prepared for the

chemo metric calibration as training set and sixteen mixed solutions were prepared as

validation set. The linearity ranges of 5-25 µg/ml for Paracetamol and 1.5-7.5 µg/ml for

Tolperisone hydrochloride. RMSEP values 0.022 (PCR) 0.214 (PLS) 0.259 (CLS), 0.079

(PCR) 0.075 (PLS) 0.113 (CLS) of Paracetamol and Tolperisone hydrochloride

respectively.

3. Lawan et.al., (2014):

[67]

reported work on chemometric – assisted UV

Spectrophotometric method for determination of Metformin HCl and Glyburide in

pharmaceutical tablets was performed by chemometric techniques like PCR, PLS by

using Unscrambler 10.1X software. The absorbance was measured in the wavelength

range of 200-400 nm and linearity ranges used to construct the calibration matrix were

selected in the ranges 40-200 µg/ml (MET), 1-10 µg/ml (GLY). RMSEP values of MET

& GLY were found to be 1.806, 0.256 (PCR) & 1.802, 0.185 (PLS) respectively.

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4. Khushboo et.al., (2014):

[68]

reported work on multivariate chemometric assisted

spectrophotometric techniques for simultaneous determination of Amlodipine besylate

and Telmisartanin marketed tablet formulation was performed by chemmometric

techniques like PLS, CLS by using Design expert 8.0.4 software and matlab R2007

software. The absorbance was measured for the 24 synthetic mixtures involving 19

calibration and 10 validation sets are scanned at 230-302 nm at every 3 nm. Calibration

range was found to be 1-10 µg/ml (AMP, 8-80 µg/ml (TEL) with a r

2

of 0.999 (PLS)

0.998 (CLS), 0.999(PLS) 0.998 (CLS) for Amlodipine besylate and Telimisartan

respectively.

AIM AND OBJECTIVES

3.1. Aim

 The primary aim of this work is to show how spectroscopy and chemometrics in

conjugation can be used as an alternative to HPLC for quantitative and qualitative

analysis.

 The study is mainly aimed at analyzing overlapped binary combinations (Itraconazole and

Terbinafine) by using chemometrics-assisted UV-Visible spectrophotometric techniques

without prior separation of them, and comparing the obtained results with those obtained

from official, or reported methods.

 To determine the quantities in binary combinations (Itraconazole and Terbinafine) with

the Classical Least Square method (CLS), Partial Least Square (PLS) method, Principle

Component Regression (PCR) method.

The aim has been to combine chemometric methods with spectroscopic analytical techniques

in a simple and straight forward fashion to obtain easily applied methods applicable in real

life.

3.2. Objectives

 The USP has suggested that the reduction in amount of reagents and materials which are

routinely used in HPLC assays that have the potential to cause harm to human health and

environment. Therefore, spectrophotometry as a simple, robust, quick and low cost

method may be a good alternative if it is combined with multivariate calibration methods

for determination of a complex mixture in pharmaceutical quality control laboratories.

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 The quality control of dosage form requires reliable and quick analytical methods.

UV/Visible spectrophotometry is by far the instrumental technique of choice, owing

mainly to simplicity, often demanding low cost equipment.

 Simultaneous quantitative analysis of pharmaceuticals containing multi-active

compounds is difficult to perform by classical spectrophotometric methods due to

overlapping spectra.

 The objective of this work is to illustrate how spectroscopic techniques can be used in

conjugation with chemometric tools in order to achieve rapid and efficient analytical

methods for the analysis of solutions.

 These spectrometric methods consisting of spectroscopic analysis, a high level of

automation and chemometric data evaluation can lead to rapid methods having a high

analytical capacity, and the term high capacity analysis (HCA) is suggested for these

methods.

 HCA methods might very well be alternatives to HPLC and in some cases even

outperform the chromatographic methods.

 Chemometric methods with spectroscopic measurements which will results in fast

analytical method that is simple enough for technicians to perform.

 The use of experimental design as a means to obtain good quality data and combine with

multivariate data analysis to eliminate possible covariance between variables.

 Use of multivariate data analysis is to reduce relatively large number of variables to a

small number of orthogonal factors by treating with statistical methods.

 These methods might very well be alternatives to HPLC and, in some cases, even

outperform the chromatographic methods.

Plan of work

 Literature survey

 Procurement of selected drug for study

Reference standards were procured from pharmaceutical company.

 Selection of analytical technique

UV spectroscopy.

 Estimation of drugs in combination by UV-spectrophotometric method

1) Selection of solvent system.

2) Selection of wavelength.

3) Selection of different conditions.

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4) System suitability parameters study.

5) Analysis of standard laboratory mixture to see feasibility of proposed method.

6) To adopt selected method on marketed formulation.

o Accuracy

o Linearity/range

o Precision

o LOD

o LOQ

o Ruggedness

o Robustness

o specificity

 Preparation of standard solutions of drugs in required concentrations, analyzing using

UV-Visible spectrophotometer and optimizing the method using surface response method

by Design Expert 11.1 software.

 Using the above obtained optimized results carrying out chemometric analysis with

suitable methods for both pure drug and pharmaceutical formulation by using

Unscrambler 10.4X statistical software.

 Comparison of models to conclude with with the best.

Fig. 4.1: Plan of work.

5.1. MATERIALS AND METHODS

Spectrophotometric measurements were carried out on a computerized double beam

Shimadzu 1800 series UV/Visible spectrophotometer and a pair of 1cm matched quartz cells

was used to measure absorbance of solution over the range 200-400nm. The subsequent

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statistical manipulation was performed by transferring the spectral data to Microsoft Excel®

2017 program and processing them with standard curve fit package and matrix calculations.

Data were processed on an Intel® Pentium V equipped with essential statistical trail version

programs (Design Expert® 11.1 provided by Stat Ease and Unscrambler® 10.4X provided by

Camo Software) were used for optimization and linearity of method.

Pharmaceutical grade Itraconazole and Terbinafine provided as a gift samples by K.P Labs

Pvt Ltd., Hyderabad, India, were used as working standards. All other chemicals used were

analytical grade. Methanol was used as a solvent. The Myitra-T tablets containing (each

100mg of Itraconazole and 250mg of Terbinafine) manufactured by Biophar life science

Pvt.Ltd, India was purchased from the local market and subjected to analysis by proposed

method.

5.2. Procedures

5.2.1. Preparation of standard solutions for linear calibration models Itraconazole and

Terbinafine standard stock solutions

Accurately weighed quantity of Itraconazole (ITRA; 10mg) and Terbinafine (TERB; 10mg)

were transferred to two separate 100ml volumetric flask. Dissolved in sufficient quantity of

Methanol, and then diluted up to the mark with same solvent (stock solution 100 µg/ml). A

training set of standard mixture solutions containing 2-30 µg/ml ITRA and 2-45 µg/ml TERB

was made from stock solutions. A calibration curve was prepared by plotting absorbance vs.

concentration at their respective λ

max

.

5.2.2. Preparation of sample solution

Twenty tablets of Myitra-T were weighed and crushed to fine powder. An accurately weighed

powder sample equivalent to 1000 mg was weighed, transferred to 100ml volumetric and

dissolved in sufficient quantity of Methanol. Sonicated for 15 mins and filtered through

Whatmann filter paper. Finally, the filtrate was made up to volume with Methanol.

5.2.3. Stanard solutions for multivariate calibration

A multivariate calibration model for the analysis of individual drugs was carried out using

UV-Visible spectrophotometer. In order to obtain the calibration matrixes for applying PCR

and PLS analysis, solutions of each of ITRA and TERB were prepared with 2-35 µg/ml and

2-45µg/ml respectively. These ranges were previously verified to obey Beer’s – Lambert’s

law for each of the ITRA and TERB in the selected solutions. Absorption spectrum was

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recorded for each solution between 200 and 260nm (digitalized every 2.0nm interval) with

Methanol as blank were subjected to the least square regression (PCR and PLS) analysis in

order to obtain the calibration model for each.

The optimum number of factors used in PLS algorithm is an important parameter towards

obtaining the best prediction performance. This means modeling the system with the

optimum amount of information and avoiding over fitting or under fitting (over fitting and

under fitting means that the defined model must neither over estimate nor under estimate

values, and is made by fitting constants). Cross validation procedures were applied to

calculate the number of factors which gives as much as possible information from the

absorbance data to measure the property of interest (concentration). Thus the range between

230-330 nm at 2nm interval was selected as two drugs shows closely overlapping bands and

optimum number of factors was extracted. The absorbance data matrices were constructed

from the raw spectra containing absorbance values at wavelength ranges between 230-330

nm at 2 nm intervals. The spectrum of the single sample consists of the individual absorbance

value for each wavelength at which sample was measured. Generally, MLR techniques (PLS

and PCR) employ data that is organized as matrices either as a row vector or column vector.

The pure component mixtures were then prepared by mixing known amount of ITRA with

TERB vice versa. In different varied proportions used in order to verify the precision of the

method for analysis of such mixtures and matching the commercial multi-drugs with those

having the same or approximately comparable concentrations. Composition of calibration

samples for PLS and PCR were given in Table.

Table 5.1: Composition of calibration for multivariate analysis in µg/ml.

Set 1

Set 2

Set 3

Set 4

Set 5

Set 6

Set 7

Set 8

ITR

TER

ITR

TER

ITR

TER

ITR

TER

ITR

TER

ITR

TER

ITR

TER

ITR

TER

0

1

0

3

0

5

0

7

0

9

0

11

0

13

1

0

1

3

1

5

1

7

1

9

1

11

1

13

2

0

2

1

2

3

2

5

2

7

2

9

2

11

2

13

3

0

3

1

3

5

3

7

3

9

3

11

3

13

4

0

4

1

4

3

4

5

4

7

4

9

4

11

4

13

5

0

5

1

5

3

5

7

5

9

5

11

5

13

6

0

6

1

6

3

6

5

6

7

6

9

6

11

6

13

7

0

7

1

7

3

7

5

7

9

7

11

7

13

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5.2.4. Data processing

Data were processed on computer equipped with essential statistical program for PLS and

PCR calculations. Multivariate calibration techniques are then used to construct a

mathematical model (calibration model) that relates the multivariate response (spectrum) to

the concentration of the analyte of interest, and such model can be used to effectively predict

“unknown” Y- values from the measurements of new X- variables.

5.2.5. Validation of the chemometric methods

Precision

The intra-day, inter-day, analyst to analyst variation, reproducibility of the method and

accuracy were determined by assaying one sample with ITR (10 µg/ml) and TER (20 µg/ml)

in replicates (n=6) for each drug for the applicability of proposed chemometric methods.

Precision is reported as percent relative standard deviation (%RSD). Intermediate precision

expresses within the laboratory variations: different days and different analysts. The

procedure followed for the method precision was repeated on two different days, and by two

different analysts. Six sets of standard solution each containing ITR and TER for evaluation

of intermediate precision were prepared using above said concentration was analyzed six

times.

Recovery studies

To check the validity of the proposed methods (PLS and PCR), recovery studies were carried

out by standard addition technique. Appropriate volumes of the standard stock solutions of

ITR and TER at three different concentration levels of ITR 15, 20, 25 µg/ml and TER

25,30,35 µg/ml.

6. RESULTS AND DISCUSSION

6.1. Preliminary investigation of data structure in chemometric techniques

The original laboratory (experimental) observations are taken as a raw data and well

tabulated systematically for software analysis like mathematical and statistical purpose. The

pure ITR and TER data are tabulated, discussed and analyzed first (calibration). Eventually

the dosage form (commercial) mixture is analyzed (prediction). Subsequently various graphs

are sketched from the tabulated data using software such as MS-Excel and Design Expert

11.1 software. These preliminary graphs may help us in reaching a final conclusion about the

data structure and the proper selection of the used analytical method.

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Overlay plots of the given spectra

This will make apparent gross outliners and clear clusters in the given responses (absorption

in our case) Figure.6.1 & 6.2 represent the overlay plots for the standard compounds (ITR

and TER respectively) at different concentration levels.

From the two graphs, one can readily observe that no obvious studied overlapping and/ or

crossing of spectra lines are observed. The lowest concentration is at the top outside with

longest amplitude. A calibration curve was plotted for absorbance versus concentration (ITR:

2-30 µg/ml; TER: 2-45 µg/ml) shown in figure.6.4 & 6.5

Table 6.1: Linearity table for ITR & TER.

S. no

Conc of ITR

(µg/ml)

Absorbance

Conc of TER

(µg/ml)

Absorbance

1

2

0.295

2

0.342

2

4

0.375

4

0.578

3

6

0.448

6

0.614

4

8

0.525

8

0.939

5

10

0.596

10

1.006

6

15

0.86

15

1.395

7

20

1.114

20

1.794

8

25

1.369

25

2.171

9

30

1.576

30

2.558

10

-

35

2.871

11

-

40

3.23

12

-

45

3.661

Slope

LOD

LOQ

0.047

0.075

0.622857

0.305492

1.88744

0.92573

Figure 6.1: Overlay absorption curves of Itraconazole in calibration range (2-35 µg/ml).

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Figure 6.2: Overlay absorption of Terbinafine in the calibration range (2-45 µg/ml).

Figure 6.3: Isobestic point of the absorption curves of Itraconazole and Teerbinafine

(20µg/ml).

Figure 6.4: Linearity graph for Itraconazole (2-35 µg/ml).

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Figure 6.5: Linearity graph for Terbinafine (2-45 µg/ml).

Statistical analysis

6.2. Surface response methodology

Surface response method is used to determine the relationship between dependent and

independent variables.

Statistical optimization was performed using Design Expert-11.1 software.

According to 3

2

experimental designs, over view of the experimental trial and observed

responses.

Method of experiment

Experimental design 3

2

(two-factor and three-level) factorial design was employed for

namely, ITR (A), TER (B), as prime selected independent variables (factors), which were

varied at three levels (low, middle and high concentrations). Different trial concentrations of

ITR and TER were prepared based on the trial proposal of 3

2

factorial designs. The

combination chart for all proposed trial and investigation as dependent variable (responses).

Design-Expert 11.1 software (Stat-Ease Inc., USA) was used for generation and evaluation of

the statistical experimental design by surface response method (screenshots shown in the

Figure.6.6) the matrix of the design including investigated factors and responses is shown in

Table 6.2.

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Table 6.2: 3

2

factorial design and their observed response values with combination of

Itraconazole and Terbinafine.

Where; -1= low concentration of ITR and TER (2, 2 µg/ml); 0= middle concentration of ITR

and TER (10, 10 µg/ml); 1= high concentration of ITR and TER (20, 20 µg/ml)

The effect of various independent variables upon measured responses were modeled using

following mathematical model equation involving independent variables and their

interactions for various measured responses generated by 3

2

factorial design was given in

Table.6.3. 2D and 3D were shown in

Figure 6.6: Screen shot for design expert software.

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Figure.6.7: 2D plot with respect to terbinafine.

Pale blue color indicates low absorption, whereas orange color indicates higher absorption.

Figure.6.8: 3D plots with respect to Terbinafine.

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Figure 6.9: 2D plots with respect to Itraconazole.

Pale blue color indicates low absorption, whereas orange color indicates higher

concentration.

Figure.6.10: 3D plots with respect to itraconazole.

The effect of various independent variables upon measured responses were modeled using

following mathematical model equation involving independent variables and their

interactions for various measured responses generated by 3

2

factorial responses.

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Table 6.3: Response surface model values obtained by design expert software.

Response

Equation model

Adjusted R

2

Predicted R

2

C.V

R1 (ITR)

0.1103+0.0764A+0.0138B

0.9954

0.9930

5.32

R2 (TER)

0.157754-

0.014255A+0.147563B+0.00

1560AB-

0.001656A

2

+0.011189B

2

0.9984

0.9953

3.24

Where A= Absorbance of ITR ; B= Absorbance of TER

General conclusion

Using surface response methodology by Design-Expert 11.1, the method is found to show

significant relationship between independent and dependent variables using the given

concentrations.

6.3. Calibration step

The purpose of the calibration step is to make a standard relationship between the response

(absorbance) and concentration. The absorbencies of well-known concentrations is measured

at wide range of wavelengths (200-400 nm).

It is clear that the relationship to be set will strongly help to predict the concentration of

unknown analyte to be analyzed. In a univariate case we may take one Y (response

component) for each X value (concentration ingredient). Unlike to this in case many Y

components that measured under the same conditions for many X values are treated. In this

process, to attain the suitable calibration for the entire data (matrix) many steps are followed.

Finally the efficiency of the calibration step is tested and the precision of determinations will

be seen.

The calibration is done for pure ITR and TER. In all the cases about 10 rows and about 200

column matrices are used. The calibration step done by using zero order absorbencies. Thus

the steps to be followed are the same in all studied compounds. Overlay plot of all samples

containing mixture of Itraconazole and Terbinafine in various concentrations [ITR (2-

35µg/ml) TER (2-45µg/ml)].

Principle component regression (PCR) method

In the basic concepts of the PCR are presented, for the original data of absorbances (A) and

concentrations (C) of analytes the mean-centering denoted by A

O

and C

O

, were calculated.

The next step is to calculate the covariance dispersion matrix of the centered A

o

. After that

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the normalized eigenvalues and eigenvectors were calculated by using the square covariance

matrix. The number of the optimal eigenvectors is obtained by using the highest values of the

eigenvalues. The final PCR calibration equation is used for the prediction of analytes in

samples.

Partial least square (PLS) method

The PLS calibration method is done by the composition of both concentration and absorbance

matrix into latent variables,

A = TP

T

+ E and C = UQ

T

+ F

The vector b is given as,

b = W (P

T

W)

-1

Q ,

where W represent a weight matrix.

The next step is to use the linear regression

C = a+b x A,

Where the constant has the form

a = C

mean

– A

T

mean x b

Figure 6.11: Overlay plot of all samples containing mixture of Itraconazole and

Terbinafine in various concentrations (ITR (2-35µg/ml) TER (2-45µg/ml)).

In the techniques, calibration or regression was obtained by using the absorbance data matrix

and concentration data matrix for prediction of the unknown concentrations of ITR and TER

in the binary mixtures and pharmaceutical formulations.

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The predictive ability of a model can be defined in various ways, the screenshots of

Unscrambler 10.4X are given in Figure.6.11. The most general expression is the standard

error of prediction (SEP) which is given by the following equation,

SEP =

Where, is the added concentration of drug

is the predicted concentration of drug

N is the total number of synthetic mixtures

In order to test the proposed techniques, the sets of synthetic mixtures containing the two

drugs in variable composition were prepared. The results obtained in the application of PLS

and PCR methods to the same binary mixture are indicated in table.6.4.and assay results for

the commercial preparations are given in table.6.5.

In the table.6.6.r

2

is defined as the correlation between constituent concentrations and shows

the absorbance effects relating to the constituents of interest. The obtained r values in the

methods are close to 1, this means no interference is coming from the other constituents in

this set of synthetic mixtures.

Another statistical value is the standard error of calibration (SEC) and the calculation of this

value realized by using following equation.

SEP =

Where, is the added concentration of drug

is the predicted concentration of drug

n is the total number of synthetic mixtures and

p is the number of components in the mixtures

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Figure 6.12: Screen shots of Unscrambler 10.4X used for chemometric analysis.

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Table 6.4: Summary of statistics in PLS and PCR methods for ITR and TER in the

mixture.

Drug

ITR

TER

RMSEP

RMSEC

R

2

Intercept

Slope

PLS

PCR

PLS

PCR

PLS

PCR

PLS

PCR

PLS

PCR

0.482

0.496

0.495

0.426

0.495

0.433

0.275

0.426

0.998

0.997

0.985

0.992

0.099

0.221

0.094

0.225

0.987

0.952

0.969

0.964

Table 6.5: Assay results for commercial preparation.

Formulation

PLS

PCR

mg/tab found

%purity

mg/tablet found

%purity

ITR

TER

494.62

491.54

98.924

98.308

492.29

490.26

98.658

98.052

Validation of the chemometric methods

Precision

The method precision of the PLS method is expressed as %RSD and it is found to be 0.4460

for ITR and 0.114 for TER, results are tabulated in the Table 6.6. The PCR method precision

is expressed as %RSD and it is found to be 0.3791 and 0.114 for ITR and TER respectively.

The intraday and interday precision was tested for the applicability of proposed chemometric

methods. The results were found to be less than 2.0

Table 6.6: Precision data.

S. no

Itraconazole

Terbinafine

PLS

PCR

PLS

PCR

1

2

3

4

5

6

4.92

4.9

4.95

4.9

4.92

4.95

4.15

4.9

4.93

4.9

4.93

4.85

11.93

11.91

11.94

11.91

11.93

11.94

11.92

11.9

11.92

11.94

Mean

SD

%RSD

4.923

0.022

0.4460

4.906

0.0186

0.3791

11.926

0.0136

0.114

11.923

0.0136

0.114

Recovery studies

To check the validity of the proposed methods (PLS and PCR), recovery studies were carried

out by standard addition technique was used to observe the selectivity of the proposed

chemometric methods. Appropriate volumes of the standard stock solutions of ITR and TER

at three different concentration levels were added to the tablet solutions, respectively. This

procedure was repeated six times for each concentration level. The recovery results, standard

deviations and relative standard deviations were calculated and illustrated in Table 6.7

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Table 6.7: Recovery studies.

Itraconazole Terbinafine

% of

Target

AA

PLS PCR

AA

PLS PCR

80

100

120

Mean

SD

%RSD

4

5

6

AF

%R

AF

%R

4

5

6

AF

%R

AF

%R

3.93

4.01

3.96

4.93

4.94

4.91

5.96

5.98

5.94

98.2

100.2

99.0

98.6

98.8

98.2

99.3

99.6

99.0

3.92

3.98

3.95

4.96

4.93

4.95

5.94

5.95

5.92

98

99.5

98.7

99.2

98.6

99.0

99

99.16

98.6

3.93

3.91

3.90

4.98

4.95

5.01

5.82

5.91

6.01

98.5

98.2

98

99.6

99.2

100.1

99.1

99.4

100.1

3.95

3.92

3.91

4.97

4.95

5.01

5.99

5.82

6.01

99.5

99.1

99.0

99.8

99.2

100

99.9

98

100

98.9

0.645

0.656

98.86

0.441

0.446

99.35

1.12

1.127

99.38

0.650

0.654

AA=Amount added; AF =Amount found; %R = Percentage recovery; %RSD=Relative

standard deviation

SUMMARY AND CONCLUSION

 The contents of several pure component mixtures and commercial dosage forms were

simultaneously determined using chemometric assisted UV-spectrophotometric

measurements together with PLS and PCR calibration analysis.

 Significant relationship of responses was found for the given concentrations using Design

Expert software 11.1 version.

 Good recoveries were found for pure Itraconazole in the range of 2-35 µg/ml in

combination with Terbinafine. While Terbinafine showed good recovery in the range of

2-45 µg/ml.

 Both the PLS and PCR are sufficiently good method for analysis of individual

compounds and their binary mixtures with satisfactory recovery in commercial

formulation. Hence, they can be applied for simultaneous determination of Itraconazole

and Terbinafine.

 Chemometric algorithms, in combination with modern computer technologies and rapid

spectroscopic analysis, provide the basis for the modern-day development methods of

chemical analysis that are fast, simple to use and environmentally friendly.

 Chemometry is a growing field with huge gains in quality and cost reduction for

development of pharmaceutical formulations.

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ACKNOWLEDGEMENT

It is a moment of gratification and pride to look back with sense of contentment at the long

travelled path, to be able to recapture some of the fine moments, to be able to think infinite

number of people, some who were with me from the beginning, some who joined with me at

some stage during the journey whose kindness, love and blessing has brought this day. I wish

to thank each one of them with all my heart.

Among them there are two persons to whom I am grateful indebted and delighted to express

my gratitude for the successful completion of this thesis.

My heartfelt gratitude and respect to my esteemed teacher and research supervisor

Dr.K.Srinivas Reddy Associate Professor, Department of Pharmaceutical Analysis, Vaagdevi

college of Pharmacy, Ramnagar, Hanmakonda. Whose inspiring support, valuable

suggestions, confidence, constant motivation and guidance made me to complete this project

successfully.

It is a great affection and appreciation I acknowledge my esteemed teacher and research

supervisor Dr.K.Praveen Kumar, Associate professor, Department of Pharmaceutical

Analysis, for his keen interest in my work and constant co-operation.

Am grateful to Dr.C.Srinivas Reddy, Principal, Vaagdevi college of Pharmacy, Ramnagar,

Hanmakonda. Whose encouragement, constant support, dynamic approach boosted me

morally, which helped me to a very great extent in completion of my dissertation.

My humble thanks to Mr.Nagaraju, and supporting staff of Vaagdevi college of Pharmacy for

their kind cooperation.

Special thanks to Dr.Y.Sravan Kumar, Head, Department of Pharmaceutics for providing the

active pharmaceutical ingredients.

I express my humble thanks to my friends Shiva Ranjani, Sindhu, Aishwarya Kiron, Mounika

and senior Ravindar Reddy for their support at the time when I needed in doing my project

work.

I owe a special dept to my father, mother Sri&Smt Bikkasani.Ramesh Chowdary&Ahalya for

their encouragement and love that served as a soure of inspiration, strength and determination

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at each and every front of my life to transport my dreams into reality. And also sincere thanks

to my beloved brother Mahesh chowdary for the emotional and healthy support which has

strengthened me.

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