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History of Paracetamol

Describe about the Examination of ATR-FTIR Spectral Resolution Settings in the Classification of Pharmaceutical Suspensions with Chemometric Data Analysis?

Paracetamol is also known as acetaminophen. The structural formula of Paracetamol is N-[4-Hydroxyphenol], which is considered as an effective metabolite of Phenacetin. It is considered as a more effective analgesic (pain killer) and antipyretic (fever reducer) in the present medical market. Paracetamol was launched in the market for the first time on 1878 in Germany. Paracetamol is considered as the major ingredient in the various cold and flu drugs. This element is equally available for other disorders such as headaches, body aches and other minor pains (Akiny, 2013). Based on various surveys it can be analysed that Paracetamol has rare adverse side effects. However, cases such as hypersensitivity may result to skin rash and blood dyscrasias. These parameters often results to other disorders such as agronulocytosis, leukopenia, neutropenia, thrombocytopenia and pancytopenia.

Structure of Paracetamol

Figure: Structure of Paracetamol

In the United States of America, it was estimated that tons of paracetamol was consumed by every individual with a range of 10/20,000 every year. Simple access of paracetamol highlights towards the possibilities for developing mismanagement. One of the most effective evidence of mismanagement highlights towards the consequences of fatal overdoses by the children. Evidence of high death rate among the children due to overdose of paracetamol concentration was quite evident within the country (Panicker et al. 2013).

Paracetamol suspension, which was prescribed to the children, has a unique role as compared to the other available medicines in the market. It was analyzed that paracetamol is considered as the first line option for both fever and pain relief efficiently, which was not available by the other medicines available in the market previously. However, it can be analyzed that the children were highly susceptible to the induced hepatic damages, which was higher as compared to the adults. Harmful effects are registered to occur due to intense reaction of ingestion. As a result, the children suffering from acute disorder of malnutrition or cytochrome P450 enzyme induced drug was considered to be subjected to specialized treatment. It was determined from several surveys that toxic paracetamol concentration was found to be linked to the coincidental ingestion of paracetamol suspension. It was analysed to be highly uncommon in the market as compared to the other products in the market. Paraecetamol was found to be effective against the mild to moderate pain, and was not subjected to any alterations due to the actions of the other drugs. The normal dosage of paracetamol was considered to range around 10mg/kg and 15mg/kg among the children. 10mg/kg was recommended as an effective dosage in the United Kingdom, which was prescribed for six times a day (Zimmermannand Baranović, 2011).

Composition of Paracetamol Suspension

In the ancient and medieval times, the popular antipyretic agents were considered the compound containing white willow bark. These chemicals were known as salicins, which led to the development of the well-known bio-chemical substance, aspirin. Cinchona bark was considered a versatile element, which was efficiently used to prepare anti-malarial drug ‘Quinine’. The element also found to have antipyretic functions. One of the most effective processing associated to the fields of ancient medicines is the isolation of salicin and salicyclic acid. This isolation was first performed in the middle and late 19th century, which was mainly accompanied by the Bayer chemist- Sir Felix Hoffmann. The work was previously accomplished by a French Chemist; Charles Frederic Gerhardt, but was discarded.

The researchers encountered an effective challenge for producing antipyretic drugs as Cinchona trees became scare. During this time, the researchers focused on developing effective alternatives, which would efficiently replace the usage of Cinchona, based antipyretic effects. By 1880s, two alternative antipyretic agents were developed, i.e. Acetanilide (by 1886) and Phenacetin (by 1887). It was this time when Harmon Northrop synthesized Paracetamol by the protocol of ‘reduction of p-nitrophenol with tin in glacial acetic acid’. However, this synthesized paracetamol was not used medically used for two decades. Later in 1893, paracetamol was extracted in urine of individual treated with phenacetin. In 1899, Parecetemol was found to be an effective metabolite of acetanilide.

The Institute awarded the New York City Department of Health for the Study of Analgesic and Sedative Drugs to study about the various issues associated to the usage of analgesic agents. It was during this time that Bernard Brodie and Julius Axelrod were assigned I order to investigate regarding the association of non-aspirion products with the development of methemoglobinemia within the patients. In 1948, these scientists effectively highlighted towards linking the usage of acetanilide with methemoglobinemia. This helped to determine the analgesic effect of acetanilide, which mainly targeted in metabolizing paracetamol. Hence, the scientists focused towards the usage of paracetamol, as it was provided to be intoxicating for acetanilide.

The product went on sale for the first time in the market of the United States of America in 1955 under the brand name of Tylenol. By 1956, 500mg tablets of paracetamols went on sale in the United Kingdom for the first time. Thus, the formula of paracetamol got an international fame and acceptance, which flourished the pharmaceutical market hugely. In the United Kingdom, the brand name of paracetamol was Panadol, which was manufactured by Frederick Stearns & Company (which was a subsidiary company of Sterling Drug Inc.). Panadol was mainly available only by prescription of the registered medical practitioners. The drug was prescribed for relieving pain and fever. It was advertised as ‘gentle to the stomach’, since the other analgesic tablets, which were previously available in the market, contained aspirin (causes stomach irritation). The product was further extended and was made available for the paediatric patients. The Panadol Elixir was released by 1958 (June), which resulted to a huge market boom in both the United Kingdom and the United States of America.

Method to prepare Paracetamol Tablets

By 1963, paracetamol was effectively added to the British Pharmacopoeia, and thereby gained the popularity to a huge extent. Being an analgesic agent accustomed to fewer side effects along with low interaction with the other pharmaceutical agents, paracetamol effectively flourished in the international market as the prime prescribed agent.

Focusing on the recent studies of paracetamol it can be analyzed that the United States of America has lost its patent on paracetamol. Since the patent of paracetamol was expired to the country, thereby many pharmaceutical companies from all across the world targeted it. Wide varieties of generic versions were derived, which was widely available under the Drug Price Competition and Patent Term Restoration Act of 1984. Although certain Tylenol preparations are effectively protected until 2007, the U.S. patent 6,126,967 filed September 3, 1998 was efficiently granted for the ‘Extended’ release of acetaminophen particles.

The composition of two most commonly used paracetamol suspensions are as follows:


(% w/v)



Propylene glycol


Monohydrated Citric Acid


Disodium Hydrogen Phosphate


Sodium Chloride


Injection Water

100 ml

In this composition, it is observed that the heating time involved in 20 mins and he heating temperature associated is 120 degree Celsius. Moreover, there is no protocol associated for recrystallization in order to retain the solution state.

The other type of composition generally observed is stated below:


Amount (g/100 ml)







Citric acid monohydrate


Hydrochloric Acid 10%

q.s. to hydrogen strength 6.0

NaOH 4%

q.s. to hydrogen strength 6.0

Water for Injection


There are two basic methods which are associated to the preparation of paracetamol. These two protocols are completely dependent upon the starting material. The two commonly used protocol to generate paracetamol are as follows:

a) Preparation of Paracetamol from 4-Amino Phenol and Ethanoic Anhydride

b) Preparation of Paracetamol from Phenol

Between the two protocols, the first protocol, (i.e. Preparation of Paracetamol from 4-Amino Phenol and Ethanoic Anhydride) is analyzed thoroughly, as this is the most effective and widely used method.

50cc conical flask was placed and thereby introduced to 1.0g of Amino Phenol solution. This was followed by the addition of 9cc doubled distilled water. The solution was stirred briskly at the room temperature. The focus for the protocol highlights towards suspending the solid within the solution.

A fumed cupboard was provided, which was added with 1.1cc (1.17g)  Ethanoic Anhydride. The suspension was stirred thoroughly and thereby mixed by shaking. After 30 seconds, the solid gets dissolved and the shaking is continued until the formation of clear precipitate.

After 10 minutes, the filtration of the precipitate was filtered. This was performed by suction and cold water, which generally helps to wash the entire material. The remnant is dried off thoroughly. Crystallization was mainly done from the distilled water, which helped to purify the products. The re-crystallized product was collected through suction and the clear solution was allowed to cool to the room temperature. The resultant yield was determined by the re-crystallized product, which was dried between the filter papers. In the course of the experiment, the melting point of the paracetamol in the dry state and the re-crystalized state was monitored thoroughly throughout the experiment.

It was estimated that the net yield of paracetamol attained from this protocol (Preparation of Paracetamol from 4-Amino Phenol and Ethanoic Anhydride) yields nearly 45% of the product. The concentration of the obtained product can be effectively magnified by following the principle of filtration at a lower temperature (rather than maintaining in normal room temperature). The product prepared from this protocol is of immense medical usage and is of great market value.

Paracetamols ate available in many forms of brands. However, over usage of paracetamol causes serious harm. The maximum amount of paracetamol, which can be prescribed for an adult, is 1 gram (1000 mg) per does along with 4 grams (4000 mg) per day. Paracetamols tend to damage the liver largely. Patients found to be drinking alcoholic beverages for more than 3 times a day are often devoided from in taking paracetamol. Apart from this, paracetamols are generally used in the cold, allergy or pain medications, but always under an effective monetarization of the concerned medical practitioner. It is often observed that paracetamols are often found to have a combined effect with the other medicines.

Overdose of paracetamols results in various medical symptoms, such as appetite, vomiting, nausea, stomachache, sweating and signs of confusion or weakness. These symptoms are considered as the primary symptoms of paracetamol reaction. On adverse situation, these symptoms might develop dark urine, yellowing of skin, yellowing of the whites of eyes and excessive pain on the upper stomach. Thus, the commonly observed side effects include low fever with acute nausea, stomachache and loss of appetite due to liver dysfunction. Furthermore, the patient might also be subjected to Jaundice to a huge scale.

As per the latest medical review dosage of paracetamol it can be analyzed that general dosing incudes 325 to 650 mg every 4 to 6 hours or 1000 mg every 6 to 8 hours orally or rectally. On the other hand, the dosage for paracetamol 500 mg tablets includes two pieces orally for every 4 to 6 hours.

To avoid from drug interaction, the medical practitioner can efficiently transform the drug lines of vitamins, herbal products and minerals. The medical practitioner should carefully monitor every new medication.

It can be said that classification of paracetamol generates great interest among the researchers. According to Royal Pharmaceutical Society it can be said that Paracetamol suspensions can be classified based on different criteria, such as product, Container size, legal status, the maximum amount of the drug that can be sold to a person and the place, where the drug can be sold (Rodriguez et al. 2011). However, it is found that in order to secure the safety of the consumers and make a distinction of the original drug from the counterfeit paracetamol, the classification of paracetamol is very important.

Counterfeit drugs are actually the fake medicines. These counterfeit drugs generally contaminated or might contain inactive or wrong ingredients. Sometimes, these drugs could have right active components but in a wrong dose. These counterfeit drugs are illegal and banned in worldwide because of the wrong impact on the health. From the researches, it is found that most of the developed countries as well as the underdeveloped countries in the world are facing a serious threat from the counterfeit medicines. From different surveys in the United States of America, it is observed that the administration of counterfeit drugs and foods today make up more than 10 percent of the international market of food and medicine.

The impact of counterfeit paracetamol could be proved as fatal for the public health. The increasing marketing of counterfeit can be considered as the global challenge as the consumption of counterfeit drugs can cause potential public health risk hazard. In addition, the increasing marketing of paracetamol can also affect the income of the consumers as well as reduce the intensive to work in the research, innovation and development of the drug. Therefore, the classification of paracetamol is greatly interesting to the researchers.

In recent days the technique of FTIR has not been widely used in the classification of the paracetamol suspension, rather it has been primarily employed for the general quantification of the widely available paracetamols ibuprofen and paracetamol in the formulation of tablets. The result thus obtained helps in understanding regarding the capability of the transmission of the instrument FTIR that was very much useful for assessing the amount of active pharmaceutical ingredients (API) (Pelletier et al. 2012). Moreover, it is observed that maintenance of API is an important criterion for the marketed products as well as use of solvent during the processing in industries. Thus, the use ATR-FTIR spectroscopy helped in determining the endometrial tissue, which helped in understanding the difference between the benign and the malignant tumors. It is observed that the use of ATR-FTIR spectroscopy is efficiently implicated to assess endometrial tissues, which in turn demonstrate the potentiality of the ATR-FTIR to differentiate between malignant and benign tumor as well as the various subtypes. It is also observed that in most of the countries the use of the configuration as a mandatory method to recognize medicines has been considered as concern in the pharmacopoeia.

It is unfortunate that not many works has been done yet to find out the counterfeit paracetamol, however, it is observed that sufficient work has been done to find out a counterfeit “antimalerial” tablet, which is artesunate. To find out the counterfeit Paracetamol Raman Spectroscopy is vastly used with the proper application of Chemometrics, which include hierarchical cluster analysis and principal component analysis (PCA) (Kalantri et al. 2010). To conduct this task, a spectrum of pure artesunate (antimalerial tablet) was noted down and the functional groups, which are correspondent to the Raman bands, were recognized. A Raman spectrum band includes a range of characteristics while each characteristic is associated with a specific vibration mode. The presence or absence of these Raman bands are considered as the key points, which are used in the classification of the genuine component from the original. It is observed from previous researches that Raman spectroscopy helps to bring success in research field, whenever used properly. When in drug research Raman spectroscopy is used besides the Chemometrics, it is found that this combitaion of technique is the best to differentiate between counterfeit drugs and real drugs. These techniques were helpful in order to offer chemical fingerprints of a large variety of counterfeits, which are beneficial in the findings of the relationship between dissimilar samples.

According to the researchers, UV Spectrophotometric multi-wavelength method could be used to estimate paracetamol in tablet dosage and bulk drug (Fransson et al. 2010). Laser-induced breakdown spectroscopy was used on the classified pharmaceutical tablets to find out the feasibility of LIBS for the commonly used pharmaceutical tablets. It is found that Multivaret chemometrics could be equally recruited in the data analysis. While performing the experiment it is observed that each laser pulse hits a fresh portion by using the motorized X-Y (translational stage) to translate the samples. The lens system attached with the spectrograph machine was involved to collect the signal and in this case 500 resolving power was used. Many spectra were noted down for each of the samples. It can be said that in the spectral analysis peaks which indicates hydrogen, nitrogen, carbon and oxygen were showed in the spectra, where strong peaks, which are similar to titanium, were exhibited in the “coated spectra” (Connatser et al. 2010).

The application of Multivariate Chemometrics Analysis (Principal Component’s analysis) on the total samples used to understand the characteristics of the critical spectra (in LIBS data). This application is also helpful to check the cluster behavior of the “pharmaceutical samples” (Wang et al. 2011). 3PCA is able to reveal dimensions, which defined the variables found in the observed data set. In addition, the use of spectrophotometric determination paired with chemometrics (PCA) was also proved helpful to determine paracetamol and other drugs as well as biological fluids, such as Urine. To determine the tablet PLS (Partial least square) was used, however for the biological fluids other techniques such as PARAFAC (Parallel Factor Analysis). Other researchers also used this process, where monoclinic powdered paracetamol was recognized using Raman Spectroscopy method and FTIR.

Paracetamols has been use as one of the major consumer relief that has been produced by various companies. The classification of paracetamol provides safety for the consumers (Nanubolu and Burley, 2012). Paracetamols are primarily used as a part of the prescription, which are primarily given to patients suffering from various kinds of diseases. Thus, the study helped in examining six major paracetamol products in using the technique of FTIR-ATR and the data thus produced helped in analyzing using various statistical tools. Thereby this will help in achieving the present goal of the experimental process (Connatser et al. 2010). The counterfeit drugs or the fake drugs are those drugs, which are generally contaminated or contain wrong ingredients or may contain active ingredients but not in sufficient amounts. From previous surveys, it was found that 10% of the total drugs sold in the world market of medicines are filled up with counterfeit drugs or the fake drugs. It is found that most of the pharmacists prescribe paracetamol as the medicines to the patients who are suffering from headache, fever or malaria. Therefore, the corrupt people target to prepare fake medicines to acquire the market. However, these fake drugs have the potentiality to cause public health hazard (Panicker et al. 2010). Therefore, it can be said that the classification of paracetamol is necessary to secure the safety of the public health. In this context Raman Spectroscopy and FTIR is helpful to differentiate between the counterfeit drugs and the original drugs. However, it is found that use of Raman Spectroscopy and FTIR with the addition of Chemometrics can be proved as more beneficial than using Raman Spectroscopy and FTIR individually (Fransson et al. 2010). Chemometrics with the help of Raman Spectroscopy and FTIR can provide more information about the drug as well as quantifying and recognizing the best drug from the Counterfeit drug (Kolesov et al. 2011). However, it was found that not many works have been performed by using Raman Spectroscopy and FTIR but from the previous researches it can be said that the Raman Spectra is helpful to recognize the best peak for the active component present in the drug (Zimmermann and Baranović, 2011).

To compare between two resolutions (resolution 1 and resolution 8) in order to find out which resolution is best for the classification of paracetamol suspensions along with the chemometric data analysis.

Fourier Transform Infrared Spectroscopy (FTIR) is an advanced technique that is used in the field of biophysics for obtaining the infrared spectrum of emission and absorption of a gas, liquid and solid. The FTIR spectrometer collects the high spectral resolution data over a huge spectral range. This makes the FTIR more advantageous than the dispersive spectrometer that is known for measurement of a narrow range of wavelengths at a certain point of time (Aksoy, Uckan and Severcan, 2013). Fourier Transform Infrared Spectroscopy is the method of infrared spectroscopy that is usually preferred by the researchers. In this kind of spectroscopy, the infrared radiation is passed through a sample, and it is found that some of the infrared gets absorbed by the sample while the other portion of the radiation gets transmitted. The spectrum that is the resultant of this phenomena represent the molecular transmission and absorption. A molecular fingerprint of the sample is thus created. Just like the principle of fingerprinting, where no two molecular structure produces the same pattern of the fingerprint, no two dissimilar molecular structures give rise to a similar infrared spectrum. This aspect makes infrared spectroscopy the useful method for different kinds of analysis (Ferraro, 2012).  Fourier Transform Infrared Spectroscopy can be used for receiving information valuable for different researches. It can identify materials unknown to the researchers. It can effectively determine the consistency and quality of an unknown sample as well as the amount of components present in the particular mixture that is tested for (Bassan et al. 2012).  Fourier Transform Infrared Spectroscopy has been considered as the workhorse technique for analysing samples in the laboratory. A spectrum of the infrared represents a fingerprint received for a sample with the absorption peaks that has corresponding to the vibration frequencies between the bonds present between the atoms that together make up the material (Trevisan et al. 2012). Due to the fact that each separate substance has the unique combination of atoms, it is obvious that no two compound can produce the same infrared spectrum in an exact manner. Thus, Infrared Spectroscopy results in a positive identification, that is, qualitative analysis, of different types of materials. Moreover, the size of the absorption peaks found in the spectrum is the direct and suitable indication of the exact amount of material that is present in the sample. With the presence of modern and advanced software algorithms, infrared is the excellent tool in the field of quantitative analysis (Ferraro and Basile, 2012).

The older infrared instruments were such that they used dispersive methodology. Fourier Transform Infrared Spectroscopy is the chosen method over dispersive methods of spectral analysis due to many reasons. Firstly, it is a technique that is non-destructive in nature. It gives a precise measurement process requiring no calibration from external agency. The speed is increased, and a scan can be collected every second. Sensitivity is also more. With this methodology, it is possible to co-add together the one-second scans for having an impact on random noise. Optical throughput is greater, and this method is a mechanically simpler one (Allewell, Narhi and Rayment, 2013).  Fourier Transform Infrared Spectroscopy is developed for overcoming shortcomings in the dispersive instruments. The main issue was a slow process of scanning. A method that can measure all infrared frequencies simultaneously was needed. The solution was found by developing the interferometer. The interferometer gave signals that had all infrared frequencies encoded in it. The signal is being able to be measured in a quick method in order of one second (Serdyuk, Zaccai and Zaccai, 2013).

The normal instrument of a Fourier Transform Infrared Spectroscopy comprises of the following parts (Nadeau, 2012):

The source- The infrared energy is emitted from the black-body source. This originating beam then passes through the aperture that controls the energy that is presented to the sample and then to the detector.

The interferometer- the beam then enters the interferometer. Over here, the spectral encoding is undertaken. The signal that is the resultant of this phenomena then moves out of the interferometer.

The sample- The next step is the entering of the beam into the sample compartment. Over here, it is either reflected off of the sample surface or transmitted through. This depends on the analysis undertaken. It is in this that specific energy frequencies are absorbed. The energies are the unique features of the samples.

The detector- the beam then moves forward to the detector so that the final measurement can be done. The detectors can be made in such a way that those interferogram signals that are special can be measured.

The computer- Digitization of the signal is very much significant for proper analysis of data. The digitization is done and after that sent to the computer where the process of fourier transformation takes place. The final spectrum is thereafter presented for interpretation. Further manipulation can also be done.

Instrumentational process of FTIR, Source: (, 2016)

Figure: Instrumentational process of FTIR, Source: (, 2016)

Layout of  spectrometer, Source: (, 2016)

Figure: Layout of  spectrometer, Source: (, 2016)

Since there is a requirement of a relative scale for an intensity of absorption, a background spectrum needs to be measured too. This is a measurement that has no sample in the beam. A comparison can be done with the sample placed in the beam for determining the percent transmittance. This method results in the spectrum having removal of all instrumental characteristics. The result is that all the spectral features are due to the sample solely. One single background measurement can be taken for multiple sample measurements since this spectrum is feature of the instrument itself (Narhi, 2013).

Some of the advantages of the FTIR are as follows (Campbell, 2012):

Speed- Since all the frequencies are measured in a simultaneous method, measurements are done in a very short time and as a matter of fact in seconds rather than in minutes. This can be referred to as Felgett advantage.

Sensitivity- This aspect is significantly improved in FTIR. The detectors that are used are much sensitive and higher performance is found by the optical throughput. This is referred to as Jacquinot advantage. The low noise level is achieved, and fast scans can take place. Reduction of random measurement noise to the desired level is also achieved. This is referred to as signal averaging.

Mechanical simplicity- There is not much possibility of any severe mechanical breakdown as there is only one moving the part in the instrument.

Internal calibration- The FTIR employ the HeNe laser as the calibration standard. This is an internal wavelength. This gives an advantage known as the Connes advantage. Self-calibration is the advantage as there is no need to do calibration by the user.

These advantages make Fourier Transform Infrared Spectroscopy measurements extremely reproducible and accurate. It is thus a very reliable technique for identifying all samples in a positive manner (Gajjar et al. 2013). Even small contaminations can be identified, and this makes the Fourier Transform Infrared Spectroscopy a valuable tool for quality control. Thus, it can be concluded that the Fourier Transform Infrared technique has definitely brought important practical advantages to infrared spectroscopy and biophysical techniques. Challenging problems can be tackled easily (Alsenaidy et al., 2014). In the present research, it is, therefore, justified to use FTIR method.

In recent days, Attenuated Total Reflectance (ATR) is widely used as an FTIR sampling tool throughout the world. ATR helps in both qualitative and quantitative analysis of the sample with little or no sample preparation that helps in speeding up the process of sample analysis. ATR is a form of mid-infrared spectroscopy, which helps in the process of identification, characterization and quantification of the respective sample (Hseih et al. 2013). IR spectroscopy uses an analytical technique for obtaining the spectra of solid, liquid and gas from a very wide range (Baker et al. 2014). IR spectrometers emphasize on analyzing the structure of solids, liquids and gases by directly transmitting the infrared radiation through the sample. ATR primarily uses the principle of total internal reflection, which results in the formation of evanescent waves. ATR provides an advantage over the other traditional methods of sampling technique by providing small sampling path length and a higher penetration power (Hseih et al. 2013).

 Schematic representation of the ATR-FTIR sampling technique

Figure: Schematic representation of the ATR-FTIR sampling technique

In the method of ATR sampling, the infrared beams are directed into a crystal having a higher refractive index. The infrared light thus enters the ATR crystal and thereby produces reflection through the internal surface of the crystal (Milosevic, 2012). This leads to the formation of evanescent waves which is projected in a directly into the sample in contact with the ATR crystal in an orthogonal direction (Kazarian and Chan, 2013). The beam is reflected in such a way that a portion of the infrared light. The infrared beam passes through the ATR crystal in such a way that a portion of the infrared light is reflected at least once off the internal surface, which remains in direct contact with the sample (Hughes et al. 2013). Some of the energy thus produced in the production of the formation of the evanescent waves are absorbed by the sample, and the reflected radiation is returned to the detector. ATR provides a higher penetration power, which typically ranges between 0.5 µm and 0.2 µm. Thus, with the exact values being determined by the wavelength of light, the angle of incidence and the refractive indices of the ATR crystals and the medium is measured (Hayes et al. 2014). By varying the angle of incidence, a number of reflection may be varied. In recent days, the modern infrared spectrometers can be easily converted for further characterization of the samples by mounting the ATR accessory directly into the spectrometer’s samples compartment. The materials, which are commonly used for the manufacture of the ATR crystal, include germanium, zinc selenide and potassium bromide. In the case of determination of the infrared region of the electromagnetic spectrum, silicon is primarily used. The mechanical properties of diamond make it an ideal substance for ATR study because analyzing the properties by ATR helps in understanding the nature and structural orientation of very hard solids (Hayes et al. 2014). Depending upon the nature of the sample and the type of the spectrometer, the shape of the crystal varies. The various factors associated with affecting the final spectrum of the sample includes:

Critical angle

Refractive Index

Penetration depth

Wavelength of the infrared beam

Type of crystal used

Total number of reflections produced

Path length

Schematic representation of the crystal used in ATR-FTIR technology

Figure : Schematic representation of the crystal used in ATR-FTIR technology

Fourier Transform Infrared Spectroscopy (FT-IR) has been widely used as an analytical tool, which uses the principle of IR-spectroscopy. FT-IR has been primarily implemented for the structural identification of the functional groups like aldehyde, ketones, acids, etc. In addition to it, FT-IR is also used for the determination of characteristic spectra produced by the substance, which helps in determining the crystalline structure and the orientation of the functional groups (Rakshit et al. 2013).

The instruments help in analyzing different kinds of substances, which include all classes of solids, liquids and gases. However, the method of sample preparation associated with the FT-IR is a little bit complex process. In the case of liquid samples, the liquid used in the technology needs to be filled within the liquid cell having a suitable path length. In the case of solid samples, the solids need to be diluted with the IR-inactive KBr, which is popularly known as the KBr pellet. In spite of having a drawback in the method of sample preparation, the method of FT-IR has tremendous benefits over the other traditional and conventional methods of spectroscopy. The primary advantage of using FTIR is that a major class of chemical and biochemical compounds can be simultaneously analyzed and examined using the method of FTIR. With the advancement in recent technology, FTIR spectroscopy in association with ATR plays a key role for fast and accurate analysis of liquid samples (Shirai et al. 2014). ATR-FTIR is primarily used as a technique in the field of pharmacological research, which helps in investigating the structural interactions and orientations of proteins. Poly histidine tags are used to determine the structure of water-soluble proteins. Internal reflection, which is considered as one of the major phenomena, associated with the technique of ATR-FTIR helps in understanding the difference in the spectra produced in contrast to the presence and absence of the pharmaceutical ligands. Difference spectra thus produced helps in studying the various kinds of conformational changes associated regarding protein association, binding and interactions. The method also helps to monitor the amount of drugs that is penetrated into the membrane. Thus, this helps in understanding the amount of drugs being incorporated into the biological membrane and helps in determining the coefficient of diffusion for the samples. The method is also used for determination of the sugar content in honey samples (Naito et al. 2014). Thus, in brief, it can be stated that ATR-FTIR helps in spectral analysis of the respective solids, liquids and gases by using the principle of infrared spectroscopy (Amma et al. 2015).

By the present days, the principle of ATR –FTIR has also been applied to the determination of the microfluidic flows of the liquefied solution produced inside an engineering microreactor. The engineering micro reactors already have the build in apertures for the ATR crystal that helps the micro channel to pass easily over the surface of the crystal for further characterization and thus helps in the passive characterization of the samples. The ability of the technique to passively characterize the samples has resulted from the use of ATR-FTIR in the field of forensic studies as it does not require any sample preparation (Arsov, 2015).

Hence, in brief, it can be stated that the FTIR-ATR spectroscopy is one of the modern methods of spectroscopy in which infrared light is introduced into the prism at an angle that exceeds the value of critical angle for total internal reflection (Milosevic, 2012). The reflection, in turn, produces evanescent waves at the respective reflecting surface (the surface that remains primarily transparent to infrared radiation such as thallium bromide) over which the sample is supported. The change or the distortion produced in the waves by the resulting sample helps in measuring the spectrum thus produced which is finally subjected to Fourier transform.

Multivariate Data Analysis is a statistical method of analyzing data arising from multiple variables. This is used for such cases where the different situations involve multiple variables (Hsieh et al. 2013). Huge amount of data is collected in every field of bioscience. The significant part is that despite of gathering rich data, there is an absence of a definite and accurate method of obtaining the clear picture of what is happening (Tanna et al. 2013). Multivariate Analysis gives the opportunity to process the information in a more particular manner when the data available is in rows and columns in the database. This multivariate analysis is generally used to perform “trade studies” while considering the effects of the all variables (Lawson et al. 2014).

This multivariate data analysis may include Analysis of Alternatives (AoA), capability based design, inverse design etc (Ogwu et al. 2015). It is generally observed that Multivariate analysis is considered as complicated system as it includes physics based analysis while calculating the effects of the variables (Martino et al. 2010). However, the essence of multivariate analysis is to reveal the inherent structure and meaning revealed within variables through the interpretation of different statistical methods as well as application. Multivariate data analysis keeps data analysis comprising different variables measured from various samples (Khan et al. 2011).

Chemometrics is the calculations on the measurements of “chemical data”. This chemical data may include calculation of pH or Fourier transforms interpolation (Akiny, 2013). Chemometrix is considered as the science to extract information from chemical systems. This interdisciplinary method may be used in multivariate statistical analysis, computer science and applied mathematics to solve the problems of biology, chemical engineering, medicine, chemistry and biochemistry (Memis et al. 2010). This Chemometrics calculation was first introduced by a group of researchers at the end of 1960s in the area of physical and analytical organic chemistry (Ong et al. 2010). It is observed that chemometrix is used to resolve both predictive and descriptive problems in the field of biology and chemistry. In both cases, predictive and descriptive problems have a huge dataset, which include highly complex, thousands of variables. This technique is mainly used in biology and analytical chemistry. The improvement of chemometrics also leads to the improvement of analytical methodology and instrumentation (Rodriguez et al. 2011). The Chemometrics are completely based on applications; therefore, the standard chemometric methods are vastly used in the industries (Kalantri et al. 2010).

In the supervised method, the samples are generally selected from more than one groups for the measurement, which resulted to I×K matrix (Pelletier et al. 2012). In addition, other classification methods are extensively used in the supervised technique. In unsupervised cases, the samples are measured and the I×K matrix is prepared without the presence of classes.

Principal Component Analysis (PCA) is well-established statistical procedure that is widely used for the use of orthogonal transformation for converting the use of observations obtained of the correlated variable into a group of values of uncorrelated variables that is known as principal components (CAO et al. 2010). In PCA technique, the numbers of “original variables” are greater than the number of “principle components”. It is found that the PCA has largest possible variables. The principal components of PCA are orthogonal as orthogonal components are “eignvectors” of the “covariance matrix” (Martino et al. 2010).

In 1901, Karl Pearson first invented Principal Component Analysis (PCA), which was analogous of the Principal Axis Theorem. However, Harold Hotelling further developed this method in 1930s (Wang et al. 2011). The essence of this theory is to convert huge number of correlated variables into a smaller number of variants, which is further known as “principle components”. The background of Principal Component Analysis is deeply related to the multivariate data analysis and other extensive applications (Nanubolu and Burley, 2012). It is observed that PCA can be proved as the most significant results from applied algebra. The PCA is commonly used to handle huge set of data. The common application of Principal Component Analysis includes denoising signals, data compression and blind source separation (Connatser et al. 2010).

This PCA (Principal Component Analysis) is a commom multivariate method. This method may be applied to a large quantity of data to provide way better overviews (Panicker et al. 2010). The PCA application is aimed to demonstrate the large variation of data with least number of variables. On the other hand, the variables in a data are not independent. They depend on each other to form a perfect coordination and display a structure (Fransson et al. 2010). While applying the PCA, the first Pc represents most of the variance, which includes most of the variance in the data set. On the other hand, second Pc helps to develop approximation and third Pc. The main concept of PCA is to decrease different dimensionality of data set, which comprise vast number of variables. A fresh set of variables are introduced in the data set, which are systematic and uncorrelated, so that the first components can maintain most of the variations among the original variables (Kolesov, Mikhailenko and Boldyreva, 2011).

The mathematical expression of PCA is

X = TPT + E

X= The original data matrix, is described by K variable and consists of N objects.

T= The score vector matrix

PT = The loading matrix

E= The error in the matrix, which contains the residues

The mathematical expression of PCA

variations among the original variables

The literature review showed that Chemometricians prefer PCA technique more to collect information from a set of data and for data compression (Zimmermann and Baranović, 2011). PCA is dependent on correlation matrix of “process variables” or eigenvector decomposition of the covariance. However, it is found that the whole variance in the total data cannot be observed by the PCA method, which is the limitation of PCA method (Panicker et al. 2010).  

Hierarchical Cluster Analysis or Hierarchical clustering is considered as a technique of multivariate, which helps in the organization of components based on the characteristics. This analysis method helps to classify the components based on their similar characteristics (Trevisan et al. 2012). Therefore, Hierarchical Cluster Analysis helps in defining considerable heterogeneity among the characteristically different groups as well as shows significant homogeneity among the characteristically similar groups. In statistical analysis and data mining Hierarchical Cluster Analysis is used to develop a hierarchy of “clusters”. There are two types of strategies to develop hierarchical clustering, such as agglomerative and divisive. The agglomerative is considered as bottom up approach (Panicker et al. 2010). On the other hand, divisive is considered as top down approach. In divisive, total observation starts from one cluster, when one shifts down the hierarchy the splits took place. Unlike divisive, in agglomerative method, individual observation starts from its own cluster and simultaneously they merged with each other as one moves up to the hierarchy (Bassan et al. 2012). From previous researches it is found that the splits and merges while using Hierarchical Cluster Analysis is performed in a greedy manner. It is also observed that Hierarchical Cluster Analysis presents the result in the form of a dendrogram (Narhi et al. 2013).

It can be said that the agglomerative clustering method is used to produce a far better similarity association data from one sample to the cluster of total samples. The relation can be demonstrated in a better way by using graphical presentation of various groups and their closeness, which indicate a high reduction in the “dimensionality” of an authentic data (Campbell, 2012). It is observed that Hierarchical Cluster Analysis data are generally described in a tree like structures by the researchers while performing the analysis. Most of the hierarchical methods are stumble in the agglomerative clustering analysis. It is observed that most of the clusters in this group (agglomerative clustering analysis) are developed sequentially from various objects. Primirily, it is found that one single object is responsible to begin this procedure (Narhi, 2013). After that based on the similar characteristics, the clusters merged with each other as well as splits from each other (divisive clustering method). In this, phenomena, at first two clusters, which have most similarities between them are combined and produce a new cluster at the beginning of the hierarchy. After that, another pair of clusters joined with the previously formed cluster and connected the newly formed cluster to the advanced level of hierarchy. Therefore, this allows a hierarchy of clusters to start from the “bottom up” to show a linkage point.

While selecting the clusters, which are going to be split (divisive clustering method) and which are going to be merged (agglomerative clustering method) a measure of dissimilarity between the observed data is essential (Bassan et al. 2012). In most of the Hierarchical Cluster Analysis, the measurement of dissimilarity is accomplished by using appropriate metric as well as using a linkage criterion that helps to specify the dissimilarity of the observed data. On the other hand, it is observed that the choice of metric can directly influence the shape or the appearance of the cluster. This is because, some components of the data are too close, while some are too far from the closed data. Some of the commonly used metrics in the Hierarchical Cluster Analysis are maximum distance, Manhattan distance, Mahalanobis distance and Euclidean distance (Trevisan et al. 2012).


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