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According to several theories, blend uniformity of a binary mixture is achieved when all the components in the mixture have an equal probability to obtain from any part of the blender. If it doesn't happen then it is called as non- uniform mixing. In most of the cases the mixing is because of gravity and not because of physical interaction which leads to segregation of the components. Many other factors lead to this type of non-uniformity like surface charges, cohesiveness, spreading coefficients, etc. Generally these parameters are not routinely used for predicting the blending end point as they are not relevant to the major factors affecting blending like particle size and flow properties. This mainly depends on variables like physical properties of sample, the degree of mixing carried out and conditions at which the process takes place. The extensive work of Derjaguin [1] explains the force acting between two spheres on basis of energy per unit area.

In this study, we mainly investigate the time (end point) at which uniform blend is obtained considering some factors affecting the blend homogeneity and by applying the chemometric methods.

Near Infra Red Spectroscopy (NIRS) is a potential device for evaluating the homogeneity of powder blends. In this particular context of laboratory analysis, I am taking a mixture of two components with an objective to determine end point of blending process. I evaluated 15 batches of the same samples in same proportions to access the uniformity of the homogenous mixing of lactose and kaolin.

Blending process is generally accessed by many conventional methods; the mostly applied method among them is using a volumetric sample thief where the powder is withdrawn by inserting the probe. Application of this method is not much effective because of its limitations2 like disturbing of sample mixture while collecting samples segregation of powders in the cavities, etc. Other methods are all sample destructive and so non-invasively methods are preferred mostly. Also time taken for analysing the end point is also more [2] . At this context there is a demand for fast and non-destructive methods to determine blend homogeneity like NIR Spectrometry. Different approaches for quantitative and qualitative determination using NIRS are off-line, at-line, on-line, and in-line procedures.

Chemometrics are adopted for the qualitative determinations using methods like Moving Block Standard Deviation (MBSD), Principal Component Analysis, Cluster Analysis, etc. Even though some authors claim that these techniques underestimate blending end point, all that claims are subsided by applying these chemometric methods [3] .


importance of blending process in pharmaceutical formulations

advantages of monitoring blending process

using of nir for monitoring blending process

factors to be considered in the blending (flow properties, exothermic properties, shearing, etc)


previous research

previous methods applied for detection

principle involved in blending process & nir ( refluctance , emission , etc )



Binary mixture ( Lactose & Kaolin )---(Particle size, shape, flow properties n all )

The binary mixture of a-lactose monohydrate and kaolin is used as a sample for the blending end point monitoring.

Several properties and favouring characters of lactose made it compatible as filler in many pharmaceutical formulations like tablets and capsules. The properties like low hygroscopicity, and less compatibility with Pharmaceutical Active Ingredients (PAI) and other excipients and its physical and chemical stability are the most favourable properties of lactose. Here crystalline a-lactose monohydrate is used for blending as it is the majorly used form of lactose.

Even though kaolin is not much pharmaceutical preparations because of its less compatible nature it suits for this experiment to monitor blending point. Kaolin is used as a tablet excipient, serves as an emollient (topically) and controls diarrhoea when ingested [4] .

Blend uniformity is achieved easily with good flow properties like regular shape (particularly spherical) and narrow particle size distributions. The flow properties of Lactose are good compared with kaolin as the later one shows some gritty nature but these are negligible. Powder density also plays a good role in this process as the density increases the rate of sedimentation of the particles increases and this result in a non-uniform mixture i.e.., segregation of the denser particles fast in the pharmaceutical formulations. The density of the lactose is good and in acceptable range for the pharmaceutical preparations.



Lactose Fine Crystals

0.73 (Bulk )

0.86 (Tapped )


2.6 (Relative with water =1)

FIGURE : [5] 




Coarse crystals


Fine crystals








Many other factors are also considered in blending process to avoid parallax errors. Chemically lactose is very stable; the low hygroscopicity [8] of lactose supports its inertness and prevents from Maillard reaction [9] . It has no tendency to attract moisture and the water of crystallisation is bound tightly in the crystal lattice and is lost only form 100°C to 140°C. This nature of lactose helps in preventing unwanted reactions and contamination of the active ingredients. Both kaolin and lactose are incompatible and cannot bind with each other in the middle of the process and it results in a uniform mixture.

α-Lactose monohydrate

(β-D-Gal-(1→4)-α-D-Glc, Milk sugar, 4-O-β-D-Galactopyranosyl-α-D-glucose)


(Aluminum silicate hydroxide, Bolus, Hydrated aluminum silicate)





C12H22O11 · H2O



0.01% Glucose

≤0.0025% heavy metals (as Pb)

≤1% soluble in acid (as SO4)

FIGURE [10] 

Kaolin has surface charges but they do not affect the blending in regular basis. If the '-' ve or '+'ve charge of kaolin raise they develop the electrostatic forces which results in colloidal instability and interactions with low molecular polyions [11] .

In order to achieve ideal blending the components should not interact chemically and lactose and kaolin both satisfy that feature.



The instrument used is Buchi NIRFlex N-500 FT-NIR spectrometer (Buchi UK Ltd, Oldham, UK). It works on the principle of over toning and combination vibrations of molecules. When energy transitions take place in atoms they don't follow electric dipole method and emit a spectral line. The spectra attained by this overtone and combination vibrations are very complex and are with broad bands.

NIR spectrometer was used for the first time in 1950s, but it was used in combinations with other devices like UV, Vis, MIR spectrometers, etc. and was later used as a single unit. The beginning of light-fibre optics and monochromatic-detector made NIR a powerful tool for scientific research. NIRS is applied in the analysis of foodstuffs, pharmaceuticals, medical tool and a branch of astronomical spectroscopy.


FIGURE :http://www.buchi.com/NIRFlex-N-500.465.0.html


It is equipped with a light source and a thermo-electrically cooled InGaAs detector (with Indium gallium arsenide semiconductor), dispersive element and polarization interferometer and a wide spectral range of 800 - 2500 nm (12,500 - 4000 cm -1). In order to reduce the mechanical distortions and to manage difference of spatial movements and the optical path shifts a rugged crystal polarization interferometer is used which is of superior performance. The polarization is carried out through the crystals with very high refractive index. The principle involved is, the incident light is split to two when it strikes the crystal, and they are polarized in right angles and traverse the crystal at different velocities. Then they shift to different phases because of the moving prisms and hence polarization of combined beam is changed.

Figure: Polarization interferometer [12] 

Figure: Principle of the polarization interferometer9

It also helps in obtaining with an optimum resolution of 8cm-1. For solid samples because of broad absorption bands, higher resolutions are not fruitful for NIR applications. Also, higher resolution gives unnecessary bulk data sets and leads to a poor signal-to-noise ratio, where it is 10,000: 1 by using rugged crystal interferometer. This NIRS collects an interferogram and transforms it to a new single-beam frequency-domain spectrum. Then it subtracts the reference spectrum that is acquired in the starting from this transformed spectrum to giving a new reflectance spectrum.

Spectral range

800-2500 nm

12500-4000 cm-1


8 cm-1 (with boxcar apodization)

Type of interferometer

Polarisation interferometer with TeO2 wedges

Wavenumber accuracy

± 0.2 cm-1

Signal-to-noise ratio

10,000 : 1

Number of scans


Analog digital converter

24 bit

Ambient temperature

5-35 °C

Type of lamp/lifetime lamp (MTBF)

Tungsten halogen lamp / 12 000 h (2x 6000 h)

Type of laser

12 VDC HeNe, wavelength at 632.992 nm

Photometric dyn. range


FIGURE:SPECIFICATIONS OF ------ Prospekt_NIRFlex_en_0810 [13] 

The spectrum is measured by single sample solid reflectance angle.

The data obtained is then used for extracting the chemical information using the multivariate calibration techniques like PCA and Cluster Analysis. For all near infrared techniques develop the calibration samples and are reduced and calibrated using multivariate techniques.

NIRS technique surmounts other established techniques by its unique properties



It can handle several factors concurrently and very efficient.

It generates reports very rapid.

This method is very easy to handle without any complexity.

Operating expenses are very less and satisfactory.

Reactions are very safe and leave no chemical wastes.


NIRFlex N-500 covers a wide range of spectrum from 1000 to 2500 nm, where as other techniques can serve at specific wavelengths with small range.

The resolution provided is optimum to record the samples.

Operation is very quick and all frequencies are measured at same time and signals strike the detector simultaneously.

Signal- to-noise ratio is very efficient and reduces the signals caused by external atmosphere.

Near infrared rays can travel very deep into the sample than that of Mid Infrared rays.

NIRS is not a sensitive technique and can handle bulk materials with minute samples.

NIRS can be used to measure different varieties of samples.


NIRWare and NIRCal are the two software tools that manage wide range of applications and meet operational and calibration demands of NIR Spectrometer.

NIRWare software suite mainly helps in managing spectral information of samples, tailoring user interface and designing applications.

It comprises tools for import and export of spectral information, security tools and backup tools to recover the data whenever needed, apart from calibrations which are carried out by NIRCal 5 Chemometric Software. Its main applications are NIRWare Management Console and NIRWare Operator.

NIRWare Management Console

NIRWare Management Console holds all snap-in modules and is configured for several user collections.it includes

Application Designer: This guides about handling procedure for analysis, defining instrument, application, calibrations and estimating properties.

Sample Management: This helps in checking the previous data, and monitors in managing samples and properties.

Administrative Tools: it includes

Audit Trail / System Logger: A part of FDA's 21 CFR Part 11 regulations.

Database Maintenance: Backup files.

Import / Export: Easy data exchange.

Security Designer: It helps in protecting files from unauthorized access. Account Policy helps in defining user names and passwords

Library Designer: It is useful in applying chemometrics and also spectral comparison with other files in library.

NIRWare Operator

The operator helps in handling measurements and results are displayed as reports. It is user friendly and guides the users in tailoring interface and in applications.

NIRCal 5 Chemometric Software


TURBULA T 2 F mixer (WAB, Muttenz 1, switzerland)

FIGURE: [14] 

TURBULA T 2 F was used in various fields like Cosmetic, Pharmaceutical and Chemical industries while handling sample load up to 10 Kg. Turbula mixer is very efficient as it can mix particles with different size and densities, can handle both dry and wet samples and can generates a homogenous mixture within a short period. It consists of a basket wind with elastic belt to hold container and gears.

The efficient homogeneity is reached by the 3-Dimensional mixing following the Paul Schatz theory of geometry1. The basket with container runs in rhythmic movements with rotation, translation and inversion motions. His first practical application of inversion motion was applied in Turbula. [15] 

The other instrument used for mixing samples industrially and in laboratories is INVERSINA (Bioengineering AG, CH - 8636 Wald) [16] . But because of many additional features of the Turbula it is used in determining the blending homogeneity.

It is highly versatile and can handle containers up to 55 litres depending on the requirement.

3-Dimensional motions as per Schatz.

Belt driven and geared basket which is adjustable.

Rotational speed is 23 per minute. And can be adjustable up to 101 rpm.

Frequency converter to adjust frequency.

Process is sterile avoiding exposure of sample contents to external environment.



Chemometric methods involve computer applications and mathematical tools to simplify the complex data and generate the results. They play a major role in interpreting the chemical information, in quantitative and qualitative analysis using NIR Spectrometry.

In this method the raw data is extracted from analytical instruments and uses statistical methods for developing the results. They deal with the correlated variables as well as un correlated variables and then process the analytical data by reduction of variables. Among these methods, multivariate statistical techniques are the mostly adopted methods for NIR spectrometric analysis. Here it is applied for the quantitative prediction of samples and determine the end point when the blend uniformity of the sample mixture is achieved.

Chemometric techniques are majorly used methods for spectral data interpretation by many analytical instruments like NIRS, NMR, IR and UV both industrially and academically.


The secondary derivative methods are used to sort the spectra and remove unwanted data points. At same wavelength it has a minimum band (negative) and zero order has a maximum band. The main band has two satellite bands on its either sides. The number of bands can be known by adding one to its corresponding order.


It is a very competent and forms basis of the derivatization algorithm in many analytical instruments. N number of data points is collected and fixed with a polynomial to analyse derivative.


a0...al are coefficients at each wavelength and are multiplied by order.

This method gives a smooth data. When data points are more than the order, all data points are not covered by polynomial and generate a smooth estimate to the original data points. By using this property the deprivation of signal-to-noise ratio can be countered.

Features Of Savitzky And Golay Secondary Derivative Method:


The increase in complexity by addition of bands, in the higher derivative spectra is useful for characterising and identifying the samples.

Resolution enhancement

The resolution is increased by abolishing the background effects.

Background elimination

Unwanted baseline shifts are totally eliminated, that are caused by mistake in sample handling.


This method helps in suppressing broad bands to narrow bands and increases amplitude with increase in derivative order.

Reduction in scattering

This also helps in reducing the scattering errors.

Matrix suppression

The absorbing background helps in spotting and categorizing tiny components.

Signal-to-noise ratio

The decrease in signal-to-noise ratio at higher orders of derivatives is countered by this method.


Because of the random scattering of the radiations, larger variances are seen in the Near infrared spectrum. Among the several variance reduction methods followed, Standard Normal Variate (SNV) transformation was important.

These spectra are very similar to the original spectra in shape and are very easy to interpret. These are applied in several fields, as they can measure both transmittance and reflectance data [12,13,15]. This method carries out transmission of individual samples rather than sets of data. The major limitation of SNV method is formation of artefact, which gives worse results. The other limitation is, it results in "closure" of the data, the point where two data changes in opposite directions to compensate each other.


Principal component analysis (PCA) is a multivariate statistical technique which is valid only for correlated variables and data it functions by reducing the whole data. It finds the principal components that are linear combinations of the original variables describing the fluorescence intensities at the given wavelengths and then creates new variables. The new variables created by using the coefficients are not correlated and then two principal components with largest variation are selected. These principal components obtained from the measure of the joint variance of two variables are called as Covariance matrix. PCA is also applied in the fields of multiple regressions.

Principal Component Analysis is a mathematical method that reduces data from NIRS in order to reveal differences between samples and classifies them. This is a simple and most common technique, applied to determine the number of components contributing an event [17] . This method involves reduction of large data extracted from a spectrum to a small data [18] and arranged linearly, that represents the whole variables of the corresponding spectrum [19] .

In this method all the arbitrary values are linearly arranged and orthogonally related to each other with a decreasing order of magnitude of variance. The derived coordinates are arranged in decreasing order of variance, making the principal component with maximum variance to stand first.

It is followed by the next variance component level and is arranged orthogonally to the first component and so on [20] . The initial method followed for preparing components that are corresponding to target spectra, are same for both Fourier transformation and Principal component transformation. The number of components obtained in spectroscopy is usually equal to the number of spectra obtained [21] .

Principal components are always real and these are normalised for further computation. Each principal component is normalized and using this normalised component and target spectrum a cross product is done which can be termed as principal component score. New array of numbers that are subsequent to target spectrum are obtained from this score. This scores corresponding to target spectrum result in forming a principal component transform.

Functions of the principal components can be categorized into two classes. They belong to a class that contains empirically defined functions and arbitrary functions that can't be described by analytic mathematical expressions. Other such non-analytic function is Gram- Schmidt orthogonalization or basis function, which are because of the spectra of the pure materials. In NIR spectroscopy this function can also be called as curve fitting.

Principal components are orthogonal to each other and result in maximum variance in data, and this feature keeps it different from other functions. They are determined by computing the set of data that represents target spectrum. Then the principal component spectra are subtracted from the original target spectrum which generates a new difference spectrum. Using these difference spectra the Residual Sum of Squares and Total Sum of Squares are computed.



j = all the wavelengths in spectrum

i = all the spectra in the dataset.

By computing all the residual values RSS is obtained, from which Total Sum of Squares (TSS) is calculated.



Xij = absorbance of the ith spectrum at the jth wavelength

¯Xj = mean absorbance at jth wavelength


Cluster analysis is a multivariate statistical method which involves searching for groups, dividing the groups into objects and assembling all the analogous objects in one new group forming a cluster. In the whole variable space, it searches and identifies closely related objects and categorizes them into different classes, but no assumptions are carried about the distribution of those variables. This method also decides whether to measure all the data on the same scale or on a different scale. Measuring on a same scale is called as standardization and it helps in preventing domination among different variables. Cluster analysis method involves forming new clusters with each object of equal size and comparison of distances between those clusters.

The steps involve formation of a new cluster by joining the two closest objects together. Again from these clusters the close clusters will join to form another new cluster comparing the distances between the two clusters. This step repeats and if there is more than one member in distance between two clusters then the distance between the neighbours is considered which is called as single linkage method.



PQS is a statistical data reduction method, which helps in characterizing quality of a material in the form of a polar co-ordinate system.

The degree of divergence of the examined sample from the standard is determined by knowing the polar distances between two quality points of their NIR spectra. In NIR spectra sequence, optimisation is carried out by Polar Qualification System which helps in solving all kinds of multivariate tasks. It is a simple and very effective method.

The PQS was first introduced at 3rd International Conference on Near Infrared Spectroscopy (KAFFKA & GYARMATI, 1991) [22] in Brussels, as a new data reduction statistical tool. In this method a quality point can be explained 2-dimensionally to explain the quality differences. Basic principles of PQS method were constructed using the NIR spectra of milk powder samples. And spectra from distillation of volatile oils were used to explain the sequence optimisation.

In polar coordinate system the spectral value is represented as radius and wavelength as angle. The effects of change in absorption peak, noise and effect of base line on quality point were determined using three interpretations for "centre". These are the three possibilities for the shift in the quality points of a given spectrum.

The centre of spectrum is described as different centre of gravities by different methods

point method

of unit masses in points

line method

of wire shaped in spectrum

surface method

of surface of spectrum

The aim of PQS is to determine the quality points by using the NIR spectra in polar co-ordinate system. The Polar Qualification System works by using this point, line and surface methods and helps in determining the polar points and distances of the spectrum.

In this project PQS system is applied in determining the blending end point by spotting the quality points of the sample spectra.

Unscrambler software


Sample preparation :

15 batches of binary mixtures are prepared: a-Lactose monohydrate ( ) with Kaolin( ).

These are taken in 2:5 ratio respectively.

42.85 gm / 0.042 kg of a-Lactose monohydrate is weighed using a digital weighing balance. This powder sample is transferred into a clean and dry container and closed tightly.

107.14 gm / 0.107 kg of Kaolin is weighed and transferred carefully into another clean, dry container without any agitation.

Both the chemicals (150 gm) are transferred carefully (without mixing) into a dry plastic cylindrical container of 200 ml volume and the lid is closed tightly.

All this process is carried out carefully without any agitation or tilting to prevent the false prediction of the blending end point.

Blending Operation :

Blending operation is carried out using a Turbula ( ) blender

The required frequency and speed are adjusted to obtain the optimum reproducible results.

The geared basket is adjusted using a driver so as to give enough space for the plastic container.

Then chemical filled sample container is then fastened inside the basket, in order to avoid any misplacement of the container in the middle of the process.

The blender is operated and the sample is collected into labelled sample vials using spatula for every 40 seconds up to 10 minutes.

The sample is collected without creating any disturbance to the rest of the sample.

Preparing NIRS Instrument :

The NIRWare software and NIRCal software ( _)are installed in the NIRflex-500 instrument to perform the analysis of samples.

The application required for blending end point monitoring is Single Solid Reflectance. This application is created using a reference for identity check of incoming substances.

System Suitability test is performed by clicking on "advanced", then "perform system suitability test".

External reference is measured using aluminium-plated reflector block, to subtract it from the sample readings to minimize the reflectance caused by external environment.

Acquisition of NIR spectra : --- running sample, saving spectra and in what format.

The sample vials are cleaned at the bottom, and are placed on the cell positions and run.

Enter the sample id and after that click on "result and spectra".

These spectra are saved as separate documents in XPS Format.

Data analysis --- importing data NIR cal and unscramble and excel sheet methods

data analysis is carried out by installing NIRCal software.

Get in to "File", then click on "databases", then "search" and click on " import spectra " and then select the files corresponding to required spectra.

Then go to "table" , then "spectra", then "original", and right click and export table.

Then save all these files as separate documents in the XLS Format.

Each data matrix obtained will contain 1500 rows and one column.

Further analysis is carried out by using this data with unscrambler and chemometrics.

Using NIR ( solid reflectance angle and all )