Chemical Imaging: An overview and its Applications in Pharmaceutical Processes and Quality Control


Atmaram Tambe*, Vinayak Dalvi, Daksha Parmar, Vineeta Khanvilkar and

Dr. Vilasrao Kadam

Department of Quality Assurance, Bharati Vidyapeeth’s College of Pharmacy, C.B.D. Belapur,

Navi Mumbai-400614, Maharashtra, India.

*Corresponding Author E-mail:




Chemical imaging technique is very useful tool for pharmaceutical quality control and optimization of pharmaceutical processes. It is a fusion of vibrational spectroscopy and imaging techniques. Real time process control is major advantage of this technique. Studying spatial distribution and localizing different components in multicomponent samples is a gift of chemical imaging. Advanced image processing and multivariate statistical methods are used to extract the useful information from the chemical images. Chemical imaging is applied for many quality control tests which are discussed in this article. Defective articles can be identified and removed from the bulk with the help of chemical imaging and thus minimizing the risk of defective sample getting into the finished product line. Thus it can be promising tool with faster image acquisition in a lower cost with better image processing tools in near future.


KEYWORDS: Chemical Imaging, Real time process control, spatial distribution, multicomponent samples and multivariate statistical methods.




Real time process control for optimization of the pharmaceutical processes and quality control is the need of present era. All quality control techniques used today are not sufficient for this and lack in one or more situations. Pharmaceutical industries are in search of all such techniques which will give real time process control and optimization of the same. Chemical imaging (CI) is one of the techniques which present era is looking for. CI technique is an additional dimension of vibrational spectroscopy which work in conjunction with conventional imaging tools. Vibrational spectroscopy encompasses near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy. They allow both quantitative as well as qualitative analysis. CI is an exciting new analytical technique that answers questions like what chemical species are present in a sample, how much of each is present and most important it’s distribution in a sample. It is possible through fusion of two analytical techniques microscopy and spectroscopy. CI enables to obtain spatial and spectral information characterizing samples with unprecedented ease, speed and spatial and spectral resolution.


CI is advance in Infrared focal plane arrays which are cameras composed of many thousands of individual detector elements coupled with infrared optics. Recently the introduction of Process Analytical Technology (PAT)1 and Quality by design sparked fresh interest in pharmaceutical industry for CI techniques.


Process monitoring and control are necessary at all stages of pharmaceutical processing from raw material to packaged product characterization. Traditional quality control methods such as High Performance Liquid Chromatography (HPLC) and Mass Spectroscopy (MS) takes longer time, destructive, expensive, require lengthy sample preparation and give no information about the distribution of excipients and active pharmaceutical ingredients within a sample. Because of destructive and time consuming part of these methods, only limited samples of drugs may be tested from given production batches.


Principles of CI:

CI data is explained in three dimensional cubes in which two axes describe vertical and horizontal spatial dimensions and 3rd is spectral wavelength dimension. One image per wavelength interval stacked sequentially over one another. The intensity of single pixel plotted as a function of wavelength dimension is a standard NIR spectrum.

Chemical Images are made up of hundreds of continuous wavebands for each spatial position of a sample studied. As a result, each pixel in a CI contains the spectrum of that specific position. The resulting spectrum is a fingerprint, which can be used to characterize the distribution and composition of that particular pixel. Chemical Images, termed as hypercubes, are 3 dimensional blocks of data, consisting of two spatial and one wavelength dimension, as shown in Fig.1. The hypercube allows for the visualization of biochemical components of a sample, separated into specific areas of the image, since areas of a sample with similar spectral properties are similar by chemical composition. There are two conventional methods to build a hypercube. First method involves acquisition of simultaneous spectral measurements from a series of adjacent spatial positions - the object is moved underneath an imaging spectrograph known as pushbroom acquisition. This method has been applied for hypercube acquisition in Raman mapping investigations. Second method involves keeping view of image field fixed, and obtaining images one wavelength after another known as a staring imager configuration. This method has been popular for construction of NIR-CI.


CI instrumentation:

The two main types of CI system are staring imager and pushbroom configurations. Both of these configurations are applicable to Raman and NIR imaging; although, as mentioned earlier, the staring imager configuration is commonly applied to NIR-CI investigations, the pushbroom system is commonly applied to Raman mapping of samples. While the components of these systems vary they can be schematically represented as in Fig.2. Illumination source, sampling stage, imaging optic, spectral encoder used for wavelength selection, detector and acquisition system (usually a frame grabber board interfaced with a computer).


Illumination source

A single polychromatic thermal source is generally used for MIR and NIR spectroscopy. An inert solid electrically heated to 1500–2200K irradiates uniformly in the IR spectral range. Silicon carbide is used as illumination source in MIR and a tungsten filament in NIR region. In Raman spectroscopy, lasers emit the excitation beam. The choice of wavelength will depend on the solid-state properties of the sample and its fluorescence. Longer excitation wavelengths prevent fluorescence but decrease intensity of Raman band roughly proportional to the fourth power of the wavelength2.


Fig.1 Schematic representation of CI hypercube showing the relationship between spectral (k) and spatial (X, Y) dimensions.

Sampling stage


Sampling stage is meant for holding the sample in position for illumination with the source spectrum of the light. Two types of stages are used translational and non-translational stages. Translational stage is mounted on a motor for movement so that various spectra are taken by movement of the stage. Non-translational stage is fixed or immovable type of stage.


Various sampling techniques are used in which different responses are measured as described below:


Responses measured in MIR and NIR:

1.       Transmission

In transmission measurement, the source illuminates the sample and the detector is placed behind the sample (Fig.3) to acquire the fraction of light transmitted through the sample. Transmission analysis requires the sample to be partly transparent. Liquid can thus be prepared as a dilute solution in a cell. Potassium bromide disk is used for dispersion of solid samples. Moreover, the powder particle size must be smaller than the radiation wavelength to prevent the Christiansen scattering effect which appears as band distortion in the spectra3. Transmission has been extensively used to analyze thin samples such as films4 or tissues. It is not possible with thick samples such as tablets.


Fig. 2. Typical components of a CI system.


Fig. 3. Transmission measurement: the detector registers the signal passing through the sample.


2.       Reflection

The detector is placed on the same side of the sample as the source to record the signal reflected by the sample. The two types of reflection measurement commonly used in CI of pharmaceutical samples are diffuse reflection (DR) and Attenuated Total Reflectance (ATR).


3.       Diffuse reflection (DR)

Incoming radiation interacts with the sample components and it is scattered by interaction with the particles. Sample reflects fraction of light and it is recorded by the detector (Fig.4). In the MIR range, DR requires the sample to be diluted between 10 and 100 times to avoid saturation and band distortion5. For this reason MIR-DR is rarely used for imaging. On the other hand, because samples need no dilution at all in the NIR range (the bands are weak), NIR-DR is widely used for the image analysis of thick nontransparent samples in various noninvasive applications, notably in the food industry  and pharmaceutics.


Fig. 4. Diffuse reflectance measurement and penetration depth.


4.       Attenuated total reflection (ATR)

When a beam of light passes from a medium of high refractive index n2 to one of lower refractive index n1, with an angle θ greater than a critical angle θ c[ θ c = sin -1(n1/n2)], total internal reflection of the light occurs. The sample is placed on the optical surface of an ATR crystal objective (Fig.5), usually made of zinc selenide (ZnSe), diamond, silicon (Si) or germanium (Ge) with refractive indices of approximately 2.4, 3.4, and 4.0 respectively. As the refractive index of organic material is around 1.5, the light is totally reflected at the crystal–sample surface interface. However, the electric field of the radiation, or evanescent wave, penetrates a short distance (a few micrometers) and interacts with the sample.


The penetration depth is dependent on the wavelength. Thus only a minute sample volume is actually measured. This avoids scattering effects6 and allows analysis of solid samples, such as powder or tablets, without need for dilution. Good optical contact must be ensured at the crystal sample interface.


Fig. 5. Attenuated Total Reflection (ATR). The sample is placed on the surface of a crystal. A pressure head ensures good optical contact.



Photon detectors are the most widely used in MIR and NIR spectroscopy to record the signal after wavelength separation. In NIR, lead sulfide (PbS), indium antimonide (InSb), and uncooled indium gallium arsenide (InGaAs) are commonly used, whereas in MIR mercury cadmium telluride (HgCdTe or MCT) is most used due to wide spectral sensitivity (from 2µm to 20µm, depending on the Hg/Cd concentration ratio).  Raman detectors are mostly 2D charge coupled detectors (CCDs)7. Advancement in the production of low cost array detectors have lead to faster data collection and cheaper CI systems; detection time depends on spatial and spectral resolution required, increases with increasing resolution.


Imaging optics

The microscope is fitted with optical elements for selecting spatial resolution. The choice of magnification levels depends on the study aim. Typically 6X, 15X, and 32X objectives are used on a MIR or NIR microscope8, and 50X and 100X objectives on a Raman microscope. Additionally, Raman microscopes are often mounted with a confocal aperture to tune the penetration depth of the beam and thereby analyze only few micrometers of the sample. Such systems can also focus the beam at different sample depths to determine subsurface composition9.


Spectral encoder for wavelength selection

Spectral encoders are used for selection of single wavelength light beam to be focused on sample surface. Fourier transformer, tunable filter, and diffraction grating spectrometers are the three main types of spectral encoder used in CI:


1.       Fourier transformer (FT)

FT spectrometers record information from several wavelengths simultaneously. Their many advantages are rapid (typically 1 s) acquisition across the whole spectral range; high spectral resolution, down to 2 cm−1; high energy available to the detector because no slits are used; and high wavelength repeatability. FT interferometer can be used for all three types of CI system namely MIR-CI, NIR-CI and Raman CI systems.


2.       Filters

Filters are useful for focusing on specific wavelengths. They also dispense with moving parts in the spectrometer10. Several filters can be mounted on a wheel to select several wavelengths. An alternative is a tunable filter11, which electronically controls spectral transmission by applying a voltage. The liquid crystal tunable filter (LCTF) has become the most popular technique for global imaging. Its advantage over the filter wheel is that it can record more than 100 images at different wavelengths. Filters are mainly used in NIR and Raman hyperspectral imaging.


3.       Diffraction grating

It has a large number of parallel lines or slits separated by a distance comparable to the wavelength of light. When a polychromatic ray of light hits the grating, it is dispersed in several directions and the angle of diffraction is dependent on the wavelength. With single point detectors only one spectral point can be acquired per position of the prism and detector. The full spectrum can be acquired by rotating either part stepwise. Line detectors enable several wavelengths to be acquired simultaneously12. Slits at the entrance and exit of the gratings are used to remove parasitic light. However, narrow slits can drastically reduce the amount of signal reaching the detector whereas large slits might decrease the spectral resolution of the spectrometer. High detector sensitivity and high source intensity in the NIR range make it suitable for NIR and also Raman spectroscopy.


Working and construction of chemical images (Hypercubes)

Chemical images are developed mainly by two techniques namely Pushbroom acquisition and Staring imager configuration.


Pushbroom acquisition

Pushbroom imaging systems require a translation stage for sample movement. Chemical Images are produced using an optical microscope coupled to a spectrometer in Some Raman mapping instruments. At single points of a sample spectra are obtained, then the sample is moved and another spectrum taken and this is called as step and acquire acquisition mode. This process is repeated until a collection of spectra for points spanning the entire sample is obtained. Raman line mapping experiments are also possible, where the spectrum of each pixel in a line of sample is simultaneously recorded by an array detector. The sample is usually illuminated by a laser source in the visible or NIR wavelength range in Raman mapping. A line of light reflected from the sample is entered into the imaging optic (usually a microscope lens) and it is separated into its component wavelengths by diffraction optics contained in the spectral encoder, a two-dimensional image (spatial dimension & wavelength dimension) is then formed on. The sample is moved beneath the objective lens on a motorized stage and point or line images acquired at adjacent points on the object are stored on a computer for further analysis. Charge Coupled Device (CCD) detectors (sensitive between 400 and 1000 nm) are typically used as detectors in Raman imaging, while Fourier Trans-form (FT) Raman imaging systems require more expensive longer-wavelength focal-plane array (FPA) detectors.


Staring imager configuration

The sample is usually illuminated with a diffuse source of NIR light (for NIR-CI) or dilated laser source (global Raman Imaging). Reflected light from the sample is captured via an imaging optic; a microscope objective lens is used for microscopic images. The light is passed through a filter, which is a spectral encoder and images are resolved into their spectral components.


Early staring configuration Chemical Images were obtained using a series of bandpass filters selective to certain wavelengths, which were usually mounted on a wheel for ease of switching.  Acousto-optic Tuneable Filters (AOTFs) and Liquid Crystal Tuneable Filters (LCTFs) are the two most widely used tuneable filters in CI. Commercially available NIR-CI systems use AOTFs, while LCTFs show greater promise for filtering of Raman images. After spectral encoding, an image of the sample at the selected wavelength is recorded on a detector such as CCD detector for Raman Imaging and an FPA for NIR systems. Images are stacked at subsequent wavelengths to form a hypercube and stored on a computer for further analysis.


Chemical Image analysis

A multidisciplinary approach is required to extract useful information from chemical images, consisting of advanced image processing and multivariate statistical methods. The challenge is to reduce the dimensionality of the data while retaining important spectral information with the power to classify important areas of a sample effectively. Typical steps involved in analyzing Chemical Images are-


1.       Reflectance calibration

This is carried out to account for the background spectral response of the instrument and the ‘dark’ camera response. For measurements of reflectance, a uniform high reflectance standard or white ceramic is used for the background by collecting a hypercube from it; the dark response is acquired by turning off the light source and recording the camera response.

The corrected reflectance value (R) is calculated as follows:

R = (sample-dark)/(background-dark).


2.        Pre-processing

Pre-processing is usually performed to remove non-chemical biases from the spectral information (e.g. scattering effects due to surface in-homogeneities, interference from external light sources, random noise etc.) and prepare the data for further processing. Mathematical treatments to compensate for scatter-induced baseline offsets include Savitzky-Golay derivative conversion, multiplicative scatter correction and standard normal variate correction.


Normalisation algorithms can be used to compensate for baseline shifts and intensity variations resulting from path length differences. Other operations at the pre-processing stage include thresholding and masking to remove redundant background information from the hypercube.

3.        Classification

Hypercube classification enables the identification of regions with similar spectral characteristics, which provides information on the physical and chemical properties of a sample, their distribution in bulk and concentration. Due to the large size of hypercubes complex multivariate analytical tools are usually employed for classification. Analysis of each spectrum a hypercube can be performed with chemometric tools designed for bi-linearly structured data sets. For application of conventional chemometric techniques in Chemical Image analysis, it is required to restructure the hypercube in a process known as spectral unfolding the three-dimensional hypercube is rearranged into a two-dimensional matrix, by means of appending the two spatial dimensions. After completion of classification, the image matrix is folded back into three-dimensional form from which Chemical Images are constructed. Unsupervised methods of classification such as principal component analysis (PCA), k-means clustering and fuzzy clustering, require no appropriate information about the dataset, and are useful as exploring tools to extract important information. Partial least squares (PLS), linear discriminant analysis (LDA), Fishers discriminant analysis (FDA) and artificial neural networks (ANN) are Supervised techniques which require prior knowledge about the data and selection of pre-defined training sets against which to classify the data.


4.        Image processing

Image processing is carried out to convert the contrast developed by the classification step into a picture depicting distribution of component. Greyscale or colour mapping with intensity scaling is commonly used to display compositional contrast between pixels in an image. Single wavelength images are easy to construct but can be misleading, because the depicted images may be biased or dominated by variations in thickness across sample surface; multi-wavelength images give a better representation of the sample composition. Image fusion, in which two or more images at different wavebands are combined to form a new image, is frequently implemented to provide even greater contrast between distinct chemical regions in a sample. Images are combined using algorithms based on straightforward mathematical operators, e.g. addition, subtraction, multiplication and division. Histograms are another tool, useful for depicting pixel distribution of components.


Applications of CI

Sr. no




Quality assessment

I.         NIR-CI has ability to detect suspect manufacturing issues e.g. blending, density pattern distribution.

II.       Direct qualitative comparison with control product.


High throughput screening

I.         NIR-CI may be adapted to perform analysis on multiple samples in a single field of view.

II.       Multispectral imaging for simultaneous identification and composition of multiple individual tablets in blister packaging system.13


Coating thickness

I.         Quantification of coating thickness in a single time release micro-sphere.14


Compositional information

I.         Qualitative description of composition and architecture of a solid dosage form.15


Remote identification

I.         Far field imaging system for detection of defective capsules.16


Blend uniformity

I.         Powders blend homogeneity.

II.       Evaluation of drug product homogeneity by Blend Moniter- a prototype instrument.17


Process related information

I.         Prediction of coating time and to understand batch difference due to different processing parameters.


Counterfeit drug identification

I.         NIR-CI useful for identifying small differences in ingredient distributions, “counterfeit samples” can be identified.18


Minor component detection

I.         Raman line mapping technology for identifying minor components.

II.       Small concentrations of potent APIs and uniform distribution of them in sample.19


Extraction of process related Information

I.         Raman maps could be used to indicate the source of API used in tablets.20


Particle size estimation

I.         Raman-CI for ingredient specific particle size characterization of nasal spray formulation.21

II.       Raman-CI used distinguishing between active samples and placebos.


Tablet characterization

I.         Spatial distribution and concentration of multiple components in tablets.22

II.       Multispectral imaging used to evaluate water content and to identify  thousands of individual tablets through blister packaging.



I.         Detection and identification of polymorphic forms of drug by Raman-CI.

II.       Differentiation between solvates and hydrates by MIR-CI and NIR-CI.23

III.     Level of hydration by different process parameters can be evaluated.24


Dissolution and drug delivery

I.         Drug dissolution rates can be studied.

II.       Bioavailability and Impact of crystallization on dissolution.25


Process understanding,

Troubleshooting and product design

I.         Formulation development and troubleshooting.

II.       Process optimization.

III.     Resolve contamination and dissolution issues.26

IV.     Distribution of density and tabletting force in tablets.27





In spite of various applications of CI technology, it is not without its limitations; there are some challenges which are faced by the routine implementation of CI in the pharmaceutical industry. Present chemometric tools such as principle component analysis and partial least squares are not sufficient for analyzing hypercubes.


The spectrum obtained from an isolated area of a sample by a CI system may contain contributions not only from the surface of the sample but also from several layers beneath (‘depth contribution’). Irregular sample geometry poses another challenge to CI. In an irregular shaped sample, signal differences may arise due to changes in relative path length from the detector to areas on the sample with variable height.


Raman global imaging experiences difficulties when a large field of view is required, as it is difficult to illuminate these areas with laser power28. A major factor limiting the implementation of CI for routine process monitoring arises from the relatively lengthy time necessary for hypercube image acquisition processing and classification29.



Future improvements in precision and speed in CI are likely to arise with increased processing speed of computers, improved resolution of cameras, faster hardware, more accurate and efficient algorithms. Applications of this emerging technique to pharmaceutical product quality and process monitoring will certainly arise with the development of novel solutions for online data acquisition and more powerful data evaluation strategies.


An exciting new technique, known as Integrated CI (ICI), has been proposed to reduce the data load and increase processing speed in CI30. Innovative spectrometer designs have been developed that speed up the process by enabling the sensing detector to do some of the computation. Once a collection of training spectra at all available wavelengths has been produced, it is possible to rationally select molecular filter (MF) materials to perform PCA on the incoming data by weighting signals from spectral regions with most variability. Application of such selective molecular filters (MF) with detectors would enable molecular computing to replace traditional chemometric methods, e.g. PCA. Rapid analysis coupled with reduced data storage requirements indicate that the practical realization of ICI systems as PATs is not far away.


With improvements in acquisition speed and spectral resolution, pushbroom systems may be a place for in-process monitoring of pharmaceutical dosage forms on conveyor belt systems where line pushbroom systems, in which tablets would be diverted onto a moving stage for CI, would facilitate analysis of a significantly higher number of tablets than traditional lab methods.


The development of a lower cost imager by combination of micro electro-mechanical systems (electrically programmable diffraction gratings) with spectrometers would obviate the need for costly array detectors.



The integration of spectroscopic and digital imaging in CI produces position referenced spectra, which are important for the analysis of complex multi component samples. A major advantage of CI for the pharmaceutical industry is its power to potentially assess all tablets in a production batch (not just a limited sample), thereby significantly reducing variability and risk. Moreover, its ability to spatially localize drug constituents represents a vast improvement on average concentration methods such as HPLC and MS. Rugged, flexible and non-destructive nature of CI makes it an attractive PAT for identification of critical control parameters that impact on finished product quality. Judging by the continuing emphasis on process analytical technologies to provide rapid, non-destructive and accurate analysis of pharmaceutical processes it is likely that CI will be increasingly adopted for process monitoring and quality control in the pharmaceutical industry as has been the case with NIR spectroscopy. Future innovative changes in CI equipment manufacture are likely to depress purchase costs and encourage more widespread utilization of this emerging technology in the pharmaceutical sector.




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Received on 19.06.2013          Modified on 30.06.2013

Accepted on 03.07.2013         © RJPT All right reserved

Research J. Pharm. and Tech. 6(9): September 2013; Page 967-973