Sonja Breuninger

In recent years, confocal Raman imaging and related techniques have become more and more popular in many fields of application such as materials science, pharmaceutics, life sciences, geoscience, food technology, and many others. The available Raman microscopes are focused on user-friendly and intuitive operation. Additionally, several key qualitative factors of confocal Raman imaging microscopes should be considered to guarantee clear and substantial results. In this article, the following five criteria–speed, sensitivity, resolution, modularity and upgradeability, and combinability–are explained, and factors that influence them positively or negatively are examined. On the basis of application examples, modern Raman imaging and correlative imaging techniques are introduced, and state-of-the-art practice examples from polymer research, pharmaceutics, low-dimensional materials research, and life sciences are presented.

	It was 90 years ago that Chandrasekhara Venkata Raman and Kariamanickam Srinivasa Krishnan first documented "A New Type of Secondary Radiation," which then became known as the 

(1,2). Raman spectroscopy is based on this effect, and it is used for qualitative and quantitative analysis of the chemical components and molecules of a sample. It is a nondestructive method that requires little, if any, sample preparation.

	Nevertheless, Raman spectroscopy long remained a technique that was only performed in specialized laboratories. In recent years, however, it has been increasingly losing its outsider status. One reason for this is the development of the confocal Raman microscope, with which not only individual Raman spectra, but also complete images generated from thousands of spectra, can be acquired. Through continuous development, commercially available Raman microscopes are also becoming more user-friendly. For example, modern software interfaces guide the user through the Raman measurement and the subsequent data analysis.

	There are several key factors that can be used as criteria for determining the quality of confocal Raman microscopes, which are explained as follows:

	In the past, exposure times of minutes to hours were common for acquiring single Raman spectra, but today the process generally takes fractions of a second to less than one millisecond. In one second, more than 1000 Raman spectra can be recorded. Thus, a Raman image can be generated within a few minutes. To achieve this acquisition speed, the Raman imaging system should be equipped with optimized optics and an electron multiplying charge coupled device (EMCCD) camera.

	High acquisition speeds are particularly important for measurements on sensitive and valuable samples in which the excitation energy must be as low as possible. Time-resolved investigations of dynamic processes can also benefit from rapid Raman spectral acquisition. Operating costs can also be reduced by shorter analysis times concurrent with increased data rates. Having a high system speed is also advantageous for time-critical work.

	The signal sensitivity of a system is critical for the quality of the results, and is especially important when weak Raman signals are to be detected.

	To achieve the best possible sensitivity, a confocal beam path, such as using a diaphragm aperture, must be employed to eliminate light from outside the focal plane to increase the signal-to-noise ratio. The entire Raman imaging system should also be optimized for high light throughput. This includes a spectrometer that ensures throughput of over 70%, and is designed for measurements with low light and signal intensity. Charge-coupled device (CCD) cameras optimized for spectroscopy, which exhibit more than 90% quantum efficiency in the visible range, are most commonly used as detectors. Finally, the use of almost lossless photonic fibers ensures efficient light and signal transmission.

	The resolution of a Raman system comprises both spatial and spectral resolution. The spatial resolution includes the lateral resolution (

-direction). The spatial resolution is determined by the numerical aperture (NA) of the objective used and the excitation wavelength. In addition, a confocal microscope produces images with a higher contrast, because the background signal is reduced. The smaller the aperture of a confocal microscope, the higher its resolution. In a confocal Raman microscope, the lateral resolution is about 200–300 nm, and the depth resolution below 1 micrometer. A confocal microscope can also create optical sections from different focal planes, which can be used with transparent samples for depth profiles and 3D images.

	Spectral resolution defines the ability of a spectroscopic system to separate Raman lines near one another. Symmetric peaks in the spectrum are ensured by a spectrometer design that operates free of coma and astigmatism. The grating used, the focal length of the spectrometer, the pixel size of the CCD camera, and the size of the aperture also affect the spectral resolution.

	At room temperature, the width of the Raman lines is typically greater than 3 cm

, but some applications (gases, low temperature, or stress analysis) may require significantly higher resolution (Figure 1).


	Figure 1: Speed, sensitivity, and resolution are some of the characteristics that can be used to identify a high quality Raman microscope. These three characteristics should not be mutually exclusive. Ideally, the Raman imaging system should be configured in such a way that high-resolution images with a high signal-to-noise ratio can be acquired in a short period of time.

	The introduction of Raman microscopy into laboratories puts new demands on commercially available systems. These requirements can sometimes appear contradictory: easy operation with diverse functionality, a wide range of applications with optimized sensitivity, low cost, and high performance. To offer users a Raman system tailored to their individual requirements, it is particularly important that systems have a modular design that can be adapted to new conditions through being reconfigured or upgraded. A system can be optimized for specific requirements by individually combining suitable lasers, filters, lenses, spectrometers, and detectors. With such a customized Raman imaging system, the user is able to obtain meaningful Raman images, perform 3D volume scans, and create depth profiles.

	Confocal Raman microscopy can be combined with other microscopy techniques. By using different methods and correlating the data, the user attains a more comprehensive understanding of the sample. Common examples of correlative microscopy techniques are Raman-atomic force microscopy (AFM), Raman–scanning near-field optical microscopy (SNOM), and Raman–scanning electron microscopy (SEM). To correlate the data of these disparate technologies, the exact same sample location must be examined by each approach. If different instruments are to be used, finding this sample location can be very difficult and time-consuming. This is made much easier with a hybrid system that combines the different analysis methods in one instrument so that the sample can remain in place during all measurements.

	Some applications of correlative Raman microscopy are:

	For Raman microscopy, most samples do not need to be treated, stained, or otherwise prepared prior to measurement. The combination of a confocal Raman microscope with a profilometer module for focus stabilization allows rough or inclined surfaces to be examined (3,4). During Raman analysis, the examination area is kept constantly in focus by the simultaneously acquired profilometry data. This also compensates for thermal shifts and enables long-term measurements. The application example in Figure 2 shows the analysis of a microstructured silicon sample. The chemical image of the Raman measurement was overlaid onto the topographic profile measurement.


	Figure 2: Topographic Raman image of a silicon microstructure.

	Fluorescence microscopy has been a widespread imaging method for the analysis of biological cells and organisms for decades. Samples are stained with fluorescent dyes or organisms are genetically engineered to express fluorescent proteins. The fluorescence signal is usually much stronger than the Raman signal. Nevertheless, correlative Raman fluorescence measurements are possible with an appropriate system. Figure 3 shows a Raman fluorescence image of a live cell culture of eukaryotic cells. An inverted confocal Raman microscope was used to examine the cells in their aqueous cell culture medium in the Petri dish. The cell nuclei were stained with the fluorescent dye 4',6-diamidino-2-phenylindole (DAPI). An excitation wavelength of 532 nm was used for the Raman measurement. An image with 50 x 40 µm² and 150 x 120 pixels was acquired. A Raman spectrum was recorded at each pixel. The recording time was 0.2 s per spectrum. In the correlative Raman fluorescence image, the nuclei are shown in blue (recorded with fluorescence microscopy), the nucleoli in green, and the endoplasmic reticula in red (recorded with Raman microscopy). The corresponding Raman spectra are shown in the same colors.


	Figure 3: Showing (a) correlative Raman fluorescence image of primate cells in a cell culture. Blue objects are nuclei recorded by fluorescence microscopy, red objects are endoplasmic reticula, and green circles are nucleoli recorded by Raman microscopy. (b) Raman spectra associated with the image.

	The combination of Raman microscopy, which provides information about the type and distribution of molecules in a sample, and the high-resolution AFM technique, which determines the surface characteristics of a sample, enables the visualization of both chemical and morphological properties.

	Here, the analysis of a 1:1:1 mixture of polystyrene (PS), 2-ethylhexyl acrylate (EHA) and styrene-butadiene rubber (SBR) is shown. For this, a correlative Raman–AFM microscope was used, in which Raman microscopy and AFM technologies are fully integrated.

	The measurement with AFM in intermittent contact or alternating current (AC) mode documents the topography of the polymer mixture (Figure 4a). The simultaneously recorded phase image (Figure 4b) provides information on the viscosity and elasticity of the individual components of the polymer mixture. The confocal Raman image (Figure 4c) shows that PS (red) and EHA (green) are present separately. SBR (purple) partly mixes with EHA (mixture shown in blue). By correlating the Raman image with the AFM image, the chemical information can be linked to the structural information (Figure 4d).


	Figure 4: Correlative, high resolution Raman-atomic force microscopy (AFM) image of a 1:1:1 mixture of polystyrene (PS), 2-ethylhexyl acrylate (EHA), and styrene-butadiene rubber (SBR). The image shows (a) the topography of the polymer mixture determined with AFM in the AC mode; with (b) the phase of the AFM image showing the fine structure of the compound. In (c) a color-coded, confocal Raman image is shown as generated from the Raman spectra, showing the distribution of the polymers: PS (red), EHA (green). SBR (purple), and SBR-EHA mixture (blue). In (d) a correlative Raman-AFM image is shown where the topography and distribution of the different polymers can be visualized.

	Scanning electron microscopy (SEM) is a well-established method for structural surface analysis. By combining Raman imaging with SEM in a correlative microscope, it is possible to combine results of SEM structural analysis with chemical and molecular information from confocal Raman microscopy (5). The sample is placed in the vacuum chamber of the electron microscope. Both analysis methods are then carried out automatically at the same sample location. The obtained SEM and Raman images can then be superimposed.

	In Figure 5, a structure several atoms in thickness comprised of graphene layers was analyzed by correlative Raman–SEM microscopy. The Raman image consists of 22,500 spectra with 50 ms recording time per spectrum. While in the SEM image the contrast between the substrate and the graphene flake is visible, in the Raman image the number of graphene layers and their different orientations can be analyzed. This is not possible with SEM alone.


	Figure 5: Correlative Raman-scanning electron microscopy (SEM) image of a multilayer graphene flake. The different colors show folds and orientations in the graphene that can be identified by Raman spectroscopic analysis.

Raman Particle Identification and Characterization

	High-resolution investigations of particles are of great interest in many fields of application such as environmental science, pharmaceutical research, and many others. Combining a particle analysis tool with the fast, label-free, and nondestructive Raman imaging technique makes it possible to find, classify, and identify particles in a sample according to their size, shape and chemical characteristics. The physical and molecular attributes of the particles in a sample may be correlated and qualitatively and quantitatively evaluated. Figure 6 shows the results of particle analysis carried out on a cosmetic peeling cream sample. Figure 6a shows the overlay of an optical bright field microscope image with the corresponding confocal Raman image. Particles are identified according to their size and shape and further characterized by their molecular properties through confocal Raman imaging. The chemical analysis revealed anatase and boron nitride particles in an oil matrix (Raman spectra shown in Figure 6b). Further evaluation of the results determines the quantitative prevalence of the molecular sample components in the particles (Figure 6c) and also the distribution of chemical compounds correlated to particle size (Figure 6d). In extended analyses, the chemical characteristics of particles could also be linked to parameters such as area, perimeter, bounding box, Feret diameter, aspect ratio, equivalent diameter and many others. This illustrates the potential for comprehensive investigations of particles in many fields of application.


	Figure 6: Particles in a cosmetic peeling cream sample; (a) the optical bright field image overlaid with the confocal Raman image; (b) the corresponding Raman spectra of the molecular components in the sample; (c) a pie chart of the quantitative compound distribution in the sample; and (d) the graphical representation of the correlation between chemical characteristics and particle size.

	(2) J. Toporski, T. Dieing and O. Hollricher, Eds., 

(Springer International Publishing, New York, New York, 2nd ed., 2018).

	(5) G. Wille, C. Lerouge, and U. Schmidt, 

Damon Strom, Sonja Breuninger, Harald Fischer, Andrea Richter, Ute Schmidt, 

 are with WITec GmbH, in Ulm, Germany. Direct correspondence to: 

Articles

Five key qualitative factors–speed, sensitivity, resolution, modularity and upgradeability, and combinability–contribute to the quality of confocal Raman imaging microscopes. Using application examples, this article introduces modern Raman imaging and correlative imaging techniques, and presents
state-of-the-art practice examples from polymer research, pharmaceutics, low-dimensional materials research, and life sciences.

Criteria for High-Quality Raman Microscopy

	The sensitivity of a high-resolution Raman imaging system is crucial to the quality of the acquired information. The spectral and spatial resolutions are among the primary factors that influence the obtainable results. The limits of resolution are defined theoretically by the laws of physics, but are experimentally determined by the instrument parameters. In this article, the theoretical background and the possibilities in practical applications are discussed.
	 

Key Elements of Confocal Raman Microscopy for High-Resolution Imaging

Newly developed software tools provide various functions for the acquisition, evaluation, and processing for high-resolution 3D Raman images. The application note exemplarily describes the basic usage of several software tools.

Confocal Raman microscopy is a high-resolution imaging technique that has become widely used for the characterization of materials and specimens in terms of their chemical composition. With 2D and 3D Raman images, information regarding the chemical compounds and their distribution within the sample can be illustrated clearly. The new WITec Project FOUR software was especially developed to perform large-area and 3D Raman imaging and to ensure an efficient workflow from data acquisition, to evaluation, to post-processing, and then identification of the best results from each measurement. 

Generation of Volume Scans, Image Stacks, and 3D Visualization

Volume scans are a valuable tool in providing information about the dimensions of objects or the distribution of a certain compound throughout the sample. The generation of volume scans and 3D images always requires large data sizes. In order to generate 3D images, confocal 2D Raman images of different focal planes are acquired by scanning throughout the sample in the z-direction. The 2D images are then combined into a 3D image stack. 

 Figure 1: a) 3D color-coded Raman image of pollen in honey. Green: Pollen; Red, Blue, Cyan: Different crystalline phases in the honey; Yellow: Honey. Image parameters: volume size: 50 Ã 50 Ã 50 Âµm3; 150 Ã 150 Ã 50 = 1,125,000 spectra; integration time per spectrum: 12.2 ms. b) Example spectrum before and after background subtraction: With the automatic background subtraction varying background signals from large data sets can be automatically identified (red line) and removed. c) Corresponding Raman spectra after background subtraction.			

A sample of natural honey was investigated. For the 3D image, 50 single 2D Raman images (x-y scan) were generated from different z-positions through the sample. The scan range was 50 × 50 µm

  with 150 × 150 pixels. The raw data file size was about 2.1 GB. In a first processing step the main sample components were identified by the cluster analysis function of the Project FOUR software. Since biological samples tend to show a strong fluorescence background signal, an automatic background subtraction filter was additionally applied in order to remove varying background signal from the data set (Figure 1b). The background subtracted spectra of the main components are shown in Figure 1c. The data were further processed and optimized for 3D visualization by applying the basis analysis algorithm for spectral demixing. The resulting signal intensity values were used to generate the 3D image stack with a third party software. The color-coded 3D stack has a volume of 50 × 50 × 50 µm

  and displays a pollen shown in green surrounded by different crystalline phases of the honey in red, blue, and cyan and the liquid honey phase displayed in yellow (Figure 1a). 

Newly developed software tools provide various functions for the acquisition, evaluation, and processing for high-resolution 3D Raman images.

Software Tools for 3D Raman Imaging

The sensitivity of a high-resolution Raman imaging system is crucial to the quality of the acquired information. The spectral and spatial resolutions are among the primary factors that influence the obtainable results. The limits of resolution are defined theoretically by the laws of physics, but are experimentally determined by the instrument parameters. In this article, the theoretical background and the possibilities in practical applications are discussed.

Confocal Raman microscopes are the instruments of choice for many Raman measurements in a wide variety of applications ranging from geosciences (1–3), biology (4–6), nanocarbon materials (7–9) to pharmaceutical compounds (10,11), just to name a few. This article sheds light on the possibilities and, in part, the origins in terms of spectral and spatial resolution for confocal Raman systems in general. 

Any confocal Raman system will have a spectral resolution which is mainly determined by the following parameters:  					

the focal length of the spectrometer (the longer the focal length, the higher the spectral resolution)

the grating (the higher the groove density, the higher the spectral resolution)

the pixel size on the charge-coupled device (CCD) camera (the smaller the pixels, the higher the spectral resolution)

the entrance slit or pinhole (the smaller the slit or pinhole the higher the spectral resolution)

the line shape preservation (equals imaging quality) of the spectrometer.

In some cases, one of the parameters can put limitations on the spectral resolution. If, for example, the projection of the pinhole onto the CCD is already large compared to the pixel size on the CCD camera, then a further reduction of pixel size will not increase the spectral resolution. 

Please note that the microscope components such as the objective used for collection of the signal should not influence the spectral resolution if the entrance slit or pinhole is the limiting element. This is preferential with confocal Raman microscopes. 

The determination of the spectral resolution is often a point of debate. First, one should clearly differentiate the spectral resolution from the sensitivity of the system to detect shifts of individual peaks. Relative peak shifts can be detected with a much higher accuracy using fitting algorithms as has been demonstrated with a sensitivity down to 0.02 rel. 1/cm standard deviation of the peak shift of a Si peak (12). The maximum achievable fit accuracy depends heavily on the number of detected photons and the width of the peak that is fitted. This shift analysis is especially relevant for examining stress within a sample, but may not be taken as a measurement for the spectral resolution. 

The spectral resolution, which determines how the system can measure (that is, full width at half maximum [FWHM] of a narrow peak or how well overlapping peaks can be differentiated), needs to be addressed separately from the peak shift sensitivity. There are various ways to state the spectral resolution, and some of the most common ones are outlined below. 

 is the difference in wavenumbers when moving from one pixel on the CCD camera to the next and is independent of factors such as slit width or peak width of the detected peak. This can only be seen as the true resolution limit if the pixel size and not the size of the entrance slit or pinhole is the limiting factor. For example, if the image of the slit or pinhole on the CCD camera is 100 µm in diameter and the pixel size on the CCD camera is 26 µm, then the resolution would be significantly worse than the distance (in wavenumbers) between two pixels. Since wavenumbers are measured in reciprocal space, it also needs to be noted that the pixel resolution will differ depending on the spectral position where it is determined. The resolution close to the Rayleigh line can, in this way, differ by almost a factor of two from the pixel resolution near 3500 rel. 1/cm in the case of 532-nm excitation. 

For this criterion two times the pixel resolution is taken. The logic behind this is that to discriminate two neighboring peaks one needs to have one pixel on one peak, one in the minimum between the peaks, and a third one on the next peak. This criterion is analogous to the Nyquist theorem in signal processing. The same limitations as outlined for the pixel resolution criterion apply in this case.  

Full Width at Half Maximum of Atomic Emission Lines

Atomic emission lines are typically much narrower than any Raman line. Their narrow width makes them a good probe to check the resolution. Figure 1 shows an atomic emission line of mercury near 579.07 nm. The 

-axis is given in units of rel. 1/cm assuming a 532.00-nm excitation laser. The spectrum was recorded using a mercury and argon calibration lamp coupled via a 10-µm core diameter multimode fiber to a UHTS300 spectrograph (WITec GmbH) equipped with both 1800- and 2400 grooves/mm gratings (BLZ at 500 nm) and a Newton electron multiplying charge coupled device (EMCCD) camera with a pixel size of 16 µm. The integration time was 0.1 and 0.24 s, respectively, for the spectra. The FWHM derived through this approach is a good measure of the resolution, but care must be taken to ensure that enough points are available within the curve to ensure a good fit to the curve. 

Figure 1: Spectra of the mercury atomic emission line at 579.066 nm plotted as a function of wavenumber assuming a 532.00-nm excitation. The red line is the fitted curve (pseudo voight function) and the blue diamonds are the measured data points for the 1800-grooves/mm grating. The green triangles and the purple curve show the results obtained using the 2400-grooves/mm grating.			

Measurement of Peak Resolution on Known Reference Samples

There are a few samples that are established standards to demonstrate spectral resolution. The most prominent is probably CCl

. Figure 2 shows two spectra of this substance recorded with different spectral resolutions. It can clearly be seen, that the peaks are nicely separated in the red spectrum whereas the separation is not as clear for the purple spectrum. 

Figure 2: Raman spectrum of CCl4 with 532-nm excitation at different spectral resolutions.			

Therefore, spectral resolution can be defined in many different ways and, thus, it is advisable to specify exactly how a spectral resolution was or should be determined. Comparing actual measurement results under identical measurement conditions is certainly one of the best ways to illustrate this. It should also be noted that with few exceptions the natural linewidths of Raman lines are typically larger than 3 rel. 1/cm. Taking the Nyquist criterion into consideration, a resolution in the range of 1 rel. 1/cm should be sufficient for the majority of samples. 

When considering the spatial resolution of a confocal Raman microscope one may distinguish between the lateral (

) resolution and the depth resolution. However, in either case, it is possible that the limitations arise because of one of the five following points: 					

basic physics (that is, diffraction limit)

limited positioning accuracy of the mechanical components used

limitations because of the optical components used in the beam path (that is, beam distortion)

nondiffraction limited sample illumination

In terms of the positioning system, it is important to differentiate the single step accuracy of a stepper motor, the positioning reproducibility, and linearity. The reproducibility and linearity are the key factors for an imaging system since this allows a line by line imaging. If, for example, a straight line is imaged, then this line will only be imaged with the accuracy of the positioning reproducibility even though the step size might be much smaller. Figure 3 illustrates this effect with Figure 3a showing insufficient positioning reproducibility and Figure 3b showing sufficiently high positioning reproducibility. 

Figure 3: A straight line imaged with (a) insufficient positioning reproducibility and (b) sufficient positioning reproducibility.			

The necessary positioning reproducibility should therefore be several factors better than the smallest imaged object, or if the system should allow the best physically possible resolution, several factors (that is, 10×) better than the diffraction limit. 

Using high-quality components and positioning systems should allow imaging approaching the limits of physics. 

Based on the diffraction theory of Ernst Karl Abbe, Lord Rayleigh defined the diffraction limit in 1896. Rayleigh thereby quantified the minimal distance at which two point light sources can be identified. In this case, one of the sources is located at exactly the distance of the first minimum of the Airy function (point spread function) of the other one. This distance (

) can be expressed as a function of the wavelength emitted (λ) and the numerical aperture (NA) of the objective used as: 

The equation 1.22 λ/NA is often also found in this context, but this describes the distance between the two first minima of the Airy function and not the diffraction limit as described by Lord Rayleigh. Using the definitions of the Airy function the resolution can be easily derived by how much the intensity between the two emitting points has to decrease according to the Rayleigh criterion. Please note that the distance is different for other criteria of diffraction such as the Abbe criterion or the Sparrow criterion. 

To derive the resolution of a microscope using either of those criteria it would be necessary to have a variety of very small, well scattering objects (small compared to the size of the Airy disk; for example individual TiO

 particles) at varying distances. Following high-resolution Raman imaging, one would then have to analyze the signal intensities and based on the detectable distances between the spots one would have to derive the resolution. 

An easier way to determine the lateral resolution power of a system uses the FWHM of small objects. Based on the Airy function the relation between the FWHM of the light emitted from an object and the diffraction limited distance (

Because the measured signal is always a convolution between the object size, the emission characteristics, and the system function, the objects measured in such experiments also have to be small compared to the Airy disk. Mathematically speaking, the object's size should approach a delta function for the convolution. Such small objects naturally have only limited material which scatters and thus one may expect small Raman signals emitted by them. Carbon nanotubes (CNTs) on the contrary show a large Raman signal while being very small in diameter (typically ranging from subnanometer to a few nanometer range) and comparatively long (up to a few micrometers typically). These samples are thus the ideal probes to check the lateral resolution of a confocal Raman microscope. For a 532 nm excitation laser and an objective with an numerical aperture (NA) of 0.9 one should therefore be able to obtain a FWHM across CNTs of about 301 nm. 

Figure 4a shows the integrated intensity of the G-band on a sample of CNTs on a Si substrate. The scan range was 1.5 µm × 1.5 µm with 50 × 50 points and an integration time of 23 ms per point was used. An alpha300R system (WITec GmbH) was used for the measurement in combination with a fiber-coupled, frequency-doubled Nd:YAG laser (532 nm emission), a Zeiss 100× NA 0.9 objective, and a 50-µm multimode fiber acting as the pinhole for confocal microscopy. For this microscope and objective this corresponds to a projected pinhole size of 500 nm in the focal plane. The spectrometer used was a UHTS300 (WITec GmbH) with a 600-grooves/mm grating (BLZ at 500 nm) combined with a back-illuminated CCD camera.  

Figure 4: Integrated intensity of the G-band of a carbon nanotube with (a) the cross section position marked in red and (b) the cross-sectional intensity along this line. The lateral resolution of the microscope system can be characterized by the FWHM and is about 272 nm.			

The red line in Figure 4a indicates where the cross section shown in Figure 4b was extracted from. It can clearly be seen that the FWHM is even narrower than the predicted minimum. 

The theory of confocal microscopy (for example, see reference 13) shows that the achievable resolution can further be decreased by a maximum of 1/√2. In confocal Raman microscopy this fact is rarely used to its limit, because it requires a strong reduction of the pinhole diameter, which in turn reduces the throughput. 

Depth resolution is the best proof of the confocality of a system. The instrument design has a key influence on the achievable resolution, but the pinhole or slit as well as the way the sample is illuminated also play a crucial role for the depth resolution. Therefore, these points are outlined below before the physical limit of the depth resolution is discussed. 

The sensitivity of a high-resolution Raman imaging system is crucial to the quality of the acquired information.