Confocal Raman Microscope RAMANtouch/RAMANforce

Sophisticated high performance RAMANtouch/RAMANforce

Outstanding software is required to analyze the large amount of data from high quality Raman images. The software provided by Nanophoton is equipped with high-speed data processing capacity and miscellaneous analytical functions to support imaging analyses performed by RAMANtouch/RAMANforce. Parts of its functions are introduced here.

  • Measurement Functions
  • Analysis Functions
  • Fast, high-resolution 3D Raman Imaging

    Using confocal optics, which detects Raman light inside the sample nondestructively, a 3D Raman image of a transparent sample can be obtained. This function repeats ultra-fast XY Raman imaging using line illumination several times while changing the stage height, and stacks the slices to create a 3-dimensional image in memory. By using the high imaging speed and depth resolution of RAMANtouch/RAMANforce, a more intuitive understanding can be obtained of the internal structure and component distribution inside a sample.

    Illustration diagram of 3D Raman Imaging

    Illustration diagram of 3D Raman imaging

    3D Raman Image of transparent core-sheath fiber

    Brand-new wide field-of-view imaging along curved surface

    RAMANtouch/RAMANforce has a brand-new wide field-of-view (FOV) Raman imaging capability. It automatically measures the surface height of a sample while capturing a wide FOV microscopic image, and makes it possible to measure a wide FOV Raman image with auto-focusing. As the images show on the right, a fine focused Raman image can be obtained from all over the surface of a curved tablet.

    The measurement algorithm is further improved to speed up the imaging 3 times faster than our previous RAMANtouch model.

    Wide FOV imaging of curved surface of a tablet

    Wide FOV imaging of curved surface of a tablet

    Comparison of imaging speed of 9mm x 9mm (313,599 pixels) area

    Comparison of imaging speed of 9mm x 9mm (313,599 pixels) area

    Fully automated particle scanning (option)

    This function detects particles in a microscopic image, and Raman measurements are automatically carried out with quick auto-focus. Fully utilizing our fast and accurate laser beam scanning technology, Nanophoton has developed three different measurement modes: "auto point measurement at center of each particle", "auto scanning of whole surface of each particle to get averaged spectrum", and "auto Raman mapping of each particle with arbitrary scanning pitch". Detected spectra are automatically identified using a real-time spectrum search during measuring. Measuring and analysis according to size classes as defined by ISO 16232 is also possible.

    Detect partcles automatically in microscopic image

    Detect partcles automatically in microscopic image

    3 useful measurement modes

    3 useful measurement modes

    Fully automated particle scanning

    Interlaced Raman Imaging for quick overview

    Interlaced imaging scans a sample skipping some pixels to quickly obtain a rough Raman image, then keeps scanning the rest while the Raman image becomes finer and finer. This mode will be the best scanning mode for a quick overview of component distribution.

    Interlaced Raman Imaging of a tablet surface

    Interlaced Raman iImaging of a tablet surface

    Other dedicated scanning modes

    Real-time multivariate analysis
    Multivariate analysis is carried out during Raman imaging to show component spectra and their distribution in real time.
    Random scanning (Option)
    Sequential order of pixel measurement is determined randomly for each imaging mode to avoid sample heating by laser irradiation at a specific local area.

  • Quantitative analysis of components dispersiveness

    This function is to obtain a quantitative analysis of component dispersiveness such as uniformity, aggregational states and locality in a Raman image. Such can be investigated from every possible angle by counting the number of particle outlines along an evaluation axis such as (X and Y) or (r and θ), or calculating the standard deviation of number of particles in a (grid) or (Voronoi diagram) layout.

    Quantitative analysis of components dispersiveness

    Quantitative analysis of components dispersiveness

    Composition rate evaluation by area ratio analysis

    The composition rate of a sample is evaluated from a Raman image which shows the distribution of components. It is calculated from a binary Raman image, which is made assuming that each pixel corresponds to only one component. Composition rate can also be calculated using CLS as described below.

    Distribution and ratio evaluation of components present on the tablet surface

    Raman Image and Analysis Result

    Raman image of surface of a tablet

    :Active pharmaceutical ingredient (API)
    :API polymorph

    Result of area ratio analysis

    Result of Area Ratio Analysis

    Particle size analysis of a component from the Raman image

    Statistical analysis of particle sizes for a specific component can be carried out from the Raman image. Approximating a particle as an oval, size distribution can be indicated in a histogram on a number basis, area basis and volume basis. The statistics (maximum, minimum, average value, standard deviation, etc.) can also be calculated.

    Particle size analysis of API in a tablet

    Raman Image
    Raman inage of a tablet

    Binary Image of API
    Binary image of APIs

    Particle size analysis of API
    Result of particle analysis

    ※The size related information was deleted because samples are commercial products

    Noise reduction by Singular Value Decomposition (SVD)

    First extract the orthogonal spectra from the Raman image and place them in descending order of contribution. By reconstructing the Raman image after the removal of spectra with a low contribution ratio (i.e., noise reduction), a clear Raman image consisting of high S/N spectra can be obtained even if the S/N of raw data is non-optimal.

    SVD equation

    Noise reduction processing of Raman image of HeLa cells

    A part of the extracted spectra by SVD
    Extracted spectra by SVD

    Raman Image
    Raw data

    After noise reduction

    Linear combination analysis by Classical Least Squares (CLS)

    Represent the unknown spectrum by a linear combination of known raw material spectra and calculate intensity of the known spectra by the least squares method. A quantitative evaluation of the composition is applicable using raw material spectra obtained under the same measurement conditions.

    CLS equation

    CLS analysis of cathode materials of a lithium-ion battery

    Raman image calculated by CLS
    CLS Raman image
    :Lithium cobalt oxide

    Composition rate calculated by CLS
    Composition rate

    Raw material spectra used for calculation
    Raw material spectra

    Comparison of raw spectrum and CLS spectrum
    Raw spectrum and CLS spectrum

    Multivariate Curve Resolution (MCR)

    MCR extrapolates the component spectra by assuming that the unknown spectrum can be expressed by linearly combining a finite number (N) of component spectra and that both the spectrum intensity and concentration of each component have non-negative values. ALS (Alternating Least Squares) (which allows for a quick calculation), and MUR (Multiplicative Update Rule) (which guarantees convergence) are both installed and available.

    MCR equation

    Raman imaging analysis of HeLa cells using MCR

    Component spectra calculated by MCR
    Component spectra

    Raman image calculated by MCR
    MCR image
    :Component 1(Protein)
    :Component 2(Lipid)
    :Component 3(Cytochrome c)