Hyperspectral Imaging Software

KemoQuant™ Analysis Software for Hyperspectral Imaging

MCR hyperspectral imaging softwareKemoQuant™ hyperspectral imaging software allows the user to discover all independently varying spectral components associated with the chemical composition of the hyperspectral image without any prior information about the image. It uses Multivariate Curve Resolution (MCR) algorithm. This is a linear additive method that extracts the spectral components present within the image and calculates the relative quantities of each of these spectral components in each spatial pixel of the image. MCR is an iterative least squares analysis technique employing non-negativity constraints on the spectral and spatial domains of the image data. These constraints create real, interpretable pure spectral components and allow the user to make spectral assignments to the chemical composition of the pixels within the image. Because hyperspectral images can be quite large (sometimes gigabytes in size), we have licensed software implementing high-speed MCR algorithms from Sandia National Laboratories. These fast and efficient algorithms can analyze large hyperspectral images in seconds.

Once the pure spectral components are discovered, these spectral components can be stored in user-created spectral libraries for future analyses. These stored spectral components then can be used repeatedly to initialize future MCR analyses or simply be applied to the image data using Classical Least Squares (CLS) to obtain the intensities of each component within the image.

KemoQuant hyperspectral imaging software can be used for fluorescence, visible, VNIR, and NIR/ SWIR hyperspectral images. When working with reflectance data, the software can ratio the raw data with white and dark reference images to obtain absorbance spectra. These absorbance data can then be used with the MCR analysis.

Example Hyperspectral Imaging Applications

Fluorescence Imaging of a Carrot

A cross section of carrot slice was imaged through a single pass of our macrPhor fluorescence imaging system. The sample was excited using a 488 nm laser and emission wavelengths 500-800 nm were collected and then analyzed using KemoQuant software.

MCR hyperspectral imaging software
Figure 1: Image created from the hyperspectral data overlaid on a photograph of the carrot imaged.
MCR hyperspectral imaging software
Figure 2: Three of the five spectral components discovered in hyperspectral image of the carrot sample.

The KemoQuant™ algorithm identified five different spectral components associated with the sample, three of which are shown in Figure 2.

The observed components resembled Chl-a (not shown), Chl-b (red), carotenoid (not shown) and two auto-fluorescing components (green and blue). Fluorescence imaging combined with MCR analysis can provide unique insights into many other components, such as the photosynthetic pigments of plant parts, algae and other samples associated with plant growth.

VNIR Whole Plant imaging

We used KemoQuant™ hyperspectral imaging software to analyze VNIR hyperspectral images of potato plants grown in a greenhouse environment. Potato plants were place upon a rotating stage while the InSight whole plant hyperspectral linescan imager recorded the image of the entire plant. Figure 3 shows the false-colored image associated with three of the six spectral components associated with the plant. Figure 4 shows the spectral components used to generate the image: Chl-a (red); carotenoid (blue); and anthocyanin, which was found predominately in the stems of the potato plant (green). Whole plant imaging can provide insight into plant phenotyping and information about the photosynthetic processes of the entire plant during the growing cycle.

MCR hyperspectral imaging software
Figure 3: Spectral image of a rotating potato plant. The colors of this image correspond to the colors of the spectral components in figure 4.
MCR hyperspectral imaging software
Figure 4: Three of the six spectral components found in the potato plant. Anthocyanin is found predominately in the stems of the potato plant.

Remote Sensing of Agricultural Fields

KemoQuant™was used to analyze hyperspectral images of agricultural soy and corn fields. MCR discovered 10 spectral components associated with the vegetation within the fields. Figure 5 shows a three-color image using four of the 10 components. Figure 6 shows the actual components. The components shown resemble Chl-a and Chl-b (shown in green), carotenoid (red), and a chlorophyll component combined with a spectral feature at 520 nm (blue). These types of analysis results can indicate the health and maturity of plants within the field. The components can be saved and used as input to the Middleton’s KemoQuant™ software for comparing different plots or charting the evolution of the component over time.

MCR hyperspectral imaging software
Figure 5: Spectral image of an agricultural field comprised of mainly soy and corn. The colors of this image correspond to the colors of the spectral components in figure 6.
MCR hyperspectral imaging software
Figure 6: Four of the ten spectral components that MCR discovered in the hyperspectral data of a sample agricultural field.

Computer Requirements

The hyperspectral images that are generated by the acquisition software can have a very large file size. If KemoQuant Analysis software is installed on the same computer that controls the hyperspectral system, the computer may also require a PCI express card to support a frame grabber or USB3 card.

  • 4th Generation or better Intel® Core™ i7 Processor
  • Memory: 16 GB+
  • Solid State Drive for data and operating system, preferably on 2 separate drives 250GB or greater
  • 500GB or better 3.5inch Serial ATA (7200 Rpm) Hard Drive

References for MCR analysis:

  1. David M. Haaland, Howland D. T. Jones, Mark H. Van Benthem, Michael B. Sinclair, David K. Melgaard, Christopher L. Stork, Maria C. Pedroso, Ping Liu, Allan R. Brasier, and Nicholas L. Andrews, “Hyperspectral Confocal Fluorescence Imaging: Exploring Alternative Multivariate Curve Resolution Approaches,” Applied Spectroscopy, 2009; 63: 271-279.
  2. Jones,  Howland D. T., Haaland, David M., Sinclair, Michael B., Melgaard, David K., Collins, Aaron M., and Timlin, Jerilyn A., “Preprocessing Strategies to Improve MCR Analyses of Hyperspectral Images”, Chemometrics and Intelligent Laboratory Systems, 117 (2012) 149-158.
  3. Pedroso, M. C.; Sinclair, M. B.; Jones, H. D. T.; Haaland, D. M., Hyperspectral Confocal Fluorescence Microscope: A New Look into the Cell. Microscopy Today 2010, 18, (05), 14-18.
  4. Howland D. T. Jones, David M. Haaland, Michael B. Sinclair, David K. Melgaard, Mark H. Van Benthem, and M. Cristina Pedroso, “Weighting hyperspectral image data for improved multivariate curve resolution results,” Journal of Chemometrics, 2008; 22: 482-490.

KemoQuant™ Hyperspectral Imaging Software Product Sheet

View page as PDF product sheet

Ordering Information
Part numberDescription
MRC-930-021 KemoQuant Software

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