Spectroscopy and Hyperspectral Imaging
What is Spectroscopy?
Spectroscopy is a commonly used technique for analysing the composition of materials. In industry it is used to analyse ores, soils, chemicals, polymers, pharmaceuticals and many other materials. It is also a valuable technique for predicting the performance of manufactured products based on the properties of raw materials.
At CSIRO, we have developed a range of statistical methods for analysing spectroscopic data and used them to solve a number of important industrial problems. Our methods can add value to your analyses by extracting more useful information from spectroscopic data.
Spectroscopy is the study of the interaction of electromagnetic radiation with a chemical substance. Modern spectroscopic methods are based on the phenomena of absorption, reflectance, fluorescence, emission or scattering.
For example infrared spectroscopy is one of the most commonly used analytical methods in industry today. The fraction of radiation reflected by a sample is measured over many contiguous wavelengths. This produces the characteristic reflectance spectrum of the sample. Some examples of infrared spectra are shown in Figure 1 (b) below.
Spectroscopic data present unique challenges because they are highly correlated. Sophisticated statistical methods need to be applied to such data either to identify the components of a mixture (and their concentration) or for prediction purposes. Some of the techniques used include partial least squares, principal component regression, penalised discriminant analysis and neural networks.
What is a Hyperspectral Image and Why is it Useful?
Hyperspectral images produce a spectrum (represented by several hundred numbers) at each pixel in an image. While greyscale or colour images can discriminate between, say, rocks and vegetation, hyperspectral images can discriminate between different types of rock or vegetation.
What are the Major Applications for Hyperspectral Imaging?
The major airborne applications are in mineral exploration, environmental monitoring and military surveillance. Major airborne hyperspectral scanners include NASA’s AVIRIS with 224 channels and HyMapTM (128 channels). Both scanners record their images at visible and infrared wavelengths. Figure 1 (a) shows 54 AVIRIS shortwave infrared images (between 1960 and 2490 nanometres (nm)) of Oatman, formerly the site of a goldmine in Arizona. The images have been atmospherically corrected and normalised in a certain way.
There are also beginning to be “terrestrial” applications of hyperspectral image analysis, in areas such as cancer detection, pharmaceuticals and food inspection.
What are the Major Processing and Analysis Challenges for Hyperspectral Image Data?
One of the most important challenges is the volume of data generated, especially in airborne surveys. These days, it is not uncommon to generate hundreds of gigabytes of data in such a survey. Therefore there is a major need for information extraction algorithms which are automated, fast and reliable! In particular there is a need to automatically and quickly identify the mineral or vegetation species that spectra such as those in Fig. 1 (b) represent. We have developed a spatial version of our spectral identification package, The Spectral Assistant, for use with hyperspectral images. It is called The Spatial Spectral Assistant (TSSA).
However, airborne hyperspectral image data present additional difficulties:
1. The width of a pixel in images such as those in Fig. 1 (a) is between 5 and 30 metres. Therefore most pixels will contain a mixture of materials, and so there is a need to “unmix” the spectrum recorded at each pixel into the spectra of its constituent materials. Unmixing is also sometimes called supervised mixture decomposition and can be thought of as an extension of classification. TSSA is designed to do this given a library of pure spectra, representing all the materials in a given hyperspectral image.
2. Atmospheric gases, viewing geometry and topography significantly distort the spectra recorded by hyperspectral scanners. These spectra need to be “corrected” so that they can be matched against a suitable spectral library. The data in Figs. 1 (a) and 1 (b) have been corrected with a leading correction package. Unfortunately, correction is difficult and existing packages are still not sufficiently reliable for use with spectral libraries. For instance, 5 of the 6 spectra in Fig. 1 (b) have an “absorption feature” just above 2000nm. This is due to residual carbon dioxide.
Identification of Mineral Mixtures
Integrated Spectronics, an innovative Australian company, has developed one of the world’s most sophisticated infrared field-portable spectrometers, the Portable Infrared Mineral Analyser (PIMA). The PIMA is the size of a shoe box and works within seconds, making it easy for geologists in remote locations to measure the infrared reflectance spectra of individual rocks.
To help geologists interpret PIMA spectra, especially mixtures, CSIRO scientists have developed The Spectral Assistant (TSA), a sophisticated software tool that assists in identifying the constituent minerals of a rock sample.
TSA Version 4 is at the heart of another CSIRO package, The Spectral Geologist, which is marketed commercially and has been sold to mineral exploration companies throughout the world.
Using a database of about 500 samples representing natural variation in 42 pure minerals, TSA uses modern and fast multivariate statistical techniques to find the most likely pure minerals and the most likely mixtures of two minerals. Figure 1 illustrates its use.
TSA Version 5 is currently under development, following the completion of an AMIRA-funded project “Automated Mineralogical Logging of Drill Core, Chips and Powders”. It will be applicable to spectrometers other than the PIMA, such as the ASD, which also measure visible spectra.
Automated Analysis of Bauxite Ore
Knowing the composition of bauxite ore helps to determine whether it can be processed profitably. CSIRO has worked with Alcoa of Australia Limted to help evaluate methods of constructing multivariate calibration models, relating Fourier transform infrared spectra of bauxite ore to the concentrations of its constituents.
CSIRO scientists evaluated different baseline correction methods along with non-linear models. This helped show that a constituent concentration, combined with a partial least squares model, yielded near-optimal results over a broad range of concentrations. This method is now in use in an automated ore analysis system at Aloa’s Kwinana mining laboratory.
Spectroscopy in Agriculture
CSIRO Mathematics, Informatics and Statistics is collaborating with CSIRO Plant Industry to develop near infrared (NIR) monitoring protocols for a variety of food products. For whole-wheat grains, it allows protein content to be assessed on arrival at the silo. In preparation of wheat-flour dough, NIR can be used to track free and bonded water during mixing.
Experiments to assess the quality and potential of Prime Hard wheat require a cheap and efficient way of assessing dough properties. NIR spectroscopy is quicker and cheaper than direct laboratory measurements of quality indicators. CSIRO statisticians used a partial least squares method to develop predictions of dough properties from NIR spectra of grain samples.
There are many more industrial applications of spectroscopy. CSIRO can help you find the right spectroscopic solution to your analytical needs.
Our highly skilled team of world class researchers and engineers is open to partnerships and collaborations for research, development, and commercialisation.
Contact us to learn more.