2. Food Quality Evaluation Methods (FQEM)
University of Kurdistan
Food Quality Evaluation Methods (FQEM)
Lecture 3: Hyperspectral/Multispectral Imaging
Lecturer:
Kaveh Mollazade, Ph.D.
Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, IRAN.

3. This lecture will cover:
Contents
This lecture will cover:
An introduction to hyperspectral/multispectral imaging
Principles of hyperspectral/multispectral imaging
Devices and apparatus
Terminology 1

4. Introduction
During the past few decades a number of different techniques have been explored as possible instrumental methods for quality evaluation of food products. In recent years, hyperspectral/multispectral imaging technique has been regarded as a smart and promising analytical tool for analyses conducted in research, control, and industries.
Hyperspectral/multispectral imaging, or spectral imaging, collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. 2

5. Introduction 3

6. Introduction 4

7. Hyperspectral imaging vs. conventional imaging and spectroscopy
General system configurations for conventional imaging, conventional spectroscopy, and hyperspectral imaging 5

8. Hyperspectral imaging vs. conventional imaging and spectroscopy
Unfortunately, the computer vision system has some drawbacks that make it unsuitable for certain industrial applications.
- It is inefficient in the case of objects of similar colors.
It is inefficient in the case of complex classifications.
It is unable to predict quality attributes (e.g. chemical composition).
It is inefficient for detecting invisible defects.
Since machine vision is operated at visible wavelengths, it can only produce an image registering the external view of the object and not its internal view. 6

9. Hyperspectral imaging vs. conventional imaging and spectroscopy
Near infrared spectroscopy (NIRS) is rapid, nondestructive, and relatively easy to implement for on-line and off-line applications. More importantly, NIRS has the potential for simultaneously measuring multiple quality attributes.
In the spectroscopic techniques, it is possible to obtain information about the sample components based on the light absorption of the sample, but it is not easy to know the position/location information.
On the other hand, it is easy to know the position of certain features by naked eye or computer vision systems, but it is not easy to conduct the quantitative analysis of a component . 7

10. Hyperspectral imaging vs. conventional imaging and spectroscopy
Main differences among imaging, spectroscopy, and hyperspectral/multispectral imaging techniques: 8

11. Advantage of hyperspectral imaging
No sample preparation is required.
It is a chemical-free assessment method, which enables safety and environmental protection by thoroughly eliminating pollutant solvents, chemicals and/or potentially dangerous reagents during analyses.
It is a noninvasive, and nondestructive method, so that the same sample could be used for other purposes and analyses.
Rather than collecting a single spectrum at one spot on a sample, as in spectroscopy, hyperspectral imaging records a spectral volume that contains a complete spectrum for every spot (pixels) in the sample. 9

12. Advantage of hyperspectral imaging
Once the calibration model is built and validated, it becomes an extremely simple and expeditious analysis method.
It has the flexibility in choosing any region of interest (ROI) in the image even after image acquisition. Also, when an object or a ROI in the object presents very obvious spectral characteristics, that region could be selected and its spectrum is saved in a spectral library.
Due to its high spectral resolution, hyperspectral imaging provides both qualitative and quantitative measurements.
It is able to determine several constituents simultaneously in the same sample . 10

13. Disadvantages and constraints of hyperspectral imaging
Hyperspectral images contain a substantial amount of data, including much redundant information, and pose considerable computational challenges.
It takes a long time for image acquisition and analysis, therefore hyperspectral imaging technology has to a very limited extent been directly implemented in on-line systems for automated quality evaluation purposes.
Hyperspectral imaging is not suitable in some cases, such as liquids or homogenous samples, because the value of imaging lies in the ability to resolve spatial heterogeneities in samples. 11

14. Methods for hyperspectral image acquisition12

15. Devices and apparatus 13

16. Devices and apparatus: Light source
Halogen lamps:
Halogen lamps are the most common broadband illumination sources used in visible (VIS) and near-infrared (NIR) spectral regions. In their typical form, a lamp filament made of tungsten wire is housed in a quartz glass envelope filled with halogen gas. Light is generated through incandescent emission when a high operation temperature is on the filament. 14

17. Devices and apparatus: Light source
Light emitting diodes:
Owing to the demand for cheap, powerful, robust, and reliable light sources, light emitting diode (LED) technology has advanced rapidly during the past few years. Unlike tungsten halogen lamps, LEDs do not have a filament for incandescent emission. Instead, they are solid state sources that emit light when electricity is applied to a semiconductor. They can generate narrow band light in the VIS region at different wavelengths. 15

18. Devices and apparatus: Light source
Lasers:
Excited by a monochromatic light with a high energy, some biological materials (e. g., animal and plant tissues) emit light of a lower energy in a broad wavelength range. The energy change (or frequency shift) can cause fluorescence emission or Raman scattering that carries composition information of the target. The spectral constitution of the incident broadband light is not changed after light-sample interactions. The measurement is performed based on intensity changes at different wavelengths. Unlike broadband illumination sources, lasers are powerful directional monochromatic light sources. 16

19. Devices and apparatus: Light source
Tunable sources 17

20. Devices and apparatus: Wavelength dispersion devices
Imaging spectrographs:
An imaging spectrograph is an optical device that is capable of dispersing incident broad band light into different wavelengths instantaneously for different spatial regions from a target surface. It can be considered as an enhanced version of the traditional spectrograph in that the imaging spectrograph can carry spatial information in addition to the spectral information. The imaging spectrograph generally operates in line-scanning mode, and it is the core component for the pushbroom hyperspectral imaging systems. 18

21. Devices and apparatus: Wavelength dispersion devices19

22. Devices and apparatus: Wavelength dispersion devices20

23. Devices and apparatus: Wavelength dispersion devices
Filter wheels:
The most basic implementation of spectral imaging is the use of a rotatable disk called a filter wheel carrying a set of discrete bandpass filters. The main characteristic of the bandpass filters is that they transmit a particular wavelength with high efficiency while rejecting light energy out of the passband. As the filter wheel employs mechanical rotation, the light perpendicularly transmits across different filters, generating a series of narrow band images at different predetermined wavelengths. Interference filters are commonly used as optical bandpass filters. Modern interference filters are constructed with a series of thin films (usually a few nanometers thick) between two glass plates. Each film layer is made from a dielectric material with a specified refractive index. The incident light to the filter is affected by interferences due to different refractive indices of the films. 21

24. Devices and apparatus: Wavelength dispersion devices22

25. Devices and apparatus: Area detectors
CCD cameras
CMOS cameras 23

26. Devices and apparatus: Single shot imagers24

27. Terminology 25 Spectral range:
The spectral range describes the wavelength regions covered by the hyperspectral imaging system. Spectral imaging instruments could cover either the ultraviolet, visible, near-infrared or infrared wavelengths based on the required application. Hyperspectral imaging system in the visible and very near-infrared range 380–800 nm or 400–1000 nm is the most widely used in food analysis applications. Nowadays, hyperspectral imaging systems in the range 900–1700 nm that provide the accuracy required in today’s most challenging applications in food analysis are available. 25

28. Terminology 26 Spectral resolution:
The spectral resolution of the hyperspectral imaging system is related to its spectrograph as a measure of its power to resolve features in the electromagnetic spectrum. Spectral resolution is defined as the absolute limit of the ability of a hyperspectral imaging system to separate two adjacent monochromatic spectral features emitted by a point in the image. Spectral resolution is a measure of the narrowest spectral feature that can be resolved by a hyperspectral imaging system. The magnitude of spectral resolution is determined by the wavelength dispersion of the spectrograph and the sizes of the entrance and exit apertures. 26

29. Terminology 27 Spatial resolution:
The spatial resolution of the hyperspectral imaging system determines the size of the smallest object that can be seen on the surface of the specimen by the sensor as a distinct object separate from its surroundings. Spatial resolution also determines the ability of a system to record details of the objects under study. Higher spatial resolution means more image detail explained. In other words, spatial resolution is defined as the area in the scene that is represented by one image pixel. For practical purposes the clarity of the image is decided by its spatial resolution, not the number of pixels in an image. The parameter most commonly used to describe spatial resolution is the field of view (FOV). In effect, spatial resolution refers to the number of pixels per unit length. The spatial resolution is determined by the pixel size of the two dimensional camera and the objective lens as the spectrograph is designed with a unity magnification. 27

30. Terminology 28 Band numbers:
The number of bands is one of the main parameters that characterize hyperspectral imaging systems. Based on the type of spectral imaging system, i.e. multispectral or hyperspectral, the number of spectral bands could vary from a few (usually fewer than 10) in multispectral imaging to about 100–250 spectral bands in the electromagnetic spectrum in the case of hyperspectral imaging. However, the band number is not the only and decisive criterion for choosing a hyperspectral system for certain applications; the second important criterion is the bandwidth. 28

31. Terminology 29 Bandwidth:
The bandwidth is a parameter that is defined as the full width at half maximum (FWHM) response to a spectral line, describing the narrowest spectral feature that can be resolved by spectrography. Bandwidth should not be interchanged with the spectral sampling intervals, indicating that the spectral distance between two contiguous bands is the same without referring to their bandwidth. 29

32. Terminology 30 Signal-to-noise ratio (SNR or S/N)
The signal-to-noise ratio (SNR) is the ratio of the radiance measured to the noise created by the detect or and instrument electronics. In other words, signal-to-noise ratio compares the level of a desired signal to the level of background noise. In hyperspectral imaging systems, the SNR is always wavelength-dependent because of overall decreasing radiance towards longer wavelengths. The higher the ratio, the less obtrusive the background noise is . 30

33. Terminology 31 Spectral signature:
Hyperspectral imaging exploits the fact that all materials, due to the difference of their chemical composition and inherent physical structure, reflect, scatter, absorb, and/or emit electromagnetic energy in distinctive patterns at specific wavelengths. This characteristic is called spectral signature or spectral fingerprint, or simply spectrum. Every image element (pixel) in the hyperspectral image contains its own spectral signature. Briefly, spectral signature is defined as the pattern of reflection, absorbance, transmittance, and/or emitting of electromagnetic energy at specific wavelengths. 31

34. Kurdistan Nature
Zrebar Lake, Marivan