Transcript Food Quality Evaluation Methods - Lecture 3

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.
Contents
• This lecture will cover:
– An introduction to hyperspectral/multispectral imaging
– Principles of hyperspectral/multispectral imaging
– Devices and apparatus
– Terminology
Food Quality Evaluation Methods– Department of Biosystems Engineering – University of Kurdistan
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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.
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Introduction
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Introduction
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Hyperspectral imaging vs. conventional imaging and spectroscopy
 General
system
configurations
for
conventional
imaging,
conventional
spectroscopy, and hyperspectral imaging
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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.
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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 .
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Hyperspectral imaging vs. conventional imaging and spectroscopy
 Main differences among imaging, spectroscopy, and hyperspectral/multispectral
imaging techniques:
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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.
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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 .
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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.
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Methods for hyperspectral image acquisition
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Devices and apparatus
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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.
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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.
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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.
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Devices and apparatus: Light source
 Tunable sources
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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.
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Devices and apparatus: Wavelength dispersion devices
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Devices and apparatus: Wavelength dispersion devices
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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.
Food Quality Evaluation Methods– Department of Biosystems Engineering – University of Kurdistan
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Devices and apparatus: Wavelength dispersion devices
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Devices and apparatus: Area detectors
CCD cameras
CMOS cameras
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Devices and apparatus: Single shot imagers
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Terminology
 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.
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Terminology
 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.
Food Quality Evaluation Methods– Department of Biosystems Engineering – University of Kurdistan
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Terminology
 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.
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Terminology
 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.
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Terminology
 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.
Food Quality Evaluation Methods– Department of Biosystems Engineering – University of Kurdistan
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Terminology
 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 .
Food Quality Evaluation Methods– Department of Biosystems Engineering – University of Kurdistan
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Terminology
 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.
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KurdistanFood
Nature
Quality
Zrebar Lake, Marivan
Evaluation Methods– Department of Biosystems Engineering – University of Kurdistan
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