Food Quality Evaluation Techniques
Beyond the Visible Spectrum
Murat Balaban
Professor, and Chair of Food Process Engineering
Chemical and Materials Engineering Department
University of Auckland
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Definition of Food Quality
• Safety
- Microbial, chemical
• Nutritional content
- Micronutrients, macronutrients (composition)
• Physical and Chemical Properties
- Texture, age, etc
• Appearance and sensory attributes
- Freshness, ripeness, wholesomeness.
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Context
Measurement of the quality attributes, using
machine vision / image analysis:
- Non-destructive
- Near real-time
- Reliable
- Distribution as opposed to average values.
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Spectrum
“Traditional”
Machine vision
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Light at different wavelengths interacts with matter differently
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Advantage of hyperspectral
Machine vision
Spectroscopy
Fast
Separates wavelengths
Averages the view area
(spatial)
Spatially resolves at
pixel level
Averages wavelengths
Hyperspectral Imaging
Separates at pixel level
Separates wavelengths.
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Hyperspectral imaging
Wavelengths between 200 and 2500 nm.
The food sample is scanned with many wavelengths.
Can measure moisture,
lipids,
astaxanthin,…
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This gives a 2D view of the sample at each wavelength.
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Methods
1- Reflectance
Light source
Spectrometer
or camera
Sample
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Methods
2- Transmittance
Spectrometer
or camera
Two difficulties:
- Thickness affects penetration
- Light disperses
Light source
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Methods
3- Interactance
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Measurement examples
UV
Detection of bones and
parasites in fish
(Barnes, 1986)
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Parasites
Manual detection 75% effective
Imaging spectroscopy:
Depth up to 0.8 cm detected
Speed:
1 fillet/sec
40 cm/s
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Composition
Different chemical bonds absorb at different wavelengths
It is possible to scan the food using many wavelengths, and
correlate these with chemically measured composition.
Both the UV and IR range can be used.
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Composition of cow components
US Patent 4,631,413
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Cocoa powder
Near infrared reflectance factor (R) spectra were recorded
for 60 cocoa powder samples
The spectra were transformed to log (R) versus l, and to
the second derivative of log (1/R) versus wavelength for
correlation with compositional data
Linear stepwise regression techniques were used to
determine the optimum l and other parameters for
predicting chemical constituents
The ratio of second derivatives of log (1/R) measured at
two characteristic wavelengths.
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Composition of cocoa powder
Kaffka et al., 1982
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Fish
ElMasry and Wold, 2008
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Hyperspectral water and fat analysis
Atlantic halibut
Catfish
Cod
Herring
Mackerel
Saithe
NIR cold smoked salmon
Oyster Composition
Oysters were homogenized
Composition was measured by wet
chemistry, then scanned
high throughput: 250–300 samples
can be analyzed for moisture, fat,
protein and glycogen each day.
Brown 2011
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Moisture
Protein
Fat
Glycogen
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Meat Ageing
(Firtha, 2012)
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(Firtha, 2012)
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Methods of Data Analysis
Chemometrics:
These methods include (not exclusively):
- partial least squares (PLS) regression,
- multiple linear regression (MLR), and
- principal component analysis (PCA).
Pork quality
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Summary
In addition to visible light analysis (size, color, shape,
texture, etc) UV and IR regions can also be used for
quality evaluation.
These include composition, specific objects (e.g. parasites,
or bones), tenderness.
Advantages: Use of multiple wavelengths allow more
insight into the materials
Disadvantages: Multiple wavelengths require complex
chemometric analysis.
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Thank you
Nikon D300S
UV and IR filters removed
JenOptik 60 mm macro
Lens UV-VIS-IR
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