Food Quality Evaluation Techniques Beyond the Visible Spectrum

Murat BalabanProfessor, 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

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Measurement of the quality attributes, using machine vision / image analysis:

- Non-destructive

- Near real-time

- Reliable

- Distribution as opposed to average values.

Spectrum

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“Traditional”Machine vision

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Light at different wavelengths interacts with matter differently

Advantage of hyperspectral

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Spectroscopy Machine vision

Hyperspectral Imaging

FastSeparates wavelengthsAverages the view area (spatial)

Spatially resolves at pixel levelAverages wavelengths

Separates at pixel levelSeparates wavelengths.

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Hyperspectral imagingWavelengths 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.

Methods

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1- Reflectance

Sample

Light sourceSpectrometeror camera

Methods

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2- Transmittance

Light source

Spectrometeror camera

Two difficulties:-Thickness affects penetration-Light disperses

Methods

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3- Interactance

Measurement examples

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UV Detection of bones and parasites in fish

(Barnes, 1986)

Parasites

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Manual detection 75% effective

Imaging spectroscopy:Depth up to 0.8 cm detected

Speed:1 fillet/sec40 cm/s

Composition

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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

Cocoa powder

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Near infrared reflectance factor (R) spectra were recorded for 60 cocoa powder samples

The spectra were transformed to log (R) versus 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 and other parameters for predicting chemical constituents

The ratio of second derivatives of log (1/R) measured at two characteristic wavelengths.

Composition of cocoa powder

18Kaffka et al., 1982

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Fish

ElMasry and Wold, 2008

Hyperspectral water and fat analysis

Atlantic halibut

Catfish

Cod

Herring

Mackerel

Saithe

NIR cold smoked salmon

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Oyster Composition

Brown 2011

Oysters were homogenized

Composition was measured by wet chemistry, then scanned

high throughput: 250–300 samplescan be analyzed for moisture, fat, protein and glycogen each day.

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Moisture

GlycogenFat

Protein

Meat Ageing

24(Firtha, 2012)

25(Firtha, 2012)

Methods of Data Analysis

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Chemometrics:These methods include (not exclusively):

-partial least squares (PLS) regression, -multiple linear regression (MLR), and -principal component analysis (PCA).

Pork quality

Summary

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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.

Thank you

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Nikon D300SUV and IR filters removed

JenOptik 60 mm macroLens UV-VIS-IR