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 1

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 4

Light at different wavelengths interacts with matter differently 5

Advantage of hyperspectral

Spectroscopy Fast Separates wavelengths Averages the view area (spatial) Machine vision 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,… 8

This gives a 2D view of the sample at each wavelength.

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1- Reflectance

Methods

Light source Spectrometer or camera Sample 10

2- Transmittance

Methods

Spectrometer or camera Two difficulties: - Thickness affects penetration - Light disperses Light source 11

3- Interactance

Methods

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UV

Measurement examples

Detection of bones and parasites in fish (Barnes, 1986) 13

Speed: 1 fillet/sec 40 cm/s

Parasites

Manual detection 75% effective Imaging spectroscopy: Depth up to 0.8 cm detected 14

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 16

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

18 Kaffka et al., 1982

Fish

ElMasry and Wold, 2008 19

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 22

Moisture Fat Protein Glycogen 23

Meat Ageing

(Firtha, 2012) 24

(Firtha, 2012) 25

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 26

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 28