Measurement of food colour and

structure using digital imaging

Martin Whitworth

Campden & Chorleywood

Food Research Association

www.campden.co.uk

Contents

• Colour imaging and measurement of

foods

• C-Cell analysis of bread structure

• X-ray computed tomography of

structural changes during baking

• Hyperspectral near infrared imaging of

food composition

Colour imaging

Assessment of food colour

• Colorimeters are used for quality

control, including on-line

measurement.

• Coloured reference tiles and cards

are used as references for some

specific applications.

• Photographs and printed images

are widely used as visual

references in production areas;

several retailers require suppliers

to provide product images.

Colour imaging

• Digital images and prints

are widely used for

documentation,

specification and quality

control.

• Colour reproduction is

often poor and

inconsistent.

• Calibrated imaging systems can provide better

specification images.

• Image analysis can be used to measure aspects such

as colour distribution.

Colour imaging facilities at CCFRA

DigiEye

imaging

system

Profiled

monitorD65

viewing

cabinet

Monitor

profiling

device

Eye-One iO

spectrophotometer

Profiled

printer

Colour management

software:

DigiEye

Photoshop

Profile Maker

CCFRA software

Image capture• DigiEye imaging system

• Controlled illumination conditions

• Calibration against CIELAB colour space

Camera calibration

• Set white balance.

• Take image of uniform

white card to correct for

spatial variations in

illumination.

• Take image of test card

with 240 known

colours.

• Calculate mapping

between reference XYZ

colour values and

measured RGB values.

Printing

• Epson Stylus Photo R2400

printer

• Lyson continuous ink

system used to reduce ink

costs

• Calibrated using a custom

ICC profile

Creating an ICC printer profile

Printer driver

RGB data

Ink loading

CIELAB data

Printer profileRGB test image

Measure print

Calculate profile

specifying which

RGB values to

send to printer to

achieve the

required CIELAB

colour

Printed image

Editing profile for use with DigiEye

Edit profile to

match measured

colours to original

CIELAB data.

Printer driver

RGB data

Ink loading

CIELAB data

Printer profile

Printed image

Measure print

with DigiEye

CIELAB test image

Printer profiling performance

Mean ΔE=2.2 for DC Colorchecker chart

Products Profiled print

Development of colour scales

• Aim to develop a systematic approach for

producing sets of images for food assessment

against defined colour scales.

• Approach:

• Identify relevant attributes by sensory profiling

• Develop image analysis measurements of these

attributes and define a numerical scale.

• Determine the spacing of distinct points on the scale

from the sensory data.

• Select images at these points to produce a chart.

Case study: Bread roll crust colour

• Rolls baked with 36

recipe and process

variations

• Measurements:

• Sensory descriptive

analysis by 9 assessors

• Colorimeter

• DigiEye images

• Diffuse

• Diffuse + oblique

a*

b*

L*

Degree of bake, λLight

Dark

Roll crust colour measurements(DigiEye images, diffuse, 10mm central region)

• Scale assigned

based on fitting

a principal curve

• Reduces colour

variation to a

single parameter

• Provides a

general

approach for

defining colour

scales

Distribution of degree of bake

30

40

50

60

70

80

90

100

20

10

0

Degree

of bake

30

35

90

8090

30

20

65

70

7060

70

252590

Synthetic images

• Colour of each pixel shifted parallel to the curve.

• More realistic effect than a global colour shift.

• Could be used to specify acceptable colour

limits.

λ=30 λ=50 λ=70

Image analysis

Cherry cake

a*

b*Crumb Cherries

Ruler

Background

1330 (10.2%)

1409 (11.1%)

Area of

cherries (mm2)

Crumb colour

28.9 3.413.4 5.475.7 6.91808213061Bottom

29.4 3.914.8 5.773.5 8.91798112714Top

b*a*L*

Width

(mm)

Height

(mm)

Area

(mm2)

Crust colour variation

0

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12 14

Distance from surface (mm)

Colo

ur

valu

e

Top crustLeft crustRight crustBottom crust

L*

b*

a*

Distribution of cherries

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

Height within slice (mm)

Are

a o

f ch

err

ies (

mm

2)

Upper slice based on discrete cherries

Lower slice based on discrete cherries

Upper slice based on integrated area

Lower slice based on integrated area

0

100

200

300

400

500

600

700

800

0.0 - 2.5 2.5 - 5.0 5.0 - 7.5 7.5 - 10.0 10.0 - 12.5 12.5 - 15.0 15.0 - 17.5 17.5 - 20.0

Diameter range (mm)

Are

a o

f ch

err

ies (

mm

2)

Upper slice

Lower slice

Size

distribution

Distribution

with height

C-Cell image analysis system

• Designed for bakery use.

• Sliced products imaged

under oblique illumination,

providing good contrast of

structure.

www.c-cell.info

• Analysed to measure

structure and dimensions.

• 50 instruments in use in

15 countries.

Measurement of cell sizes in bread

davg=2.00mmdavg=1.27mm

davg=2.20mmdavg=1.61mm

Cell

are

a (

% o

f sli

ce a

rea)

Cell diameter (mm)

Cell

are

a (

% o

f sli

ce a

rea)

Cell diameter (mm)

Cell

are

a (

% o

f sli

ce a

rea)

Cell diameter (mm)

Cell

are

a (

% o

f sli

ce a

rea)

Cell diameter (mm)

High magnification for extrudates

250

0 0.5 1.0 1.5

300

350

CaCO3 (% of maize mass)

Scre

w s

peed

(rp

m)

Cell diameter (mm)

1.82

1.35

1.00

1.86

1.44

1.16

1.89

1.29

1.12

1.90

1.31

1.10

(Mean of 3

samples)

Area (cm2)

1.59

1.45

1.16

1.48

1.26

1.21

1.47

1.27

1.16

1.61

1.25

1.09

X-ray computed tomography

imaging of bakery processes

X-ray computed tomography of baking

• Products are baked in an

insulated glass chamber.

Heater

Fan

Baking chamber

CT scanner• Baking is carried out using

hot air blown through the

chamber.

• Non-destructive images are taken during processing

Final proof

• Single piece moulding

creates a spiral

structure in the dough.

• During proof, the

dough expands to fill

the pan and then

expands vertically.

Bread baking• A break forms in the crust

early during baking.

• Internal expansion causes oven spring.

• A zone of expansion moves inwards.

• Peripheral structures are compressed against the pan walls.

• As the crust becomes firm,

oven spring ceases and

material is compressed

against the underside of the

crust.

Baking processes in cakes

Sponge

MuffinsHigh ratio cake

1.0

0.1

0.0

0.2

0.3

0.4

0.5

0.6

0.7

0.9

0.8

Relative

absorbance

Air

Water

Hyperspectral near infrared imaging

of food composition

Hyperspectral near infrared imaging• Gilden Photonics/Specim

instrument

• Takes images with a full NIR

spectrum for each pixel.

• Cooled HgCdTe camera

sensitive over 900-2500nm

NIR wavelengths, fitted with

spectrograph.

• Field of view ~8mm to 30cm.

• Scan time ~5s

• Enables mapping of food

composition.

Method

• Data cube collected

• Sliced for analysis

Width of deck

Wavelength

900nm

2500nm

Scan

direction

Spectra for each pixel

Images for each wavelength

Mean spectra for selected regions

Doughnut

• Measure areas under peaks and plot distribution

• Doughnut loses water and absorbs fat at the

surface during frying

Fat Water

Baguette moisture

Unbaked

1 hour 5 hours 24 hours 48 hours 96 hours

Stored at 65% R.H.

Stored at 80% R.H.

Mois

ture

(%

)

10

20

30

40

50

60

45.4 45.6 44.3 40.5 26.4

45.7 45.4 44.7 36.2 18.0

43.1

Plots: NIR data

Values: Reference values for central 20mm discs

Conclusions

• Calibrated colour imaging can improve the accuracy of

images used for food specification. Image analysis can

provide new quantitative measurements.

• The C-Cell imaging system provides practical objective

measurements of baked product structure.

• X-ray CT imaging enables the formation of these

structures to be studied with applications for product

development, fault diagnosis and training.

• Hyperspectral NIR imaging is a new technology for

mapping food composition. Applications include

measurement of fat and moisture migration.