Online spectral imaging applied to food process control · 2010-10-07 · – NIR, VIS, spectral...

Online spectral imaging applied to food process control

Jens Petter WoldNorwegian Food Research Institute

IFPAC Cortona 2010

08.10.2008 Online Measurements of Quality - Siena 2

Background: Nofima• Peak competence in applied

biospectroscopy for food analysis

• On-line– NIR, VIS, spectral imaging

• At-line– Fluorescence, VIS, NIR,

Raman, FT-IR, spectral imaging

• Microscopy of tissues and cells– Raman, FT-IR

• Chemometrics, multivariate calibration

• Food quality, food safety, process optimization

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Food industry today: Under pressure!

• Consumers demand low prices and high quality• Increasing focus on nutrition and health• Increasing demands for product documentation and traceability • Food production is more and more industrialised

• Strong need for advanced quality measurements for process and product control

• The ideal measurements is– rapid and preferably on-line • non-destructive– accurate • robust– etc.


Rapid spectroscopic techniques:An effective tool within food process control

• Enables efficient monitoring and control of complex products andprocesses

• multispectral images (chemical imaging) improves sampling and precision

• Genotype ↔ Phenotype measured by e.g. Raman, NIR, FT-IR• By-products based on novel bioprocesses

Food

waste

Bio-Process

Valueadded bio-product

Measurem

ents


Challenges with non-destructive measurements

• Foods/ are very complex from a measurement point of view!• Large variations in chemical composition, texture, shape and size• Main challenge: Representative sampling!• Often need to characterise every single product in the production

• Different products needs different solutions: Tailor made systems


Examples of analytical challengesBacalao

Salmon

Beef Crabs


NIR Reflection: Measures the surface

Detector


Interactance: Forces light into product

Spec.


Instrumental solution (patented):Scanning interactance measurement

Conveyor

12 x 50 W, 12°, halogen lamps

Cylinder optic

Adjustable slit

Illuminated field Scanned field

Imaging spectrometer

Focusing Al mirror

BlackenedAl plates


NIR/VIS interactance imaging scanner

• Developed by Nofima, Sintef and QVision AS• Produces a 2D multispectral image of the conveyor belt• 15 wavelengths in VIS/ 15 in NIR• Handles a conveyor belt speed of 3 m/s• Does about 10.000 measurements/sec


NIR image of salmon fillet: An image for each wavelength / a spectrum in each pixel

• Water• Fat• Protein• Temperature


Commercial implementation


Quantitative chemical imaging• Every application requires careful consideration of

– Sampling– Calibration regime (how to match spectroscopy and

reference values)– Image segmentation, image processing– Spectral pre-processing at pixel level

• to avoid effects of variation in sample height, temperature, colour, etc.

– Multivariate modelling (regression, curve resolution)– How to apply model on new data


Application: Water content in clipfish

Challenges:• Water is unevenly distributed• Dry on the outside, wet inside• Covered with salt• Varying size and shape

Alternatives:• Manual grading is expensive

and inaccurate• Lab measurements of water

are tedious and destructive


Chemical imaging: Water content in each pixel

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55

% w

ater

37.0 41.7 44.7Average water content (%)


On-line predicted water content in whole clipfish

R=0.97

Pred. error =±0.65 %

Meas

50

46

44

42

40

38

36

Pre

dict

ed w

ater

con t

ent [

%]

36 38 40 42 44 46 50 % water


A shift in paradigm:

From random sampling10 out of 2000

Full profiling ofeach product


Industrial installation• Sorting according to water content• Producer gets correct price• Avoids reclamations• Enables optimization of drying process


Chemical images of salmon fillets: Fat content in each pixel

Fisk: 20 FettFisk: 19.8969% Share: 23.6285

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50Fisk: 16 FettFisk: 17.2034% Share: 20.2078

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17.2 % 19.9 %

• Calculates average fat content• Fat distribution guides

– Automatic trimming/cutting– Selection of phenotypes for breeding (genetic selection)

17.2% 21.4%


Measuring fat and pigment in whole/live fish

• To be used within breeding and genetics

• Continuous evaluation of feeding regimes

• Measurements in production:– sorting to different

retail– different markets– product labelling


Effective breeding and selection• Measurement of fat and pigment in

4500 live salmon• Heritability factor for fat/pigment can

be calculated• Selection of the best families for

production• Saves one generation of fish + a lot of

costly wet chemistry

0,0

5,0

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15,0

20,0

25,0

30,0

1 500 999 1498 1997 2496 2995 3494 3993 4492

Estimated fat%Estimated fat%Fa

t %

Salmon no.


• 40 tons per day• Manual grading is difficult• Capacity need: 2 crabs per sec.• Need to optimize production

line

Industrial system to separate between full or empty crabs


Four qualities:

1. Superior quality– Much liver and roe– Boiled whole, sent to France/Italy

2. Acceptable quality – Well filled– Boiled whole, distributed in Norway

3. Little food– Shell opened, liver is taken out and boiled separately– Used for different crab products

4. Empty crab– Claws are used, rest of crab is waste

• 3-4 processing lines• Problem: Very difficult to grade!

08.10.2008 Online Measurements of Quality - Siena 2407.10.2010 24

Interactance: Forces light into the crab

Spec.


How and what do we measure?

• Crabs are scanned on-line on a conveyor with the shell up and exposed to the scanner

• The crab is measured from above• Mainly the upper 15 mm is probed • Multispectral NIR images are captured• 15 NIR channels in each image

A B


Determination of food content in crabs: The meat quality index

07.10.2010 26

Meat Q Index, MQI:

(L+R)*100MQI =

(W/10)^2

• L = liver content• R = Roe content• W = Width of the

crab shell



1. Segmentation:

1 0 2 0 3 0

1 0

2 0

3 0

4 0

1 0 2 0 3 0

1 0

2 0

3 0

4 0

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Raw image Segmented image Region for spectral extraction

1 0 2 0 3 0

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1 0 2 0 3 0

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2. Extraction of NIR spectra:

Little food(much water)Much food


3. Calibration model for food index


Possible challenges

• Seasonal variations– Food mass vary in

composition (roe+liver)

• Can obtain a good estimate of the amount of both liver and roe separately

JuneSeptember

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11 15 18 21 22 25 13 19 24 31 34 41

June September

MQI

% R

oe o

fmea

twei

ght

% roe in meat mass


NIR scanner at HitraMat, Norway


Practical results• The crabs are sorted on-line into 3 - 4 quality classes, 1-2 crabs /sec.• Quicker and much more reliable than manual grading• Yield in process has increased, less waste• Can guarantee high quality of superior crabs, which is extremely

important to keep the crab market alive• Systemized data gives overview of seasonal and regional variations • Will be used to adjust payments to the fisherman


Next step: System for the fishermen

07.10.2010 34

It is possible to do similar measurements with simpler system more suited for e.g. boatsEvery crab can then be measured, and only the medium and full crabs can be collected.Empty crabs can be returned to the sea:

High quality capturesSustainable harvestingSatisfied customers


Beef processing• 60% of the carcass ends up as beef trimmings

– for meat products• Batches of beef trimmings are priced according

to fat content– Low fat gives higher price– Batch sizes vary from 20 – 400 kg– Very important for the company

to optimize in order to make profit• No good way to measure fat content in intact trimmings

– The cutters try to reach target fat content, but difficult• Fat can be measured in ground meat, but most customers prefer intact

trimmings• Reliable measurements on trimmings would be very valuable for

– Getting the correct price– Optimised use of raw-materials– Optimised logistics


NIR-spectra from beef

63 % fat

3% fat30% fat


• Trimmings are heterogeneous!• Vary in type of meat/muscle, colour, structure, size,

shape


Calibration

• Need to record NIR spectra from meat samples that span fat content from 2 – 90%

• Under different conditions• “Big pixel” strategy: need to

calibrate for every situation a pixel can encounter


Calibration strategy

– The model can be used on average spectra from meat trimmings

– And pixel wise in the multispectral images (to show fat distribution in single trimmings)

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Wavelength (nm)

spectra

=fa

t val

ues

Spectral image

Prediction model


Applying model on single trimmings

• Large prediction errors, especially on fat samples• As expected…

NMR measured fat (%)

NIR

estimated

fat (%)

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800

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Prediction error vs. batch size• Prediction error decreases rapidly with increasing batch size• Depends on fat content of trimmings / heterogeneity

Lean trimmings (<30%)

Fat trimmings (> 8%)

All trimmings0 20 40 60 80 100

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1

1.5

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3.5

Batch size (kg)

RM

SE

P (%

)P

redi

ctio

ner

ror%


On-line estimation of fat in batch

• Gives good opportunity to control batch against desired fat content

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Accumulated weight (kg)

Fat (

%)

Fat

Accumulated average fat


9.6 % 16.8% 27.9 %

Flow weight

Laser height measure


Installation in Norwegian beef cutting line

• Average fat content in batches of intact trimmings is continuously monitored and controlled

• Cutters can adjust the amount of fat going into the batch

• Much better control of end product quality

• Better utilization of raw materials

• More motivating for the workers


Automatic detection of connective tissue

• Can detect surface connective tissue (CT)

• Can be used to produce batches of different qualities

Sample: CT3BSample: CT3B

Fat prediction: 3.5 % mean fat

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Sample: CT3BConnective tissue

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Sample: CT2BSample: CT2B

Fat prediction: 25.2 % mean fat

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Sample: CT2BConnective tissue

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Fat image CT image


Summary meat1. From manual, subjective sorting: imprecise fat levels, difficult to control2. To measurement on intact beef trimmings, which enables simple control of the

cutting line (implemented today)3. To automatic sorting of intact trimmings into batches of pre-defined fat content.

14%

21%+ CT

18%

26%

Automatic fat and CT determination

scanner

18.3% scan

ner

Automatic sorting into batches of specified quality

Today Next year

5%

5%

14 % & 21 % requires grinding and standardization

14 % / 21%

26%

Fat

Cutters

Yesterday


Summary• Imaging will replace spot sampling for most heterogeneous discrete

samples• Distributional information through NIR imaging is and will be beneficial in

process optimisation

• The success of an application relies on adequate setup for spectral sampling and reference sampling (which needs careful consideration!)

• New technology is sophisticated, while competence in the food industry is limited (challenge!)

• New technology requires changes in traditional processes and craftsmanship (challenge)

• New technology is adapted only when it increases profit notably– Only when “need to have”, never when only “nice to have”


Acknowledgements• Nofima

– Martin Høy– Vegard Segtnan

• Sintef ICT– Jon Tschudi– Marion O’Farrel

• QVision– Martin Kermit– Geir Hauge

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Online spectral imaging applied to food process control · 2010-10-07 · – NIR, VIS, spectral...

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