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MBM detection
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by NIRM andNIR hyperspectral imaging
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eNIR hyperspectral imagingJ A Fernández Pierna O Abbas P Dardenne V Baeten
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eJ. A. Fernández Pierna, O. Abbas, P. Dardenne, V. Baeten
Walloon Agricultural Research Centre (CRA-W), Quality of Agricultural Products Department, Chaussée de Namur n°24, 5030 Gembloux, Belgium
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WP 4
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List of NIR markers
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Validation of NIR microscop protocol
NIR-microscopy protocol for quantitative analysis
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ulValidation of NIR-microscopy protocol
Transfer to the NIR Hyperspectral imaging
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Proposal for NIR-PCR combination methods
Transfer to the NIR Hyperspectral imaging
Background
Official method: Optical microscopy
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BUT• Reproducibility :
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– = IDENTIFICATION SKILLS OF THE MICROSCOPIST
USE OF NIRM TO IMPROVE THE REPRODUCTIBILITY
Background
Alternative way
OM method
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OM method
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Fish bones
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Replacement of the eyes of the analyst by infraredReplacement of the eyes of the analyst by infrared detectors and the expertise of the microscopist by
discriminant equations
Background
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Classical microscopy is based on the visual observation of morphologic features of
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eobservation of morphologic features of ingredient particles
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Multispectral and hyperspectral infrared spectroscopy methods are based on the organic
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NIR Microscopy
MicroscopeMicroscope
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Combination of the advantages of
MicroscopeMicroscope
FTFT--NIRNIR
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egmicroscopy and « macro » NIRS
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e« macro » NIRS
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oSample particles
Spectral Signature
Each species has a proper spectrum
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NIR i i b d th b ti f thNIR microscopy is based on the absorption of the infrared light by ingredient particles
Raw material used for data bank construction
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ANIMAL MEALS
Bl d l 3
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eBlood meal 3Feather meal 5Meat and bone meal 37
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eMeat and bone meal 37Poultry by-product 12Fish meal 29
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86 l 30 t 2580 t
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Raw material used for data bank construction
VEGETAL INGREDIENTS & MEALS
entreCereals 103
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e(oats, wheat, corn, barley, rice)Protein sources 55(rape seed bean peas soya bean)
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e(rape seed, bean, peas, soya bean)Tropical by-products 31(peanuts, cocoa, coconut, manioc, palm kernel)
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(p , , , , p )Other vegetal meals 61(sugar beet by-products, bakery by-products, chicory,b h l b d fl )
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loobot hop, lucerne, potatoes by-products, sunflowers)
250 samples x 30 spectra = 7500 spectra250 samples x 30 spectra = 7500 spectra
Animal Data Base
1.2
1.4
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BLOOD
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0.8
1 (7
9.83
%)
on P
C1
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0.4
0.6
core
s on
PC
Sc
ores
o
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0.2
Sc
BEEF PIG CHICKEN SHEEP FISH
S
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0 2
0
0 100 200 300 400 500 600 700 800 900-0.2
SampleSample
Chemometrics and NIRM data
UNSUPERVISED
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•PCA
CA SUPERVISED
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e•CA… SUPERVISED
•PLS-DA
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•ANN
S C
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•SVM… Different mathematical models
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to discriminate animal from t l f d ti l dvegetal feed particles and
between animal species.
Discrimination models: PLS-DA
Eq. 1: DISCRIMINATION VEGETAL (= +1) vs ANIMAL (= -1) PARTICLES
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3
4
VEGETAL GROUP
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1
2
VALU
ES
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-1
00 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
ICTI
ON
V
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-2
1
PRED
ANIMAL GROUP
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-4
-3
PARTICLE NUMBER= CLASSIFIED PARTICLES
PARTICLE NUMBER= UNCLASSIFIED PARTICLES
Discrimination models: PLS-DA
CAL LOOCV
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Belonging to...% classified as... % classified as...
Fish Rest Fish Rest
Fish 98.6 1.4 96.8 3.2
R 9 5 90 5 10 90
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eRest 9.5 90.5 10 90
CAL LOOCV
% classified as % classified as
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Belonging to...% classified as... % classified as...
Pig Rest Pig Rest
Pig 90.5 9.5 88.1 11.9
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ulRest 15.9 84.1 18.9 81.1
CAL LOOCV
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Belonging to...% classified as... % classified as...
Beef Rest Beef Rest
Beef 76.7 23.3 75.6 24.4
Rest 15 85 16.2 83.8
Transfer of the NIRM method
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??CRA-W, Belgium
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??
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JRC, Italy & JRC, Belgium
Transfer of the NIRM method
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Transfer of the NIRM method
Spectral conditions to be fulfilled by a spectrum to be from animal origin particle
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0 6
0,8
R) a c e
Presence of maxima
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0,4
0,6
ance
(Log
1/R
b d f
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0,2Abs
orb
CerealAnimalSoymeal
Presence of minima[(abs. b + abs. f )/2] > abs. d
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1700 1800 1900 2000 2100 2200 2300 2400 2500Wavelength (nm)
) 1920 1960
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b) 2010-2030 nmc) 2030-2070 nmd) 2070-2150 nm Based on VISUAL observation)e) 2150-2200 nmf) 2210-2250 nm
Transfer of the NIRM method
Method transferred to the IRMM-JRC
entreNow, Interlaboratory study to transfer the method to
other laboratories as the College of Engineering in
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eother laboratories as the College of Engineering inChina Agricultural University (Prof. Han Lujia)
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Combination of multispectral techniques and PCR
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NIRM analysis and selection of suspicious animal particles
PCR analysis of suspicious animal particles
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Confirmation and
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uldetermination of the animal species origin
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of the particles by Single particle PCR results of analysis
p yPCR FARIMAL
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NIR hyperspectral imaging
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Large particles Small particles
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eLarge particles (fruits, grains,…)
Small particles (sedimented feed
fractions,…)
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NIR camera – Malvern Instruments Ltd
Spectral hypercube
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els e
n y)
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mbr
e de
pix
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( b d i l )
(nom
(Wavelength nm)
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- 240 x 320 pixels
(nombre de pixels en x)
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- 24 MB/cubepixel = spectrum
- 5 to 8 minutes
- 900-1700/10 nm
pixel spectrum
NIR hyperspectral imaging
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MBM detection: procedure
Support Vector Machines (SVM)
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21imisemin
n
1i i2
⎭⎬⎫
⎩⎨⎧ + ∑ =
ξ
+
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i,1)bx,w(ytosubject iii ∀−≥+>< ξ
⎟⎞
⎜⎛ SV
+
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⎟⎟⎠
⎞⎜⎜⎝
⎛+= ∑
=
b)x,x(kysign)x(f i1i
iiα
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DATABASE construction
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Prediction of new data
MBM detection: database
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OTHER
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VEG Poul- Bovine
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eVEG try + Pig
T t i l i lT t i l i l
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FISH
Terrestrial animalTerrestrial animal
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OTHER
MBM detection: prediction
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In Techniques de l'Ingénieur, 3 (2005) RE 34 – 1-8
Detection of fish meal
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eDiscriminant models
Animal vs. Vegetal
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egFish vs. Terrestrial animal
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Distribution of the fish particles
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Increasing the speed
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Conclusion
Analysis of raw and sedimented fraction
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y f f(LOD ~= 0.1%)
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High repeatability
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No expertise needed
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ulScreening method
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Non-destructive method
Conclusion
Indication of the species presence in the
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Indication of the species presence in the adulterated samples
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Transferability
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Methods not validated by collaborative studies …
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In the case of the NIRM Interlaboratory studyorganized by JRC-IRMM during 2009 in the
Wal
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framework of SAFEED-PAP
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Thank you for your attention
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