Download - Samira SARTER , Philippe DANIEL
Samira SARTER , Philippe DANIEL CIRAD -UMR Qualisud
Institut des Molécules et des Matériaux du Mans IMMM UMR CNRS 6283
1 EU-Vietnam Workshop. Safe food for Europe. Hanoi 10-14th March 2014
Food safety risks
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Salmonella spp. Raw meat sold in market: Porc 39-64%; chicken 42-49-53%; beef 62% Resistance in meat: Porc 50-73% ; Chicken 45% Tetracycline, sulphonamide, steptomycin, ampicillin, chloramphenicol,
trimethoprim, nalidic acid
Multiresistance : 21-56% of isolates 7-9 antibiotics: 15% / 10-13 antibiotics: 8%
Multiresistant Salmonella from food or food-producing animals are common in different countries:
Malaysia 49-75% (n=88) Thailand 44-66% (n=342) Vietnam 21-56% (n=180)
Thi Thu Hao Van et al. AEM 2007; Truong Ha Thai et al. IJFM 2012; Garin et al. IJFM 2012.
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Listeria monocytogenes EU rejections: Filet Pangasius (8 notifications 2010; 17 en 2009)
Campylobacter spp. Chicken sold in market: 15.3% Chicken : 95% of strains are resistant to fluoroquinolones (critical AB)
Escherichia coli : a reservoir Resistance: 84% of isolates of beef, poultry, porc Resistance to fluoroquinolones: 16-21% of isolates, mainly in chicken
samples (52-63%) Multiresistant E. coli (n=99) in raw meat: 89.5% in chicken meat 95% in chicken faeces 75% in pork meat isolates
Garin et al. IJFM 2012; Thi Thu Hao Van et al. IJFM 2012; Truong Ha Thai et al. IJFM 2012; Thi Thu Hao Van et al. AEM 2007; Thi Thu Hao Van et al. IJFM 2008.
Food safety risks
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Food Safety Objectives: "the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the appropriate level of protection (ALOP)".
To ensure that an FSO is met, it is required to set Performance Objectives
which correspond to the levels that must be met at earlier steps in the food chain before consumption.
FSOs and POs must be achievable by the application of good practices
(GAP, GHP, GMP) and HACCP Microbiological Criteria can be used to define the microbiological quality
of raw materials, food ingredients, and end-products at any stage in the food chain.
Need for accurate, rapid and sensitive methods for detection and quantification of microbial hazards
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Standard methods for pathogen identification AFNOR ISO 6579:2002
Identification of Salmonella spp
Phenotypic methods
Immunological methods (ELISA)
Molecular methods
(PCR)
Biochemical methods
Identification Time depending on method
25g of sample
Isolement XLD + XLT4
Incubation
Pre enrichement Incubation in BPW
Selective enrichment RVS + MKTTn
2 - 4 days Many hours
Incubation Agar plate
Applications of Raman
spectroscopy to bacteria
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Principles of Raman spectroscopy
Scattered radiations
Interaction with a sample monochromatic visible radiation : Laser ω0, λ0
Inelastic process
Sir Chandresekhara Venkata RAMAN
1888-1970
Raman effect gives the vibrational signature of any kind of materials
600 800 1000 1200 1400 1600 1800 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18
inte
nsity
(u.a
)
Wavelength (cm-1)
Advantages of the technics: - Fingerprint technics - No preparation of the sample - Non invasive technics - Non destructive technics - Qualitative or quantitative
Source : ISI Web of Science – January 2014 – Key words: Raman, bacter*
Number of publications related to Raman scattering and bacteria
- Single-cell analysis of bacteria
Raman study of bacteria
9 Pongsit Tangcananurak Work done in the framework of Franco-Thai Program in 2008
- Investigation of microcolonies and characterisation of heterogeneity
L.P. Choo-Smith et al, Applied and environmenetal microbiology, 2001
z coordinate
x coordinate
A B C
Interprétation of the spectrum: fingerprint technique
Nucleic acids
Proteins
Carbohydrates
Lipids
Raman study of bacteria
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507 : Carbohydrate C-O-C
652 : Tyrosine (Acide Aminé) 727 : Adénine (ADN)
872 : Tyrosine (Acide Aminé)
1037 : Lipides 955: Lipides
1240 : amide III 1323 : δ(CH2) 1377 : Symm Stretch (CON-), δ(CH2) 1464 : mono-oligosaccharides
1580 : ADN
1771 : Ester
Nom
bre d’onde
Exemple of E-coli
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0,5 to 3 µm
Allow to distinguish between types of bacteria
Salmonelle Staphylococcus
Pseudomonas Streptococcus
Escherichia coli Bacillus subtilis
Gram - Gram +
Salmonella Staphylococcus
Pseudomonas Streptococcus
Escherichia coli Bacillus subtilis
Gram - Gram +
Bacteria wall
Bac
illus
sub
tilis
Stap
hylo
cocc
us
Esch
eric
hia
coli
Pseu
dom
onas
Salm
onel
la
Hét
érog
énéi
té
0
0.2
0.4
0.6
0.8
1
Ward’s algorithm Gammes spectrales 400-1800 cm-1
Kengne-Momo, R P; Lagarde, F; Daniel, P et al, Biointerphases –
Raman shift cm-1Type de liaison
1630 ; 1705Lipides insaturés
1630 ; 1705Amide I
1440Amide II
1240Lipides
1100Amide III
980 ; 1002Phénylalanine
850Tyrosine
770Acides nucléiques
460 ; 590Carbohydrates
Raman shift cm-1Type de liaison
1630 ; 1705Lipides insaturés
1630 ; 1705Amide I
1440Amide II
1240Lipides
1100Amide III
980 ; 1002Phénylalanine
850Tyrosine
770Acides nucléiques
460 ; 590Carbohydrates
Raman study of bacteria
600 800 1000 1200 1400 1600 1800-0,02
0,00
0,02
0,04
0,06
0,08
0,10
0,12
0,14
0,16
0,18
inten
sité (
u.a)
nombre d'onde (cm-1)
Latence phase Exponential phase Stationnary phase
Aci
des
nucl
éiqu
es
Phé
nyla
lani
ne
Lipi
des
Car
bohy
drat
es
Am
ide
III
Aci
des
nucl
éiqu
es
Lipi
des
Am
ide
II
Am
ide
I, Li
pide
s
croissance de VH en milieu VH à 25°C, 1%
00,5
11,5
22,5
33,5
44,5
0 100 200 300 400 500 600
temps (min)
dens
ité o
ptiq
ue Latence phase Exponential phase
Stationnary
phase
Raman study of bacteria by Raman spectroscopy vs growth phases
L. Bendriaa, PhD Thesis , 2005
Frequency range used for classification: 1450-1750 cm-1
« Rather easy» distinction between young bacteria and old bacteria
Functionalized surfaces for detection of pathogenic
microorganisms
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Alternative method Biosensor based on a « double check procedure » :
(1) Specific capture of microorganisms (2) Recognition by Raman spectroscopy
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inte
nsity
(u.a
)
Wavelength (cm-1)
Specific functionalized surface
Raman spectroscopy analysis
Identification via spectra recognition
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Bac
illus
sub
tilis
Stap
hylo
cocc
us
Esch
eric
hia
coli
Pseu
dom
onas
Salm
onel
la
Hét
érog
énéi
té
0
0.2
0.4
0.6
0.8
1
Ward’s algorithm
Statistical data analysis
A result of presence/ absence of pathogens in less than 24h
600 800 1000 1200 1400 1600 1800-0.020.000.020.040.060.080.100.120.140.160.18
()
b d' d ( 1)
600 800 1000 1200 1400 1600 1800-0.020.000.020.040.060.080.100.120.140.160.18
()
b d' d ( 1)
600 800 1000 1200 1400 1600 1800-0.020.000.020.040.060.080.100.120.140.160.18
()
b d' d ( 1)600 800 1000 1200 1400 1600 1800-0.02
0.000.020.040.060.080.100.120.140.160.18
()
b d' d ( 1)
Raman
Quartz crystal microbalance
detection
Exemple: Gold surface functionalisation with parabenzenesulfonyle chloride
SO O SO O
SO O SO O
SO O SO O
Cl Cl SO O SO O
Cl Cl
Synthesis of specific surfaces of gold with chemical modifications Protein A Antibody
Antibody – antigen specific recognition
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QCM monitoring
Raman characterization
IgG(1g/l) Prot A (50 mg/l) 2 hours
SO O SO O
Protein A Antibody
-1000
-750
-500
-250
0
0 500 1000 1500 2000Time (s)
F (H
z)
PrA
S-IgG
1596
1543
1469
1310
111710
67
1000
823
701
638
551
483
01
2A
bitra
ryU
nits
34
5
400 600 800 1000 1200 1400 1600 1800 2000
Wavenumber (cm-1)
PrAon Au
1487
1444
1300
1130
993
699
603
539
441
PrA + S-IgGon Au
1596
1543
1469
1310
111710
67
1000
823
701
638
551
483
01
2A
bitra
ryU
nits
34
5
400 600 800 1000 1200 1400 1600 1800 2000
Wavenumber (cm-1)
PrAon Au
1487
1444
1300
1130
993
699
603
539
441
PrA + S-IgGon Au
Fluorescence image
Kengne-Momo, R P ; Daniel, P; Lagarde, F et al International Journal of Spectroscopy Article ID 462901 doi:10.1155/2012/462901 (2012)
QCM monitoring
Raman characterization
0 500 1000 1500 2000 2500-300
-250
-200
-150
-100
-50
0
50
Anti-IgG (1,07g/l)
Functionalization procedure also possible on other type of substrate : - Polyethylene traited by plasma - Functionalized Polyurethane - Systems including nanoparticles
(magnetic, silver, gold: SERS effect) 01
2A
bitra
ryU
nits
3
400 600 800 1000 1200 1400 1600 1800 2000
Wavenumber (cm-1)
1590
1446
1310
1122
1056
992
931
683
63055
1
01
2A
bitra
ryU
nits
3
400 600 800 1000 1200 1400 1600 1800 2000
Wavenumber (cm-1)
1590
1446
1310
1122
1056
992
931
683
63055
1
Raman spectra (785 nm, 10 mW) of Salmonella immobilized on functionalised Au surface
Evidence of the last step of the process
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Develop a detection kit based on Raman spectroscopy for specific pathogens in food (model and food matrix)
Target specific resistant bacteria, and try to explore the mechanisms of
actions (critical antibiotics)
Screening of resistant strains along the food chain/environment Research at the interface between physics and chemistry of materials
Institute for Molecules and Materials of Le Mans Department of solid state physics: - Physics of advanced materials, Nanomaterials, Surface
functionalization - Multiscale and multitime elaboration and characterization
technics. - Modeling and simulation.