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)
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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.
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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
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 int ensi ty (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*
- Single-cell analysis of bacteria
Raman study of bacteria
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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
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 No m br e 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 B aci ll us subt il is S taphyl ococcus E scher ichi a col i P seudom onas S al m onel la H ét ér ogénéi 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-1 Type de liaison 1630 ; 1705 Lipides insaturés 1630 ; 1705 Amide I 1440 Amide II 1240 Lipides 1100 Amide III 980 ; 1002 Phénylalanine 850 Tyrosine 770 Acides nucléiques 460 ; 590 Carbohydrates Raman shift cm-1 Type de liaison 1630 ; 1705 Lipides insaturés 1630 ; 1705 Amide I 1440 Amide II 1240 Lipides 1100 Amide III 980 ; 1002 Phénylalanine 850 Tyrosine 770 Acides nucléiques 460 ; 590 Carbohydrates
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 in ten si té ( u. a) nombre d'onde (cm-1) Latence phase Exponential phase Stationnary phase A c ides nuc léi q ues P hény lal a ni n e Li pi des C ar bohy dr at e s A m id e III A c ides nuc léi q ues Li pi des A m ide I I A m ide I , Li pi des croissance de VH en milieu VH à 25°C, 1% 0 0,5 1 1,5 2 2,5 3 3,5 4 4,5 0 100 200 300 400 500 600 temps (min) de ns it é opt
ique 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
Alternative method
Biosensor based on a « double check procedure » : (1) Specific capture of microorganisms
(2) Recognition by Raman spectroscopy
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 int ensi ty (u. a) Wavelength (cm-1) Specific functionalized surface Raman spectroscopy analysis Identification via spectra recognition 14 B aci ll us subt il is S taphyl ococcus E scher ichi a col i P seudom onas S al m onel la H ét ér ogénéi té 0 0.2 0.4 0.6 0.8 1 Ward’s algorithm Statistical data analysis
600 800 1000 1200 1400 1600 1800 -0.020.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 () b d' d ( 1) 600 800 1000 1200 1400 1600 1800 -0.020.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 () b d' d ( 1) 600 800 1000 1200 1400 1600 1800 -0.020.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 () b d' d ( 1) 600 800 1000 1200 1400 1600 1800 -0.020.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 () b d' d ( 1) Raman
Quartz crystal microbalance
detection
Exemple: Gold surface functionalisation with parabenzenesulfonyle chloride
S O O O S O S O O O S O S O OO S O Cl Cl S O OO S O Cl Cl
Synthesis of specific surfaces of gold
with chemical modifications Protein A Antibody
Antibody – antigen specific recognition
16 QCM monitoring Raman characterization IgG(1g/l) Prot A (50 mg/l) 2 hours S O O O S O Protein A Antibody -1000 -750 -500 -250 0 0 500 1000 1500 2000 Time (s) F (H z ) PrA S-IgG 15 96 15 43 14 69 13 10 11 17 10 67 10 00 82 3 70 1 63 8 55 1 48 3 0 1 2 A bi tr ar y Un it s 3 4 5 400 600 800 1000 1200 1400 1600 1800 2000 Wavenumber (cm-1) PrA on Au 14 87 14 44 13 00 11 30 99 3 69 9 60 3 53 9 44 1 PrA + S-IgG on Au 15 96 15 43 14 69 13 10 11 17 10 67 10 00 82 3 70 1 63 8 55 1 48 3 0 1 2 A bi tr ar y Un it s 3 4 5 400 600 800 1000 1200 1400 1600 1800 2000 Wavenumber (cm-1) PrA on Au 14 87 14 44 13 00 11 30 99 3 69 9 60 3 53 9 44 1 PrA + S-IgG on Au Fluorescence image
Kengne-Momo, R P ; Daniel, P; Lagarde, F et al International Journal of Spectroscopy
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) 0
1 2 A bi tr ar y Un it s 3 400 600 800 1000 1200 1400 1600 1800 2000 Wavenumber (cm-1) 15 90 14 46 13 10 11 22 10 56 99 2 93 1 68 3 63 0 55 1 0 1 2 A bi tr ar y Un it s 3 400 600 800 1000 1200 1400 1600 1800 2000 Wavenumber (cm-1) 15 90 14 46 13 10 11 22 10 56 99 2 93 1 68 3 63 0 55 1 Raman spectra (785 nm, 10 mW) of Salmonella immobilized on functionalised Au surface
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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.
