EU-Vietnam Workshop. Safe food for Europe. Hanoi 10-14th March 2014 1 Samira SARTER, Philippe DANIEL CIRAD -UMR Qualisud Institut des Molécules et des Matériaux du Mans IMMM UMR CNRS 6283
Food safety risks 2
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) 3 Thi Thu Hao Van et al. AEM 2007; Truong Ha Thai et al. IJFM 2012; Garin et al. IJFM 2012.
Food safety risks 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. 4
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 5
Standard methods for pathogen identification AFNOR ISO 6579:2002 Identification of Salmonella spp Incubation Identification Time depending on method 25g of sample 2-4 days Many hours Phenotypic methods Pre enrichement Incubation in BPW Immunological methods (ELISA) Molecular methods (PCR) Selective enrichment RVS + MKTTn Biochemical methods Isolement XLD + XLT4 6 Incubation Agar plate
Applications of Raman spectroscopy to bacteria 7
Principles of Raman spectroscopy monochromatic visible radiation : Laser ω 0, λ 0 Interaction with a sample Sir Chandresekhara Venkata RAMAN 1888-1970 Raman effect gives the vibrational signature of any kind of materials Scattered radiations Inelastic process 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00-0.02 600 800 1000 1200 1400 1600 Wavelength (cm-1) 1800 intensity (u.a) Number of publications related to Raman scattering and bacteria 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*
Raman study of bacteria - Investigation of microcolonies and characterisation of heterogeneity - Single-cell analysis of bacteria C B A z coordinate x coordinate L.P. Choo-Smith et al, Applied and environmenetal microbiology, 2001 Pongsit Tangcananurak Work done in the framework of Franco-Thai Program in 2008 9
Raman study of bacteria Interprétation of the spectrum: fingerprint technique Exemple of E-coli Lipids Carbohydrates Proteins Nucleic acids Nombre d onde 507 : Carbohydrate C-O-C 652 : Tyrosine (Acide Aminé) 727 : Adénine (ADN) 872 : Tyrosine (Acide Aminé) 955: Lipides 1037 : Lipides 1240 : amide III 1323 : δ(ch2) 1377 : Symm Stretch (CON-), δ(ch2) 1464 : mono-oligosaccharides 1580 : ADN 1771 : Ester 10
Raman study of bacteria Allow to distinguish between types of bacteria 0,5 to 3 µm Bacteria wall 0 0.2 Bacillus subtilis Staphylococcus Escherichia coli Salmonella Pseudomonas Type de liaison Carbohydrates Acides nucléiques Tyrosine Raman shift cm -1 460 ; 590 770 850 Hétérogénéité 0.4 0.6 Phénylalanine 980 ; 1002 0.8 Amide III Lipides Amide II Amide I Lipides insaturés 1100 1240 1440 1630 ; 1705 1630 ; 1705 Gram + Bacillus subtilis Staphylococcus Streptococcus Gram - Escherichia coli Salmonelle Salmonella Pseudomonas 1 Ward s algorithm Gammes spectrales 400-1800 cm-1 11 Kengne-Momo, R P; Lagarde, F; Daniel, P et al, Biointerphases
densité optique 4,5 4 3,5 3 2,5 2 1,5 1 0,5 0 Raman study of bacteria by Raman spectroscopy vs growth phases croissance de VH en milieu VH à 25 C, 1% Exponential phase Latence phase intensité (u.a) 0 100 200 300 400 500 600 temps (min) Latence phase Exponential phase Stationnary phase Stationnary phase 0,18 0,16 0,14 0,12 0,10 0,08 0,06 0,04 0,02 0,00 Acides nucléiques Phénylalanine Carbohydrates Lipides L. Bendriaa, PhD Thesis, 2005 Amide III Acides nucléiques Lipides Amide II Amide I, Lipides -0,02 600 800 1000 1200 1400 1600 1800 nombre d'onde (cm-1) «Rather easy» distinction between young bacteria and old bacteria Frequency range used for classification: 1450-1750 cm -1
Functionalized surfaces for detection of pathogenic microorganisms 13
Alternative method Biosensor based on a «double check procedure» : (1) Specific capture of microorganisms (2) Recognition by Raman spectroscopy Identification via spectra recognition 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00-0.02 600 800 1000 1200 1400 1600 Wavelength (cm-1) 1800 intensity (u.a) Specific functionalized surface Raman spectroscopy analysis Statistical data analysis Hétérogénéité 0 0.2 0.4 0.6 Bacillus subtilis Staphylococcus Escherichia coli Salmonella Pseudomonas A result of presence/ absence of pathogens in less than 24h 0.8 1 Ward s algorithm 14
Exemple: Gold surface functionalisation with parabenzenesulfonyle chloride Cl Cl O S OO S O Cl Cl O S OO S O O S O O S O Synthesis of specific surfaces of gold with chemical modifications Protein A Antibody ( ) 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00-0.02 600 800 1000 b 1200 d' d ( 1400 1) 1600 1800 Raman detection O S O O S O 15 Quartz crystal microbalance Antibody antigen specific recognition
O S O O S O Prot A (50 mg/l) 2 hours Protein A 441 IgG(1g/l) Antibody Raman characterization F (Hz) 0-250 -500 PrA S-IgG Abitrary Units 3 4 5 539 551 603 638 699 993 1130 1300 1444 1487 PrA + S-IgG on Au 1596 Fluorescence image -750 QCM monitoring -1000 0 500 1000 1500 2000 Time (s) Kengne-Momo, R P ; Daniel, P; Lagarde, F et al International Journal of Spectroscopy Article ID 462901 doi:10.1155/2012/462901 (2012) 0 1 2 483 701 823 1000 1067 1117 1310 1469 1543 400 600 800 1000 1200 1400 1600 1800 2000 Wavenumber (cm-1) PrA on Au 16
Evidence of the last step of the process 50 0 QCM monitoring -50-100 -150-200 -250-300 0 500 1000 1500 2000 2500 Anti-IgG (1,07g/l) 3 Raman characterization 1056 Functionalization procedure also possible on other type of substrate : - Polyethylene traited by plasma - Functionalized Polyurethane - Systems including nanoparticles (magnetic, silver, gold: SERS effect) Abitrary Units 0 1 2 551 630 683 931 992 1122 1310 1446 1590 400 600 800 1000 1200 1400 1600 1800 2000 Wavenumber (cm-1) Raman spectra (785 nm, 10 mw) of Salmonella immobilized on functionalised Au surface
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. 18