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

2

3

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.

4

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

5

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

6

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

7

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

10

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

11

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

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)

Specific functionalized surface

Raman spectroscopy analysis

Identification via spectra recognition

14

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

15

16

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

18

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.