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Sensors for Food Quality Safety: Detection and
Characterization
Sensors for Food Quality Safety: Detection and
Characterization
Joseph Irudayaraj, Associate ProfessorAgricultural and Biological Engineering
Purdue University
Joseph Irudayaraj, Associate ProfessorAgricultural and Biological Engineering
Purdue University
Key CollaboratorsKey CollaboratorsPenn State
Food Science: Steve Knabel, Richard Apenten, John Coupland, Koushik Seetharaman, Bob RobertsVet Sci: Chobi Debroy, Ali Demirci, Bhushan JayaraoChem/Materials: David Allara, Carlo Pantano
Purdue UniversityBindley Biosciences CenterDepts.: ABE, Food Sciences, Vet Sciences, ChemistryMike Ladisch, Cris Staiger, Garth Simpson, Sophie Liverve, Peixuan Guo, Kinam Park
IUPUI Cancer Center (Drs. Nakshatri, Sledge, MD)Mayo Clinic (Robert Jenkins, MD)
Penn StateFood Science: Steve Knabel, Richard Apenten, John Coupland, Koushik Seetharaman, Bob RobertsVet Sci: Chobi Debroy, Ali Demirci, Bhushan JayaraoChem/Materials: David Allara, Carlo Pantano
Purdue UniversityBindley Biosciences CenterDepts.: ABE, Food Sciences, Vet Sciences, ChemistryMike Ladisch, Cris Staiger, Garth Simpson, Sophie Liverve, Peixuan Guo, Kinam Park
IUPUI Cancer Center (Drs. Nakshatri, Sledge, MD)Mayo Clinic (Robert Jenkins, MD)
Several Others ….
Assessment of Food Quality and Safety Parameters
Assessment of Food Quality and Safety Parameters
Handled
Processed
Stored
Packaged
More than 90% of foodborne illnesses areattributed to bacteria and 6 of these cause over 50% of the illnesses
More than 90% of foodborne illnesses areattributed to bacteria and 6 of these cause over 50% of the illnesses
DoseDeathCasesBacteria
101 to 102400725,000E. coli O157:H7
104 to 1074,0003.8 * 106Salmonella
> 10810010,000C. perfringens
> 10612101.0 * 106S. aureus
400 t0 1034851,767L. monocytogenes
400 to 1065114.0 * 106C. jejuni
2
Non-Contact Ultrasound ImagingNon-Contact Ultrasound Imaging
DetectionFragments: minimum 3×3 mm2
Cylindrical objects: 1.5 mm in diameter
Cracks and sporadic porosities
DetectionFragments: minimum 3×3 mm2
Cylindrical objects: 1.5 mm in diameter
Cracks and sporadic porosities
Foreign object & internal disorder detection in cheese using Non-contact ultrasound imaging
Cho et al. (2000-2004)
Attenuation coefficient measurement
Attenuation coefficient measurement
Attenuation coefficient
= (IR1 – IR2) / (2*thickness)
Attenuation coefficient
= (IR1 – IR2) / (2*thickness)
Reflection
Dm
Air-coupled transducer 1
Air-coupled transducer 2
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
100 150 200 250
time (usec)
rela
tive
ampl
itude
(dB)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
100 150 200 250
time (usec)
rela
tive
ampl
itude
(dB) IR1
IR2
NCU Imaging systemNCU Imaging system
To obtain NCU imageTo obtain NCU imageNon-contact air instability compensation transducer
Sample
X-Y position system
X
Y
NCU unit
Image processingImage processing
velocity relative attenuation
raw
processed
Glass fragment
3
Foreign object & internal disorderForeign object & internal disordermetal (5×3mm) glass (3×3mm) hole (d 4mm)
sporadic porosities
4.3
4.4
4.5
4.6
4.7
4.8
4.9
5
5.1
5.2
0 5 10 15 20
0
5
10
15
20
25
X Lateral distance [mm]
Y L
ater
al d
ista
nce
[mm]
Cheese Glass fragments in chicken breast
(Noncontact ultrasound image)
Food Quality – Non contact Food Quality – Non contact
ZnoseHoney authenticityHoney adulterationWine classificationApple quality
ZnoseHoney authenticityHoney adulterationWine classificationApple quality
λ
T
PiezoelectricsubstrateMetal
ITD
L
20 SECONDS SAMPLING TIME
895000
896000
897000
898000
899000
900000
901000
902000
903000
904000
905000
906000
0 5 10 15 20 25
TIME(SECONDS)
FREQ
UEN
CY(
HZ)
Enose – abundant Literature
FTIR
PAS
Mirror
SampleMicrophone
KBr window
IR beam
Computer
IR spectrum
Diagram of FTIR-PASDiagram of FTIR-PAS
100 Hz 10 Hz
α = 0.001cm2/s
900 Hz
56 μm
18 μm6 μm
Depth
Phase modulation frequencies
4
3300
2929
1655 15
5514
5814
0713
2012
4211
1510
5692
685
5
3381
2931
1652
1559
1458
1420 11
54 1054
930
857 76
4
2959 29
23
1376
1162
996 97
3
839
719
4000 3500 3000 2500 2000 1500 1000 500
2843
1646
1457
Phot
oaco
ustic
Inte
nsity
Wavenumber (cm-1)
Starch
Protein
Polyethylen
Rapid-scan FTIR-PAS spectra of protein, starch, and polyethylene
Amide IIAmide I
857930
1457 1375Red color indicate polyethylene is above protein and starch
Asynchronous G2D correlation FTIR-PAS spectra of three-layer polyethylene/starch/protein
Fourier transform Raman spectroscopyFourier transform Raman spectroscopy
A near-infrared (1064 nm) laser is used as a probed beam to overcome the fluorescence effectThere is no sample preparationRaman spectrum is complementary to infrared spectrum
A near-infrared (1064 nm) laser is used as a probed beam to overcome the fluorescence effectThere is no sample preparationRaman spectrum is complementary to infrared spectrum
Direct determination of microorganisms on Food and package
Materials & Methods
Material: fungi (Aspergillus niger andFusarium verticilliodes), yeast (Saccharomyces cerevisiae), bacteria (Bacillus cereus, Lactobacillus casei, and E.coli (HB101, DH5α, JM107, JM101, K12, O157:H7)), and apple
Instrumentation: FTIR-PAS and FT-Raman
Data Analysis: Canonical variate analysis
5
FT-Raman spectra of uncontaminated apple and contaminated apple surfaces with different types of microorganisms
FT-Raman spectra of uncontaminated apple and contaminated apple surfaces with different types of microorganisms
Ram
an In
tens
ity
4000 3500 3000 2500 2000 1500 1000 500
Wavenumber (cm-1)
3379
2931
1658
1458 1304 1088 856
E.coli HB101
Aspergillus niger
Fusarium verticilliodes
Saccharomyces cerevisiae
Bacillus cereus
Lactobacillus casei
Apple skin
Discriminant canonical variate analysis based on the first two canonical variates from the spectra of whole apple
surface with/without microorganisms
Discriminant canonical variate analysis based on the first two canonical variates from the spectra of whole apple
surface with/without microorganisms
-6
-4
-2
0
2
4
6
-6 -4 -2 0 2 4 6 8 10
CV1
CV
2
Aspergillus nigerBacillus cereusE.coli HB101Fusaruim verticillioidesLactobacillus caseiSaccharomyces cerevisiaeApple skin
Bacteria 1. E. coli O157:H72. Salmonella Enteritidis3. Bacillus cereus4. Yersinia enterocolitis5. Shigella boydii
3 out of 5 bacteria were used to make 10 possible combination of mixtures:
m1: 1,2,3; m2: 1,2,4; m3: 1, 2, 5; m4: 1,3, 4; m5: 1, 3, 5; m6: 1,4,5; m7: 2,3,4; m8: 2, 3, 5; m9: 2 ,4, 5; m10: 3,4,5
Bacteria 1. E. coli O157:H72. Salmonella Enteritidis3. Bacillus cereus4. Yersinia enterocolitis5. Shigella boydii
3 out of 5 bacteria were used to make 10 possible combination of mixtures:
m1: 1,2,3; m2: 1,2,4; m3: 1, 2, 5; m4: 1,3, 4; m5: 1, 3, 5; m6: 1,4,5; m7: 2,3,4; m8: 2, 3, 5; m9: 2 ,4, 5; m10: 3,4,5
Testing and validation of the algorithm
Testing and validation of the algorithm
Differentiation of microorganisms in a cocktail of pathogens
Differentiation of microorganisms in a cocktail of pathogens
Example ‘fingerprint’ of each bacterium (for m2: 1, 2, 4)
Spectra after background interference removed
6
Discriminant analysis (m1: 1+2+3)
TestObservations
Centre of Group1
Centre of Group2
Centre of Group3
Centre of Group4
Centre of Group5
CV Score 1
CVScore
2
5
10
15
20
-5
-10
-15
-20
-25
10 20 30 40 50-10-20-30-40
Bacteria present1. E. coli O157:H72. Salmonella3. BacillusBacteria not present4. Yersinia5. Shigella100% accurate prediction for
this application !
Concept has been extended to a cocktail of up to 12 pathogens in apple juice
Food SafetyFood SafetyCharacterization
DetectionCharacterization
Detection
SpectroscopySpectroscopy BiosensorsBiosensors
FTIRRaman
Confocal-Raman
Single particleIR flim based
Biomimetic, SPR
BiosecurityBiosecurity
Nanoscalemethods
Single molecule methods
Chemistry, Physics,Engineering
SPR based pathogen detection
Sensor surface setup Flush
Ethanol D.Water HCl NaOH D.Water
SAM ActivationEDC & NHS mixture
Ab ImmobilizationAb in SA
Establish baseline: PBSTInject Ag: Ags in PBSTDissociation: 20 mM NaOHEstablish baseline: PBSTReuse the chip
Sensor surface setup Flush
Ethanol D.Water HCl NaOH D.Water
SAM ActivationEDC & NHS mixture
Ab ImmobilizationAb in SA
Establish baseline: PBSTInject Ag: Ags in PBSTDissociation: 20 mM NaOHEstablish baseline: PBSTReuse the chip
Assay Development
Sensitivity and SpecificitySensitivity and SpecificityO157:H7
7
Mid-Infrared BiosensorsMid-Infrared Biosensors
Chalcogenide film (GeSbSe)
Silicon substrate
Incoming IR beam To
detector
antibody Target (bacterial cells)
Gold coating
GeSe2 cladding layer
700 800 900 1000 1100 1200 1300 1400Wave numbers, cm-1
abso
rban
ce, a
rbitr
ary
unit
treated with E. coli K12treated with S. enteriditistreated with E. coli O157:H7treated with PBS buffertreated with E. coli cocktailtreated with all-three cocktail
Leigh, PSU, Purdue
Gold Islands on Chalcogenide films
50 % 50 % DiynoicDiynoic acidacid
50 % 50 % Natural LipidsNatural Lipids
++1. Sonication2. UV irradiation
Biomimetic color nanocompositesBiomimetic color nanocomposites
50 – 300 nm
Ben Gurion Univ (Jelenik group)
Professor Raz Jelinek, Ben Gurion University
Color sensor for pathogen detectionProfessor Raz Jelinek, Ben Gurion University
Color sensor for pathogen detection
Platform for pathogen detection through visible color changes.
Platform is based on films composed of lipids and chromatic polymer (polydiacetylene)
Platform for pathogen detection through visible color changes.
Platform is based on films composed of lipids and chromatic polymer (polydiacetylene)
control 1 ppb 4 ppb 20 ppb 40 ppb 4 ppm
The system detects the presence of substances that are active on biological membranes. Very high intrinsic sensitivity
Visible detection of Salmonella typhi. Less than 100,000 bacterial particles detected by eye. Higher sensitivity obtained using fluorescence microscopy
Positive control
109
107
105 106
108
medium
Salmonella typhimurrium E. coli
Lipid/PDA film
Agar growth matrix
Bacterial colony
Another color-based sensing assembly
8
Possible sensor productsPossible sensor products
Pathogen diagnosis patch for foodstuff
Generic food productPathogencontamination
Nanoparticle-based sensorsNanoparticle-based sensors
COOH
COOH
OHHNO3
(OH2)+(NO3)- + NH2CHCH2COOH
11-mercaptopropionic acid
ethanol
pH 2(O2C)CH2CHNH2
R
O
C COHOH
L-aspartic acid
(a)
(b)
(c)
O O
C OH +
H2O
C
N
N R1
R2
O
R C O C
NH
NHR1
R2
NHS+R C O
O
N
O
O
C
N
N R1
R2
O+
NH2 -RhPrPc R C
O
NH -streptavidin
(d)EDC
R C O
O
N
O
O
+ NH2 -streptavidin NHO
O
O
+R C
O
NH -streptavidin
R C
O
NH -streptavidin + biotin aptamer
R C
O
NH -streptavidin biotin aptamer
R C
O
NH -streptavidin biotin aptamer
+
biotinaptamerRhPrPc
(e)
(f)
COOH
COOH
OHHNO3
(OH2)+(NO3)- + NH2CHCH2COOH
11-mercaptopropionic acid
ethanol
pH 2(O2C)CH2CHNH2
R
O
C COHOH
L-aspartic acid
(a)
(b)
(c)
O O
C OH +
H2O
C
N
N R1
R2
O
R C O C
NH
NHR1
R2
NHS+R C O
O
N
O
O
C
N
N R1
R2
O+
NH2 -RhPrPc R C
O
NH -streptavidin
(d)EDC
R C O
O
N
O
O
+ NH2 -streptavidin NHO
O
O
+R C
O
NH -streptavidin
R C
O
NH -streptavidin + biotin aptamer
R C
O
NH -streptavidin biotin aptamer
R C
O
NH -streptavidin biotin aptamer
+
biotinaptamerRhPrPc
(e)
(f)
DNA sensors (Anal Chem); protein-aptamer (JACS), Enzyme Kinetics (NanoBio Tech J)
Rod shaped gold nanoparticles have unique optical properties
Rod shaped gold nanoparticles have unique optical properties
Gold nanoparticles
Gold nanorods
The red axis representsspherical shaped goldnanoparticles and appears atabout 500nm.
The green axis representsrod shaped goldnanoparticles and appearsat 600nm or above depends upon the aspect ratio of the rods
Au nanoparticles
Au nanorods
+ HAuCl4, AgNO3,Ascorbic acid +
Seed-mediated growth of gold nano rods
Yu and Irudayaraj [90% yield]
CTAB bilayer
anchor
targets
Capturing agents
9
Functionalization and Target detectionProbe: human IgG; Target: goat anti-human IgG
400 450 500 550 600 650 700 750 800 8500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Wave length, nm
Abs
orba
nce,
AU
RodsRods with SAM
Rods woth SAM and IgG
IgG/antiIgG binding
520 nm
679.5 nm
687 nm699.5 nm
710.5 nm
400 450 500 550 600 650 700 750 800 850 9000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Wave length, nm
Abs
orba
nce,
AU
RodsRods with SAM & IgG
Rods with SAM
IgG/anti IgG binding
514.5 nm
762.5 nm775 nm
797 nm839.5 nm
Multiplexing using nanorods
Bioaffinity SensorsBioaffinity Sensors
Receptor RDyeLectinEnzymeApoenzymeAntibodyReceptorTransport system
Receptor RDyeLectinEnzymeApoenzymeAntibodyReceptorTransport system
Chemical signal SProteinSaccharideHarmoneSubstrateAntigenSubstrate analogue
Chemical signal SProteinSaccharideHarmoneSubstrateAntigenSubstrate analogue
S + R SR
TransducersTransducersThermistors [Thermometric indication]
A change in enthalpy due to enzyme-catalyzed reactionOne reaction step and no final product
Thermistors [Thermometric indication]A change in enthalpy due to enzyme-catalyzed reactionOne reaction step and no final product
An enzyme attached thermistor is dipped in a sample Enzyme : Glucose oxidase, Urease, Trypsin
Substrate : Glucose, Urea, Cholesterol
Electrochemical TransductionPotentiometricAmperometricConductometric
Electrochemical TransductionPotentiometricAmperometricConductometric
Selected Biosensors (sugars)
Selected Biosensors (sugars)
milk0.002-350AmperoLactose
Brewer’s yeast frem
0.03-2.57AmperoMaltose
On-line< 8g/lpH-electrode
Glucose
cocoa0.2-2.820AmperoGlucose
ApplicationRange(mM)Life(d)PrincipleAnalyte
10
Other ExamplesOther Examples
0.01-160chemuLum
Lysine
E.Coli(culture)
0.025-1.090AmperoTryptophan
oils0.1-1.210AmperoFatty acids
juices0.001-1.018-21AmperoCitrate
ApplicationRange(nM)Life(d)PrincipleAnalyte
Questions in DevelopmentQuestions in DevelopmentMarketAlternate methods and costSensitivity and SpecificitySample matrix – is this changing?Measurement conditionsApplication – in/at/on/off -lineMeasurement range, time, life
MarketAlternate methods and costSensitivity and SpecificitySample matrix – is this changing?Measurement conditionsApplication – in/at/on/off -lineMeasurement range, time, life
Thank youThank you
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