the queens university of belfast supervisors: dr sej bell dr bw moss potential applications of raman...
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The Queen’s University of Belfast
Supervisors:Dr SEJ Bell Dr BW Moss
Potential Applications of Raman Spectroscopy in Predicting Meat
Quality
René Beattie
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Roskilde, August, 2001.
• Introduction to resonance Raman spectroscopy
• Introduction to Raman spectroscopy
• Comparison with NIR
• Previous work on Raman spectroscopy of meat
• Current research: Initial work on lipids – model systemsMeat lipids – adipose and intramuscular fatAspects of meat quality – cooking and ageing
• Future plans and potential for Raman
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Raman Spectroscopy
• Irradiate sample with monochromatic radiation
hn
hn
hn
hn
hnhn’
hn’
• Collect inelastically scattered light
Rayleigh
Intensity
-n n’0
• Frequency difference gives vibrational spectrum
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• Weak effect
Advantages
• Minimal sample prep.
Disadvantages
• Very general
• Rich in information
• Aqueous samples
• “Special” techniques
• Expensive
• Experimentally difficult
• Fluorescence interferes
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• Diode lasers: Wide range of wavelengths and also tunable lasers to allow increased flexibility.
Low-Cost, Compact Raman Spectrometers
Enabling Technologies
• Notch filter:Eliminate the strong laser line, preventing detector saturation.
• CCDs (Charge Coupled Detectors):Ultra high quantum efficiency detectors for detection of very low levels of light.
>$100k
~$10k
$60k
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Schematic layout diagram for the CCD system
HolographicNotch Filter
Sp
ectr
og
rap
hC
.C.D
.
Depolariser
Sample
DiffractionGrating
Telescope
Ar+ Ti-SaphLasers
l=785nm
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Raman Techniques: Dispersive Raman Spectroscopy
In food analysis fluorescence and expense are the major problems. FT Raman spectroscopy overcomes fluorescence, but at an even higher cost.
Radiation on the boundary of visible and near-infrared radiation:
Most basic form of Raman spectroscopy:
UV/ Visible radiation – use conventional optics and detection equipment
Uses conventional visible optical equipment
Reduces fluorescence
Cheaper
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Comparison of NIR & Raman Spectroscopy:- principles of measurement
Near Infrared Reflectance Raman Spectroscopy
Non Destructive Non Destructive
Spectroscopic Spectroscopic
Molecular Vibrations + Electronic Configuration
Molecular Vibrations
Difficult to assign peaks Assignable peaks
Particle size + Physical State Physical State
Large water effect Low water interference
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Comparison of NIR & Raman Spectroscopy:- Practical Aspects
Near Infrared Reflectance Raman Spectroscopy
Large area of measurement Small area of measurement
Fibre optic system Fibre optic system
Compact systems
Compact systems
User friendly User friendly
Cost £30k upwards* Cost £30k upwards*
* This price is for a general purpose bench-top instrument, rather than smaller task orientated devices
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Foodstuffs
• Sample preparation frequently required
• Main food groups all give spectra
Wavenumber (cm-1)
1800 1400 1000 6001100 1300 1500 1700 1900 2100 2300 2500
Wavelength (nm)
Carbohydrate
Protein
Fat
RamanNIR
• No sample preparation
• Main food groups all give detailed spectra
Ab
sorb
ance
Ram
an In
tens
ity
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Wavenumber (cm-1)
1800 1400 1000 6001100 1300 1500 1700 1900 2100 2300 2500
Wavelength (nm)
Ab
sorb
ance
Ram
an In
tens
ity
Protein
Water
Comparison of NIR and Raman Spectra of Protein
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Problems Encountered in Literature
Background:
Not part of the Raman Signal – generally should be removed
Signal Intensity:
Absolute intensity hard to control – need internal standard
Glass – not suitable for NIR excitation (>700nm)
Fluorescence, elastic light scattering, absorption and other photon emitting processes
Depends on exact focal position, laser power, notch tuning, sample absorption and system alignment.
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Current Research
Model Lipids: Chain length
Unsaturation Level
cis/trans isomerisation
Physical State
Adipose Tissue: Speciation
Correlation with GC data
Composition variation within sample
Meat: Ageing
Cooking
Pressure Treatment
Intramuscular Fat
Sensory panels
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Triglycerides
HO
HO
HO
CH2
CH2
CH
OH
O18:2t
OH
O16:1c
OH
O14:0
• Physical state
• Chain length
• Unsaturation Level / Iodine values
• Cis/trans isomer ratios
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Ra
ma
n In
ten
sit
y/ a
rbit
ary
un
its
Raman Spectrum of a Triglyceride
C=O
C=C
H-C-H
=C-H
H-C-H
C-CC1-C2
+ CH3
800 1000 1200 1400 1600
Raman Shift/cm-1
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EFFECT OF INCREASING CHAIN LENGTH
C5
C6
C7
C8
1200 1400 1600 18001000
Wavenumber (cm-1)
CH2
C=O
Chain length
Re
lati
ve
Ba
nd
In
ten
sit
y0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
R2 = 0.991
0 5 10 15 20
Model Fats : FAMEs
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EFFECT OF INCREASING UNSATURATION
Wavenumber (cm-1)
1200 1400 1600 18001000800
18:4cis
18:2cis
18:1cis
18:0
n(C=C)
n(C=O)d(CH2)d(=C-H)
Model Fats : FAMEs
R2 = 0.982
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
20 30 40 50 60 70 80 90 100
Iodine Value
Rel
ativ
e P
eak
Are
a
Commercial Fats and Oils
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700 1200 1700Raman Shift / cm-1
Ram
an I
nte
nsi
ty 80oC
21oC
-10oC
-176oC
Comparison of the Raman spectrum of butter fat in different physical states
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Pork
Spectra of Various Animal Fats
800 1000 1200 1400 1600Raman Shift/cm-1
Ram
an I
nte
nsi
ty
Lamb
Beef
Chicken
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-1 0 0 0 0
-5 0 0 0
0
5 0 0 0
1 0 0 0 0
-1 0 0 0 0 0 1 0 0 0 0
t[3
]
t[2]
ChickenLambBeefPork
PLS Discriminant Analysis of Adipose Data
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0 . 0 0 0
0 . 0 4 0
0 . 0 8 0
0 . 1 2 0
0 . 1 6 0
w*c
[1]
0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0
- 0 . 1 0
0 . 0 0
0 . 1 0
0 . 2 0
w*c
[3]
NUM
- 0 . 1 0
0 . 0 0
0 . 1 0
w*c
[2]
PLSDA Weightings for Adipose Data
Component 1Average Spectrum
Component 2Unsaturation Level
Component 3Solid fat content
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-8 0 0 0
-6 0 0 0
-4 0 0 0
-2 0 0 0
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
1 2 0 0 0
-1 0 0 0 0 -5 0 0 0 0 5 0 0 0 1 0 0 0 0
u[2
]
t[2]
PLS Analysis of Adipose Data
ChickenLambBeefPork
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-8 0 0 0
-6 0 0 0
-4 0 0 0
-2 0 0 0
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
-8 0 0 0 -6 0 0 0 -4 0 0 0 -2 0 0 0 0 2 0 0 0 4 0 0 0 6 0 0 0 8 0 0 0
u[2
]
t[2]
PLS Analysis of Lamb Adipose Data
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Unsaturation Profile of pork adipose tissue
0.29
0.3
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0 2000 4000 6000 8000 10000 12000 14000
depth/ mm
Un
satu
rati
on
Lev
el /
Ram
an b
and
ratio
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Previous Raman Work Whole Muscle: • Spectra dominated by myosin (main component).
• Water Content.
Intact single fibers:
• Again dominated by myosin.
• Contraction had little effect.
• Ions affected individual amino acids.
Isolated proteins:
• Effect of different conditions (pH, salts and
temperature) on protein structure.
• All major proteins in meat.
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Aspects of Meat Quality
Meat Quality:
• Texture/ Tenderness
• Taste
• Appearance/ Colour
• Fat Content
Tenderness:
• Ageing/ proteolysis
• Protein composition
• Storage conditions
• Cooking/ processing conditions
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Raman spectra of the main components of meat
Raman Shift / cm-1
Ra
ma
n In
ten
sit
y
600
Protein
1000 1400 1800
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The fine structure of skeletal muscle and meat
Z Line
A Band I Band
M Line
MyosinActin
H Zone
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600 140012001000800 1600Raman Shift cm-1
Effect of Ageing on the Raman Spectrum of Meat
1 Day
Projected Residual
11 Days
Difference
Tyr SkeletalMetCys CH2scAmide IIIn(C-N) Amide I-a helix b-sheet
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-100
-80
-60
-40
-20
0
20
40
60
80
100
-100 0 100
t[3
]
t[2]
Day 1Day 4
Day 11Day 8
Principal Component Analysis of the Raman spectra of Pork as it is aged
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-1 0 0
-5 0
0
5 0
1 0 0
-1 0 0 0 1 0 0
t[3]
t[2]
Day 1Day 4
Day 11Day 8
PLS Disriminant Analysis of the Raman spectra of Pork as it is aged
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- 0 . 1 0
0 . 0 0
0 . 1 0
w*c
[2]
0 . 0 0 0
0 . 0 4 0
0 . 0 8 0
0 . 1 2 0
0 . 1 6 0
w*c
[1]
- 0 . 0 5 0
0 . 0 0 0
0 . 0 5 0
0 . 1 0 0
0 . 1 5 0
w*c
[3]
0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0NUM
Weightings for PLS Disriminant Analysis of the Raman spectra of Pork as it is aged
Component 1: Average(unscaled normalised data)
Component 2: amide hydrolysis and residue effects
Component 3: secondary structure and residue shifts
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-1 0 0
-5 0
0
5 0
1 0 0
-1 0 0 -8 0 -6 0 -4 0 -2 0 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0
u[2
]
t[2]
Day 1Day 4
Day 11Day 8
PLS of the Raman spectra of Pork as it is aged – Component 2, proteolysis
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Effect of Cooking on Components of BeefR
ela
tiv
e In
ten
sit
y
Raman Shift / cm-1650 900
Protein
175015001200
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Possible Plans for Meat Research:
• Speciation of meat (by muscle and/or fat).
• Cold shortening – contraction of meat.
• Tenderness – state of contraction, hydrolysis of proteins etc.
• Taste – can Raman predict which pieces of meat taste good?
• Final internal temperature of cooked meats.
• Fatty Acid composition – incorporate work on lipids.
• Investigate applications for Resonance Raman
Raman spectra will be compared to standard tests and to taste tests
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The future of Raman
Meat Quality Attributes Instrumental/Rapid Method
Appearance
Flavour
Texture
Reflectance
Electronic nose +Raman?
NIR? Raman?
Nutritional Quality
Proximate Analysis
Characterisation:-
Lipid
Protein/Amino Acids
Carbohydrates
NIR? Raman?
Raman
Raman?
Raman?
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Conclusions
• Raman is a powerful probe of the structure of
the protein and fat within meat.
• Raman can be used to investigate the extent and
process of proteolysis.
• Analysis underway to correlate Raman data with :
o fat content
o time of cooking
o processing treatment.
o tenderness
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My supervisors: Dr SEJ Bell and Dr BW Moss
Lab Mates: Clare, Antionette, Monica, Lyndsay, Kate, Roma, Colin, Steve, Andrew, Philip and Fiona.
Technical help: Alan, Colum, Griff and Ernie
? Institute?
Funding: DARD (NI)
Acknowledgements
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Resonance Raman Spectroscopy
• Increases the probability of change in vibrational state
before energy is released.
hn
hn’
hn
hn’
hn’
hn
• Excite the particular bond involved in the adsorption to give
longer lived excited state.
Excitation
lmax
• Irradiate sample with
monochromatic radiation
corresponding to
adsorption band in UV-
Vis spectrum
hnChromophore
• Bands associated with
this adsorption are enhanced
by a factor of ~103 to 104
relative to the ground state
Raman and Rayleigh.
RayleighIn
ten
sity
-n n’0
Non-Resonance Raman
Resonance Raman
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Applications of Resonance Raman Spectroscopy
Resonance Raman spectroscopy (RRS) probes particular bonds (chromophores) resulting in:
• Very precise information about specific bonds.
• Detection of very low concentrations of the chromophore (less than 10-6 M).
• Detection of very small changes in the chromophore.
This is useful for meat analysis because:
The amide bond of meat is a chromophore and has a well established relationship with the secondary and tertiary structure of the protein.
RRS can improve analysis of changes in amide bonding hence structure of the protein or level of proteolysis.