Transcript No Slide Title

Potential Applications of Raman
Spectroscopy in Predicting Meat Quality
René Beattie
The Queen’s University of Belfast
Supervisors:
Dr SEJ Bell
Dr BW Moss
Roskilde, August, 2001.
•Introduction to Raman spectroscopy
•Comparison with NIR
•Current research:
Initial work on lipids – model systems
Meat lipids – adipose and intramuscular fat
Aspects of meat quality – cooking and ageing
•Previous work on Raman spectroscopy of meat
•Future plans and potential for Raman
•Introduction to resonance Raman spectroscopy
Raman Spectroscopy
• Irradiate sample with monochromatic radiation
• Collect inelastically scattered light
• Frequency difference gives vibrational spectrum
Rayleigh
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Intensity
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Advantages
Disadvantages
•Minimal sample prep.
•Weak effect
•Very general
•Expensive
•Rich in information
•Experimentally difficult
•Aqueous samples
•Fluorescence interferes
•“Special” techniques
Low-Cost, Compact Raman Spectrometers
Enabling Technologies
•Diode lasers:
Wide range of wavelengths and also
tunable lasers to allow increased flexibility.
~$10k
$60k
•Notch filter:
Eliminate the strong laser line, preventing
detector saturation.
>$100k
•CCDs (Charge Coupled Detectors):
Ultra high quantum efficiency detectors
for detection of very low levels of light.
Schematic layout diagram for the CCD system
Diffraction
Grating
Spectrograph
C.C.D.
l=785nm
Ti-Saph
Lasers
Ar+
Telescope
Depolariser
Holographic
Notch Filter
Sample
Raman Techniques: Dispersive Raman Spectroscopy
Most basic form of Raman spectroscopy:
UV/ Visible radiation – use conventional optics and detection
equipment
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:
Reduces fluorescence
Uses conventional visible optical equipment
Cheaper
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
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
Cost £30k upwards*
Cost £30k upwards*
User friendly
User friendly
* This price is for a general purpose bench-top instrument, rather than
smaller task orientated devices
Foodstuffs
NIR
Raman
•Sample preparation frequently required
•No sample preparation
•Main food groups all give spectra
•Main food groups all give detailed spectra
Raman Intensity
Absorbance
Carbohydrate
Protein
Fat
1100
1300 1500
1700
1900
2100 2300
Wavelength (nm)
2500
1800
1400
1000
Wavenumber (cm-1)
600
Comparison of NIR and Raman Spectra of Protein
Absorbance
Raman Intensity
Protein
Water
1100 1300 1500 1700 1900 2100 2300 2500
Wavelength (nm)
1800
1400
1000
Wavenumber (cm-1)
600
Problems Encountered in Literature
Background:
Not part of the Raman Signal – generally should be removed
Fluorescence, elastic light scattering, absorption and other
photon emitting processes
Glass – not suitable for NIR excitation (>700nm)
Signal Intensity:
Absolute intensity hard to control – need internal
standard
Depends on exact focal position, laser power, notch tuning,
sample absorption and system alignment.
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
Triglycerides
O
18:2t
OH
O
14:0
OH
HO
CH2
HO
CH
HO
CH2
O
16:1c
OH
• Chain length
• Unsaturation Level / Iodine values
• Cis/trans isomer ratios
• Physical state
Raman Intensity/ arbitary units
Raman Spectrum of a Triglyceride
H-C-H
H-C-H
C-C
C1-C2
+ CH3
C=C
=C-H
C=O
800
1000
1200
Raman Shift/cm-1
1400
1600
EFFECT OF INCREASING CHAIN LENGTH
Model Fats : FAMEs
CH2
5
C8
C7
C6
C5
Relative Band Intensity
4.5
C=O
R2 = 0.991
4
3.5
3
2.5
2
1.5
1
0.5
0
1000
1200
1400
1600
Wavenumber
1800
(cm-1)
0
5
10
Chain length
15
20
EFFECT OF INCREASING UNSATURATION
Model Fats : FAMEs
Commercial Fats and Oils
n(C=C)
0.5
d(CH2)
n(C=O)
18:4cis
18:2cis
18:1cis
0.45
Relative Peak Area
d(=C-H)
0.4
R2 = 0.982
0.35
0.3
0.25
0.2
0.15
18:0
0.1
20
800
1000 1200 1400 1600 1800
Wavenumber (cm-1)
30
40
50
60
70
Iodine Value
80
90
100
Raman Intensity
Comparison of the Raman spectrum of butter fat in
different physical states
80oC
21oC
-10oC
-176oC
700
1200
Raman Shift / cm-1
1700
Raman Intensity
Spectra of Various Animal Fats
Chicken
Pork
Lamb
Beef
800
1000
1200
1400
Raman Shift/cm-1
1600
PLS Discriminant Analysis of Adipose Data
10000
Chicken
Lamb
Beef
Pork
t[3]
5000
0
-5 0 0 0
-1 0 0 0 0
-1 0 0 0 0
0
t[2]
10000
PLSDA Weightings for Adipose Data
0 .1 6 0
Component 1
w*c[1]
Average Spectrum
0 .1 2 0
0 .0 8 0
0 .0 4 0
0 .0 0 0
w*c[2]
0 .1 0
Component 2
Unsaturation Level
0 .0 0
-0 .1 0
w*c[3]
0 .2 0
0 .1 0
Component 3
Solid fat content
0 .0 0
-0 . 1 0
0
1 0 0
2 0 0
3 0 0
NUM
4 0 0
5 0 0
6 0 0
PLS Analysis of Adipose Data
12000
10000
8000
Chicken
Lamb
Beef
Pork
6000
u[2]
4000
2000
0
-2 0 0 0
-4 0 0 0
-6 0 0 0
-8 0 0 0
-1 0 0 0 0
-5 0 0 0
0
t[2]
5000
10000
PLS Analysis of Lamb Adipose Data
10000
8000
6000
4000
u[2]
2000
0
-2 0 0 0
-4 0 0 0
-6 0 0 0
-8 0 0 0
-8 0 0 0
-6 0 0 0
-4 0 0 0
-2 0 0 0
0
t[2]
2000
4000
6000
8000
Unsaturation Profile of pork adipose tissue
0.38
Unsaturation Level / Raman band ratio
0.37
0.36
0.35
0.34
0.33
0.32
0.31
0.3
0.29
0
2000
4000
6000
8000
depth/ mm
10000
12000
14000
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.
Aspects of Meat Quality
Meat Quality:
•Taste
•Fat Content
•Appearance/ Colour
•Texture/ Tenderness
Tenderness:
•Cooking/ processing conditions
•Protein composition
•Storage conditions
•Ageing/ proteolysis
Raman Intensity
Raman spectra of the main components of meat
600
Protein
1000
1400
Raman Shift / cm-1
1800
The fine structure of skeletal muscle and meat
A Band
Z Line
I Band
M Line
H Zone
Actin
Myosin
Effect of Ageing on the Raman Spectrum of Meat
1 Day
11 Days
Cys Met
Tyr Skeletal n(C-N) Amide III CH2sc Amide I
a-helix b-sheet
Difference
Projected
Residual
600
800
1000
1200
1400
Raman Shift cm-1
1600
Principal Component Analysis of the Raman spectra
of Pork as it is aged
100
80
60
Day 1
Day 4
Day 8
Day 11
40
t[3]
20
0
-20
-40
-60
-80
-100
-100
0
t[2]
100
PLS Disriminant Analysis of the Raman spectra of
Pork as it is aged
100
Day 1
Day 4
Day 8
Day 11
t[3]
50
0
-5 0
-1 0 0
-1 0 0
0
t[2]
100
Weightings for PLS Disriminant Analysis of the
Raman spectra of Pork as it is aged
0 . 1 6 0
w*c[1]
0 . 1 2 0
Component 1: Average
(unscaled normalised data)
0 . 0 8 0
0 . 0 4 0
0 . 0 0 0
Component 2: amide hydrolysis and residue effects
w*c[2]
0 .1 0
0 .0 0
-0 .1 0
0 . 1 5 0
Component 3: secondary structure and residue shifts
w*c[3]
0 . 1 0 0
0 . 0 5 0
0 . 0 0 0
-0 . 0 5 0
0
1 0 0
2 0 0
3 0 0
NUM
4 0 0
5 0 0
PLS of the Raman spectra of Pork as it is aged –
Component 2, proteolysis
100
Day 1
Day 4
Day 8
Day 11
u[2]
50
0
-5 0
-1 0 0
-1 0 0 -8 0
-6 0
-4 0
-2 0
0
20
40
t[2]
60
80
100
120 140
160
Relative Intensity
Effect of Cooking on Components of Beef
650
Protein
900
1200
Raman Shift / cm-1
1500
1750
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
The future of Raman
Meat Quality Attributes
Instrumental/Rapid Method
Appearance
Reflectance
Flavour
Electronic nose +Raman?
Texture
NIR? Raman?
Nutritional Quality
Proximate Analysis
NIR?
Characterisation:Lipid
Raman
Protein/Amino Acids
Raman?
Carbohydrates
Raman?
Raman?
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 tenderness
o fat content
o time of cooking
o processing treatment.
Acknowledgements
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)
Resonance Raman Spectroscopy
hn
Chromophore
lmax
• Irradiate sample with
monochromatic radiation
corresponding to adsorption
band in UV-Vis spectrum
hn
hn’
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Excitation hn
hn’
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• Excite the particular bond involved in the adsorption to give
longer lived excited state.
• Increases the probability of change in vibrational state before
energy is released.
Rayleigh
Non-Resonance
Raman
Intensity
• Bands associated with this
adsorption are enhanced by a
factor of ~103 to 104 relative to
the ground state Raman and
Rayleigh.
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.