Chemometrics in Quantitative RamanSpectroscopy

Steven E.J. Bell,Queen’s

University, Belfast

Raman Spectroscopy- identify andquantify molecular materials through lightscattering

• Irradiate sample with monochromatic radiation• Collect inelastically scattered light• Frequency difference gives vibrational spectrum

Todetector

h

hlaser in

h

h’

hh’Sample

h

Collectionlens

Background

Raman Shift /cm-1

Rayleigh

Inte

nsi

ty

0

Raman

’

3000 2500 2000 1500 1000 500Wavenumber /cm-1

IR transmission

Raman Scattering

Sca

tter

ing

Inte

nsity

Traditional Raman instrumentation

Background

ADVANTAGESADVANTAGES

o No sample preparation

• Non-destructive• Water backgrounds are low• Rich spectroscopic data (unique fingerprints)

DISADVANTAGESDISADVANTAGES•Scattering probability low

- expense and difficulty

Raman “pros” and “cons”

500 1000 1500 2000 2500 3000 3500500 1000 1500 2000 2500 3000 3500

Wavenumber /cm-1

Glucose

Sucrose

200 400 600 800 1000 1200 1400

Wavenumber /cm-1

Glucose

Sucrose

Rich and Unique Spectra

350 400 450 500 550350 400 450 500 550

Wavenumber /cm-1

Glucose

Sucrose

Drug profiles in Intravaginal Rings (IVRs)TMC120 is a potent HIV microbicide which canprevent infection.

It can be applied as a semi-solid gel asrequired but would be preferable to havedosage form that releases at >inhibitoryconcentration for very extended periods(months) to give continuous protectionagainst HIV infection.

IVR now well established (e.g. in HRT)

-reservoir of drug in core

--release through diffusion of drug through siliconesheath layer

-want to observe and understand this process

Simple Quantitation

3000 2500 2000 1500 1000 500

Core

Non-medicated sheath

In TMC 120 core is more difficult toobserve optically but spectra of drugand silicone elastomer very distinct

Wavenumber /cm-1

Simple Quantitation

5000

10000

15000

3050 3000 2950 2900 2850 2800 2750 2700 2650 26003050 3000 2950 2900 2850 2800 2750 2700 2650 2600

5000

10000

15000

2280 2260 2240 2220 2200 2180 21602280 2260 2240 2220 2200 2180 2160

Wavenumber /cm-1 Wavenumber /cm-1

Line scan 50 m steps x-y-z macro stage

Raw Raman data- from exterior into sheath

-CNSilicone

0

0.5

1

1.5

2

2.5

3

3.5

1 21 41 61 81 101 121 141

CORE

Step number

Dru

g/m

atri

x

Drugdetected outto exteriorsurfacelayer

Quantitation- drugs

Quantitation- drugs

Rationale – even for simple analytical problems use multivariate

methods because they can detect UNEXPECTED sources of

variance in the data.

Either :

1. Include them in the model.

2. Alter the experiment to eliminate the variance- results inparsimonious models with low numbers of factors(simple, stable and understandable).

500 1000 1500 2000

Wavenumber /cm-1

0

10

20

30

0 10 20 30Actual % MDEA

Pre

dict

ed%

MD

EA

Inte

nsity

/Arb

itr.u

nits { M

DE

A

Pre-processed by scaling on the strongest sorbitol band, mean centering, taking 1st derivative (Savitsky-Golay 15 pts) and including the spectral range 678-818 cm-1). The model is entirely as expected with a univariate system, using just a single principal component gives a calibration plot with R2 = 0.988 and anRMS error of 1.1 %

Quantitation- “Ecstasy” drugs

Sor

bito

l

PLS1R2 = 0.988,RMS error = 1.1 %

900800700600500400300

Wavenumber /cm-1

Inte

nsity

/Arb

itr.u

nits

(a)

(c)

(b)

(d)

(e)

Sampling error - “Ecstasy”

400 500 600 700 800 900 1000 1100

Wavenumber /cm-1

(f)

(h)

(g)

(a)

(c)

(b)

(d)

(e)

552

cm-1

527

cm-1

808

cm-1

Inte

nsity

/Arb

itr.u

nits

Sampling error - “Ecstasy”

500 1000 1500 2000

Crystal

Solution

Raw data

Solution

Crystal

1st deriv.

Wavenumber /cm-1

Simple Crystallization

PCA F3

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

F1-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

F2

Score vs score

400 600 800 1000 1200 1400 1600 1800

Expt soln.

Expt cryst.

PCA F1

PCA F2

Wavenumber /cm-1

Simple Crystallization

ADVANTAGESADVANTAGES

o No sample preparation

• Non-destructive• Water backgrounds are low• Rich spectroscopic data (unique fingerprints)

DISADVANTAGESDISADVANTAGES•Scattering probability low unless enhanced.

Raman “pros” and “cons”

SERS increases sensitivity of Raman experiments–enhancements up to x1012.

Single molecule SERS signals known.

In SERS the analyte is adsorbed to a microscopically roughmetal (Ag or Au) surface.

Either add analyte to solution orapply a drop of colloid to thesample.

Most common medium is asuspension of Au or Ag metalnanoparticles (10’s of nm) -metalcolloid.

TEM image ofColloid particles

100 nm

Problem -- colloids do change over time.

SERS background

N

N

HCH3

Nicotine increasingly important withintroduction of nicotine replacement therapies.

Standard method is HPLC but not well-suited forhigh throughput analysis and there are problemsassociated with the matrix in some dosage forms.

SERS – Nicotine

Few mg/dose

d5-pyridineinternalstandard

Nicotine

Wavenumber /cm-1

0 ppm

0.2 ppm

10 ppm

5 ppm

2 ppm1 ppm

Choose internal standard to be as chemically similar to the analyte as possible sothat any changes in the enhancement of the standard track those of the analyte.

SERS – Nicotine

0

1

2

3

4

5

6

0 1 2 3 4 5Actual Concentration (µg/ml)

Pre

dic

ted

(µg

/ml)

StandardsUnknownsLine (Standards)

RMSEP < 0.1 ppm

Calibration is extremely stable

Can Raman provide any realadvantages over established

techniques?

Light Microscopy

PyGC-MS

50

60

70

80

90

100

3500 3000 2500 2000 1500 1000

FT-IR Microscopy SEM/EDX

www.rjlg.com

Paint Analysis

BinderBinder Modified alkyd resins, oxidising resins

PigmentsPigments Dioxazine dyes, phthalocyanines,toluidine reds

ExtendersExtenders TiO2 (rutile), talc, CaCO3,china clay,/Fillers/Fillers BaSO4 etc.

51 lilac paints investigated51 lilac paints investigated(from sub(from sub--judicejudice case)case)

Paint Composition

785 nm excitation

500Wavenumber /cm–1

15001000

Lilac Paints

Despite diversity between spectra the lilac paints are predominantlycomposed of three different constituents in different relative amounts

Rel

.Int

ensi

ty

Rutile

Component 2

Component 1

Wavenumber/cm–1500 1000 1500

1

24

35

8

910

11

Lilac Paints

The two major constituents can be identified…The two major constituents can be identified…

Wavenumber/cm–1800600 1000 16001200 1400

N

N

O

O

N

N

H5C 2

Cl

Cl

H5C 2

CuN

N

N

N

N

N N

N

Lilac Paint

Violet 23

Blue 15.1

Lilac Paint

Violet 23

Blue 15.1785 nm

514 nm

Lilac Paints

Discriminate on the basis of the intensities of the three major constituents ?Discriminate on the basis of the intensities of the three major constituents ?

Component 1 (Blue)

0.5

1

1.5

2

2.5

3

3.5

0.5 1.5 2.5 3.5 4.5

Co

mp

on

ent

2(v

iole

t)

1.2

1.6

2

Component 1

4746

Com

pone

nt2

1.3 1.5 1.7 1.9

Lilac Paints

Batch variation

However, can also measure the minor constituents, e.g. CaCO3.

Rel

.In

ten

sity

Rutile

Blue 15.1

Violet 23

Wavenumber/cm–1500 1000 1500

1 7

24

35

8

910

11Extender CaCO3

6

Lilac Paints

Lilac 43

3 6 7 9 10 110

0.10.20.30.40.50.6

0.70.80.9

1

00.10.20.30.40.50.60.70.80.91

3 6 7 9 10 11

Lilac 7

00.10.20.30.40.50.60.70.80.91

3 6 7 9 10 11

500 1000 1500500

Wavenumber /cm–1

Lilac Paints

6

6

Build a spectral library of all51 paints to search against-this automatically includesthe minor bands.

Hit Position Number ofSamples /20

Top 16

Top 2 18

Top 5 20

Hit Position Number ofSamples /20

Top 16

Top 2 18

Top 5 20

Test with 20 samples-essentially100 % identification in 40seconds even with single layers.

100 % success in blind trialsmatch sample to manufacturer,colour and finish.

400 800 1200 1600

Wavenumber /cm-1

Crown Matt emulsionViva® BreatheEasy

Lilac Paints

Major advantage – can addnew samples at any time.

0

1x106

2x106

3x106

4x106

2500 2000 1500 1000 500

Co

unts

Wavenumber /cm-1

Raman bandRaman band

Fluorescence

750 800 850 900 950100 200 300 400 500 600 700 800 900

With strongly fluorescent samples, even if accumulate sufficient counts ( often not difficult)to reduce shot noise can still see apparent “noise” on the spectra which is due toirregularity in detector response (FPR).

Fluorescence

0 (414) 1000200 (895) 400(1376) 600(1857) 800(2338)

100% Mannitol

92.5% Mannitol, 7.5% Salicylic acid

87.5% Mannitol, 12.5% Salicylic acid

97.5% Mannitol, 2.5% Salicylic acid

90% Mannitol, 10% Salicylic acid

85% Mannitol, 15% Salicylic acid

95% Mannitol, 5% Salicylic acid

Column Number (Raman shift /cm-1)

ModelModel mannitolmannitol/salicylic acid tablets/salicylic acid tablets

Fluorescence model samples

0 (414) 200(895) 400(1376) 600(1857) 800(2338) 1000

97.5% Mannitol, 2.5% salicylic acid doped with increasing conc’n of laser dye.

Column Number (Raman shift /cm-1)

Fluorescence model samples

10000(414) 200(895) 400(1376) 600(1857) 800(2338)

823139

Column Number(Raman shift /cm-1)

2nd derivatives with increasing laser dye concentration

Dye

conc

entr

atio

n

Conventional 2nd derivative

2nd derivatives of the raw data removes baseline. Also increases FPRbut use multivariate data analysis to include it in the calibration model.

0 300100 200 400 500

Column No.100 200 300 400 5000

Weighting plots of 1st and 2nd components in PLS of 2nd derivative data

Raman

FPR

2nd Derivatives

PLS which includes the FPR as a component in themodel can work.

Predictions are good(standard error of prediction =0.66%)

BUT must keep the FPR the same and any change inexperimental conditions (change in fluorescenceprofile, wavelength recalibration during servicing)causes problems.

Alternative, is to remove the FPR before building themodel.

Enhanced Sensitivity

Strategy- correct for irregular response by recording pairs of spectra attwo slightly different grating positions and then subtracting them.

Wavenumber /cm-1

0

1x107

2x107

3x107

4x107

5x107

3000 2500 2000 1500

3000 2800 27002900

Cou

nts

Shifted subtracted Raman (SSRS)

Obs

erve

d

84

86

88

90

92

94

96

98

100

84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100Predicted

Standards Conc1 Conc2 Conc3Shifted Coumarin

0 200 400 600 800 1000Column Number

SEP < 0.5% evenSEP < 0.5% evenwith different dyewith different dyebackgroundsbackgrounds

Shifted subtracted Raman (SSRS)

Fatty acid methyl esters FAMEs

• Used as modelcompounds forunderstanding chemicaland physical propertiesof triglycerides in ediblefats and oils.

• Affect melting/softeningtemperatures of spreadingfats.

• Nutritional values –saturation, CLAs.

• Storage

O

O

CH2

CH2

CH2

Important properties;(a) length of alkyl chain.(b) number of double

bonds in alkyl chain.

O

O

O

O

O

O

Mass Unsaturation(C=C bonds per CH2)

0

1

2

3

4

5

0 0.1 0.2

Ram

an(C

=C

)/(C

H2)

0.3

Systematic changesin intensity of C=C band withincreasing unsaturation.

950 1150 1350 1550 1750

18:3c

18:0

18:1c

18:1t

18:2c

C=C

C=C

C=

OC

=OC

HC

H22

CH

CH

22

DFT on FamesFames Expt. Data

Wavenumber /cm-1

Note careful definition ofunsaturation- correlation withiodine value is non-linear.

Addition of CH2 groups does not givesimple incremental changes in spectra.

Some spectra appear to have more bandsthan expected, or bands that do not followthe smooth trends.

-vibrational bands do not arise fromisolated motions of individual CH2 units.

2:0

4:0

7:0

8:0

10:0

20:0

Wavenumber/cm-1200 400 600 800 1000 1200 1400 1600 1800

Fames Expt. Data

800 1000 1200 1400 1600 1800Raman Shift / cm-1

Ram

anIn

tens

ity

Beef

Lamb

Pork

Chicken

1.1901.97720 C unsat

0.10.1320:0

0.090.1818:3c Δ6,9,12

0.931.7118:3c Δ9,12,15

1.131.2518:2t Δ9,12

10.9911.1818:2c Δ9,12

1.561.37118:1t

5.4838.6118:1c Δ9

3.7810.4618:0

0.360.4717:1c Δ10

0.50.6817:0

2.034.5116:1c Δ9

4.0825.316:0

0.480.515:0

0.740.7114:1c Δ9

1.662.5514:0

Triglycerides - Adipose

4 species, various breeds etc gives samples with a range of fatty acid profiles

3562.544.011.62.010.97312.617.3PUFA

8733.8117.791.44.660.4996.575.1Cis/Trans

3730.02017.982.70.0220.5160.030.026Trans Unsaturation

3730.0414.76.20.0450.9650.240.73Cis Unsaturation

3730.0334.65.40.0410.9650.220.76Unsaturation

3730.03716.30.30.0460.5750.0715.51Saturation

3730.0546.90.40.0630.9220.2317.3Chain Length

No.FactorsSamplesRMSEE

RMSEP(% 4)

RMSEP(% s)RMSEPR2Mean

Triglycerides - Adipose

Prediction of bulk properties

Good prediction of chain length & unsaturation-saturation poor (low range).

PUFA important due to perceived health benefits.

Measured / % of total fatty acid

Pre

dic

ted

/%o

ftot

alfa

tty

acid

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 3525

30

35

40

45

50

25 30 35 40 45 50

PorkChicken

BeefLamb

18:1c Δ9 18:2c Δ9,12

Triglycerides - Adipose

2.0580.96510.9911.1818:2c Δ9,12

2.4760.7965.4838.6118:1c Δ9

RMSEPR2Mean

14.9026.552.181.03

45.0532.7039.3737.53

ChickenPorkBeefLamb

5540.458.219.80.3920.8901.1901.97720 C unsat

4540.02913.842.50.0550.7190.10.1320:0

6540.03014.729.70.0530.5930.090.1818:3c Δ6,9,12

3540.2057.616.60.2840.9320.931.7118:3c Δ9,12,15

3380.59715.957.50.7190.6131.131.2518:2t Δ9,12

3722.2784.718.42.0580.96510.9911.1818:2c Δ9,12

4630.84313.059.10.810.7521.561.37118:1t

2721.84211.36.42.4760.7965.4838.6118:1c Δ9

3721.4038.3121.2570.893.7810.4618:0

5540.05414.343.90.2060.70.360.4717:1c Δ10

37250.15010.530.80.210.8190.50.6817:0

3720.6849.917.80.8040.8342.034.5116:1c Δ9

17251.3628.05.21.3040.8924.0825.316:0

4720.06310.540.30.2020.8230.480.515:0

4530.41617.572.70.5170.4720.740.7114:1c Δ9

4720.49411.329.50.7510.7931.662.5514:0

No.Factors

SamplesRMSEE

RMSEP(% 4)

RMSEP(% s)

RMSEPR2Mean

Triglycerides (II) - Adipose

Cross-correlation ?

16:0

14:1c 917:0

15:0

18:3c 6,9,1216:1c 9

18:2c9,12 20:xa

0.2-0.4 -0.2 0.4

18:3c9,12,15

20:xc

20:xb20:0 18:0

14:017:1c

18:1t

18:2t9,12

18:1c 9

t [1]

0

-0.4

-0.2

0.2

0.4

0.6

t[2]

Group 1Group 2Group 3Group 4Group 5

3731.627.312.21.310.88418.010.77Group 5

7730.429.023.80.300.9253.41.27Group 4

2731.9615.831.62.000.92612.66.35Group 3

7733.769.56.32.700.86128.442.94Group 2

3732.325.916.62.070.97134.812.46Group 1

No.Factors

SamplesRMSEE

RMSEP(% 4)

RMSEP(% s)RMSEPR2Mean

Separation intogroups by PCA.

Prediction of Groupsby PLS1 allows cross-correlation to beexplicitly included.

Triglycerides (II) - Adipose

t[3]

t[2]

ChickenPork

BeefLamb

10000-10000 0

10000

5000

0

-5000

-10000

PLSDA scatter plot showing the discrimination of adiposespecies by multivariate analysis of Raman spectra800 1000 1200 1400 1600 1800

Raman Shift / cm-1

Ram

anIn

ten

sity

Beef

Lamb

Pork

Chicken

Adipose Tissue –Speciation

Model built with 102 samples, tested on 153 independent samples.

PLSDA > 99% correct classification of species in the test set (1 error)

JR Beattie, SEJ Bell, C Borgaard, A Fearon, BW Moss: Classification of AdiposeTissue Species using Raman Spectroscopy . Lipids 42 (2007) 679-685.

Conclusions & Acknowledgements• Raman methods have great potential for non-contact characterisation of abroad range of sample types.

With simple samples multivariate methods can be used to detectunexpected variance. This can then be designed-out or interpreted.

• For complex multicomponent samples the richness of the data allowsquantitation of individual constituents even in extremely complex spectra.

Acknowledgements

£££££’sE.P.S.R.C., Royal Society of Chemistry, RoyalSociety, F.S.N.I, Avalon Instruments Ltd, E.U.,D.E.N.I., D.A.R.D., I.N.I., Andor Technology Ltd

Rene BeattieRoma OakesN.M.S. SirimuthuJulien VillaumieLindsay BarrettLouise FidoIain Larmour

Bruce MossAnne FearonLinda FarmerJim SpeersLaota PetersKarl MalcolmDavid WolfsonKlaus Borgaard