Breakout Session A - US Pharmacopeial Convention

Theory, Instrumentation and 

Sampling Techniques for 

Vibrational Spectroscopy 

Peter R. Griffiths 

Department of Chemistry 

University of Idaho 

Moscow, ID 83844-2343 

Food Protein Workshop: Developing a Toolbox of Analytical 

Solutions to Address Adulteration 

June 16 and 17, 2009: Breakout Session: “Theory and application 

of rapid mid-infrared, near-infrared and Raman methods and 

chemometrics to measure protein and prevent adulteration.” 

Handbook of Vibrational Spectroscopy 

1

Number of Vibrational Modes 

Molecules have three degrees of translational freedom, 

corresponding to motion in the x, y and z directions, and three 

degrees of rotational freedom, corresponding to motion 

around the three principal axes. 

y 

z 

O 

x 

H 

H 

The other 3N - 6 degrees of freedom are manifested as 

vibrational modes. 

Vibrational Transitions 

Each vibrational mode is 

quantized and the molecule 

can exist in the ground 

vibrational state (v = 0) or an 

excited vibrational state. The 

energy to promote the 

transition from the ground to 

the first excited vibrational 

state (v = 0 → 1) corresponds 

to absorption of radiation in 

the infrared spectrum. 

2

Energy of a Harmonic 

Vibrational Mode 

The energy of a harmonic vibration is: 

E 

~ 

iv 

= h cν 

i 

(vi 

+ 0.5) 

where 

~ ν 

i is the wavenumber of the i th mode 

and v i is the vibrational quantum number 

The only transitions allowed for harmonic 

vibrations are for Δv i = ±1. 

Fundamental Transitions 

Fundamental Transition: v = 0 → v = 1 

v = 0 

v = 1 

v = 2 

v is the vibrational quantum number 

3

Fundamental Transitions 

All fundamental transitions absorb radiation 

in the mid-infrared region of the spectrum 

(wavelengths from 2.5 – 25 μm); 

Wavenumber is the number of waves per 

unit length - usually given as cm -1 ; 

Therefore the wavenumber region of the 

mid-infrared spectrum is 4000 – 400 cm -1 . 

Energy of an Anharmonic 

Vibrational Mode 

The energy of an anharmonic vibration is: 

E 

iv 

= h c 

~ ν (v + 0.5) − 

~ 

i i 

h cν 

i 

xi 

(vi 

+ 

2 

0.5) 

where x i is the anharmonicity constant; 

and ~ ν is the wavenumber of the i th mode. 

i 

The transitions allowed for anharmonic vibrations are 

for Δv i = ±1, ±2, ±3, etc…. but as Δv i gets larger, the 

probability of the transition decreases and the band 

becomes much weaker. 

4

First Overtone 


First Overtone: v = 0 → v = 2 

v = 0 

v = 1 

v = 2 

Second Overtone 


First Overtone: v = 0 → v = 2 

Second Overtone: v = 0 → v = 3 

v = 0 

v = 1 

v = 2 

v = 3 

5

Combination Bands 

It is possible for one photon to excite two different 

vibrational modes, so that an absorption band is 

seen at a wavenumber equal to the sum of the 

wavenumbers of the two fundamentals. Such 

bands are known as combination bands. 

The two modes must have at least one atom in 

common. 

Overtone and Combination Bands 

Overtone and combination bands may absorb light in 

either the mid-infrared or the near-infrared region of 

the spectrum. 

Overtone and combination bands involving C-H, O-H 

or N-H stretching modes usually absorb radiation in 

the near-infrared spectrum. 

They are weaker than the fundamental modes from 

which they are derived, but this is not necessarily a 

bad thing. 

6

1st Overtone Bands 

H 2 

O 

ROH 

R‐NH 2 

Ar OH 

Ar C‐H 

CH 3 

CH 2 

CH 

CH‐Bend Fundamental 

Combined with CH‐ 

Stretch 1 st Overtone 

CH 3 

CH 2 

CH 

1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 

Wavelength (nm) 

2nd & 3rd Overtone Bands 

H 2 

O 

R‐NH 2 

R‐NH 2 

ROH 

Ar C‐H 

Ar C‐H 

CH 3 

CH 2 

CH 3 

CH 2 

CH 

CH 

750 800 850 900 950 1000 1050 1100 1150 1200 1250 


7

How Do Molecules Interact With Light? 

For a vibration to absorb infrared radiation, there must 

be a change in the dipole moment during the vibration. 

The vibration is sometimes described by a normal 

coordinate, Q. 

The absorptivity is proportional to the square of the 

change in dipole moment with the normal coordinate. 

a 

= 

k 

⎛ 

⎜ 

⎝ 

2 

μ ⎞ 

⎟ 

⎠ 

d 

dQ 

where k is a constant. 

Raman Spectroscopy 

Sample is illuminated with monochromatic light from a 

laser. 

Assume that the sample is transparent, i.e., no absorption. 

Not quite all the light passes through the sample; a small 

fraction is scattered in all directions. 

This scattered light is collected and passed into a 

monochromator or an interferometer and hence to a 

detector. 

8

Rayleigh Scattering 

Most of the scattered radiation is of the same wavelength 

as the incident laser radiation. 

This is known as Rayleigh scattering, or to elastic 

collisions between the photons and the molecule. 

Rayleigh scattered light is about 10 -5 or 10 -6 times the 

intensity of the laser radiation and is actually a nuisance 

for Raman spectroscopy. 

Raman Scattering 

Some extremely weak additional features in the scattered 

light are found at slightly different frequencies. 

They are displaced from the “exciting line” by exactly the 

vibrational energy spacings. 

These new features are called Raman lines bands and are 

due to inelastically scattered radiation. 

They are very weak - about 10,000 times weaker than the 

Rayleigh scattered radiation. 

9

Collision Model for Scattering 

Rayleigh Scattering 

hν 0 hν 0 


hν 0 h(ν 0 - ν vib ) 


An elastic collision gives Rayleigh scattering - a change in 

direction but no change in frequency. 

An inelastic collision leaves an amount of energy in the molecule 

equal to hcν ~ vib, where ~ ν vib is in cm -1 . Therefore, the scattered 

photon has less energy than the incident photon. Because it has an 

energy hc (ν ~ 0 - ~ ν vib), it will appear at the new position (ν ~ 0 - ~ ν vib). 

Thus there is a change in direction and frequency. 

Thus for a Raman band, ~ ν obs = (ν ~ 0 - ~ ν vib). This can be rearranged 

to ~ ν vib = (ν ~ 0 - ~ ν obs). Since the latter two can be measured, one can 

obtain vibrational frequencies from Raman scattering. 

By convention, Raman frequencies are displacements from the 

exciting line, ~ ν 0 - ~ ν obs. They are always given in cm -1 . 

10

Energy Level Diagram 

“Virtual State” 

Origin of Raman Scattering 

11

Nature of Raman Scattering 

It is not an absorption process. It is a scattering 

effect and closer to emission than absorption. 

The displacement, ν ~ 0 - ν ~ obs cm -1 , corresponds to 

the infrared range. 

Raman scattering offers a means of measuring 

vibrational bands using ultraviolet, visible or 

near-infrared light, where detection is far more 

sensitive. 

Raman intensity is proportional to (ν ~ 0 - ν ~ obs) 4 , so 

the shorter the wavelength the better – except 

when the sample fluoresces! 

Comparison of Infrared and 

Raman Spectra 

Both infrared and Raman are vibrational spectra 

Provided that both spectra are of samples in the 

same physical state, the wavenumber of a give 

vibrational mode is the same in the IR and Raman. 

The relative intensities of bands in the IR and 

Raman may be very different as the mechanisms 

are very different. 

In many respects, IR and Raman spectroscopy are 

complementary. 

12

Forbidden Bands in the Infrared 

If the dipole moment doesn’t change during the 

vibration, I = 0 and the band is “forbidden in the 

infrared. 

(The vibration still occurs but it cannot be excited by 

absorption of infrared radiation.) 

If ∂μ/∂Q is large, the band is intense. (Remember the 

squared dependence.) 

Strong Bands in the Infrared 

Bands that are strong in the infrared: 

Stretching of polar bonds: O-H, C-O, C=O, C-Cl 

Out-of-plane wags of unsaturated C-H, as in olefin 

and phenyl rings 

13

Weak Bands in the Infrared 

If the electronegativities of the two atoms forming the 

bond are nearly equal, stretching the bond usually gives 

rise to a weak band, e.g.: 

H-H, C-C 

C≡C stretch in H- C≡C-H, R- C≡C-R and R- C≡C-R′ 

C-H stretch; the intensity per C-H group is usually low. 

Intensity in Raman Bands 

The intensity of Raman bands is determined by a 

change in polarizability during a vibration: 

I 

⎛ ∂α 

⎞ 

α ⎜ ⎟ 

⎝ ∂Q 

⎠ 

2 

where α is the polarizability 

Q is the normal coordinate. 

The polarizability measures the ease with which an 

external electric field can induce a temporary dipole 

momemt, i.e., the ease with which electrical charges in 

a molecule can be displaced. 

14

Strong Raman Bands 

The polarizability is large when there is a high 

concentration of loosely held electrons in a bond that is 

involved in the vibration, since ∂α/∂Q is also large 

then. Some examples: 

Multiple bonds: C≡C, -C≡N, C=C, C=O; 

Stretching and bending of -Cl, -Br and -I bonds; 

Other many-electron atoms: S, Hg, other heavy metals 

For bonds that are strongly polar, Raman bands tend to 

be weak, e.g.: 

Stretching of O-H and C-F. 

Infrared Spectral Regions 

Now classified as follows: 

cm -1 

μm 

Near-Infrared 12,500 - 4000 0.8 - 2.5 

Si region 12,500 - 9,100 0.8 - 1.1 

PbS region 9,100 - 4,000 1.1 - 2.5 

Mid-Infrared 4,000 - 400 2.5 - 25 

Far-Infrared 400 - 10 25 - 1,000 

15

Sources 

Mid Infrared 

Incandescent SiC rod at ~1200K (Globar) 

Near Infrared 

Standard tungsten filament light bulb 

Quartz-tungsten-halogen lamp 

Raman 

Nd:YAG or Nd:YAlO 4 (1064 nm) 

Diode laser (785 nm usually) 

Helium-neon (633 nm) 

Frequency-doubled Nd:YAG (532 nm) 

Detectors 

Mid Infrared 

Deuterated triglycine sulfate pyroelectric (298K) 

Mercury cadmium telluride (77K) 

Near Infrared 

TE-cooled lead sulfide 

Indium gallium arsenide 

Silicon (for λ < 1100 nm) 

Raman 

TE-cooled charge-couple device array (CCD) 

LN 2 -cooled Ge or InGaAs 

16

Spectrometers 

Mid Infrared 

Fourier transform infrared (FT-IR) 

Near Infrared 

Filter 

Grating monochromator or polychromator 

FT-NIR 

Tunable laser 

Raman 

Polychromator 

FT-Raman 

Smiths Detection IdentifyIR TM 

Portable FT-IR Spectrometer 

17

Concept 

Reality 

Miniature Optical Bench 

14mm 

How do you make an optical 

bench that is just 14mm long? 

18

The Platform 

BENCH 

DEVICES 

Optical Module 

Ahura Hand-Held Raman Spectrometer 

19

Sampling Techniques for Mid-Infrared 

Transmission 

KBr disks and mineral oil mulls (rare today) 

Diffuse Reflection 

Needs dilution in an IR-transparent powder 

Attenuated Total Reflection (ATR) 

ZnSe or diamond (n = 2.4) 

Germanium (n = 4.0) 


20

Beam Paths Through Powders 

Specularly reflected 

light 

Diffusely reflected 

light 

Diffusely transmitted light 

Attenuated Total Reflection 

21

Refraction 

Low refractive 

index, n 1 

θ 1 

θ 2 

High refractive 

index, n 2 

Snell’s Law: n 1 sin θ 1 = n 2 sin θ 2 

Behavior Above and Below the Critical Angle 

Low refractive 

index, n 1 

High refractive 

index, n 2 

θ C 

Beam below critical 

angle obeys Snell’s 

Law 

θ C = sin -1 (n 1 /n 2 ) 

Beam above critical 

angle reflects 

internally 

22

Optical Properties of Infrared 

Transmitting Materials 

Material n 2 θ C (for n 1 = 1.50) 

Potassium bromide 1.51 90º 

Silver chloride 1.90 49º 

Zinc sulfide 2.20 43º 

KRS-5 2.37 40º 

Zinc selenide 2.40 40º 

Diamond 2.41 40º 

Silicon 3.41 26º 

Germanium 4.00 22º 

Depth of Penetration 

d p 

= 

λ 

2 

2 

2π 

n 

1 

2 

sin θ − 

⎛n 

⎞ 

⎜ n ⎟ 

2 ⎠ 

⎝ 

23

First-Order ATR Correction 

(divide by wavelength) 

650 to 4000 cm -1 @ better than 4 cm -1 resolution 

Single-bounce diamond ATR for ‘press-and-shoot’ 

ZnSe beamsplitter, laser-referenced interferometer 

Temperature/shock/drop rugged (-20°C to +40°C operation) 

“MIL-STD 810F” certified 

DLTGS 

ATR (diamond) 

fixed mirror 

broadband source 

beamsplitter 

moving mirror 

24

MIL-SPEC-810F Compliance 

CHALLENGE 

Mechanical shock 

Vibration 

Transit Shock 

Humidity 

Sand/dust/dirt 

Thermal shock 

Low temp. (op) 

High temp. (op) 

Low temp. (store) 

High temp. (store) 

Immersion (op) 

SPECIFICATION 

40g in 11ms, saw-tooth 1 day 

1hr/axis, composite wheeled vibration 

4 foot drop onto plywood on concrete 26 times 

5X (48hrs) @ 60C & 95% RH 

Blowing dust 

-30C to +60C in 1 meter of water 

Sampling Techniques for Near-Infrared 

Transmission 

Usually in the short-wavelength region 


Needs no dilution 

Transflection 

Mount liquid or semi-solid sample on reflective 

base and then use diffuse reflection optics. 

25

Sampling Techniques for Raman 

Liquids 

Hold sample in glass capillary or in glass bottle 

Solids 

Measure as received 

Surface-Enhanced Raman Scattering (SERS) 

Evaporate solution on roughened silver or gold. 

Surface-Enhanced Raman Scattering 

(SERS) 

Enhancement of the 

Raman spectrum of 

molecules on the 

surface of roughened 

silver or gold metal 

plates, colloids or 

nanoparticles. 

5-nm thick layer of silver 

26

SERS Spectrum of a Self-Assembled Monolayer 

of p-Nitrothiophenol on Silver Surface 

Produced by Physical Vapor Deposition (10 s) 

Counts (Normalized and Corrected) 

2000 

1000 

0 

2000 

1500 

1000 

Raman Shift / cm -1 

500 

27

NIR Technology to Help 

Food Technology 

Karl H. Norris 

Consultant 

11204 Montgomery Rd, 

Beltsville, MD 20705 

knnirs@gmail.com 


Solutions to Address Adulteration, June 16-17,2009 

NIR Merits 

1. Many food constituents have an NIR signal. 

2. The absorption bands are of the correct magnitude for 

measurement with little or no sample preparation. 

3. Radiation sources are readily available. 

4. Rapid data collection, with good signal to noise. 

5. Detectors and associated electronics exist, and they 

continuing to improve. 

6. Windows, lenses, fibers, and standards are available. 

7. Chemometrics has grown with NIR technology. 

8. Computer technology is readily available. 

9. Many technologies available for collecting NIR spectra. 

28

Error Sources for NIR of Scattering Samples 

1. Sampling Errors 

2. Sample Stability 

3. Packing Effects 

4. Sample Temperature Effects 

5. Reference Data Errors 

6. Instrument Noise 

7. Humidity Effects 

29

Source Fibers 

Collecting Fibers 

Blocking Metal 

Interactance Probe 

30

Photo by Kayla Dowell 9th Grade Germann Hills Christian School 

31

Beam Paths Through Powders 

Specularly reflected 

light 

Diffusely reflected 

light 

Diffusely transmitted light 

DIFFUSE REFLECTION: 1850-2500 nm 

1. Small Particles, less than 0.5 mm 

2. Low Moisture, less than 15% 

36


1. Medium Particle Size, less than 2 mm 

2. Medium Moisture, 15-30 % 


1. Large Particle Size, greater than 2 mm 

2. High Moisture, 30-90 % 

37

Human Hand 

Water 

Fat 

Deoxyhemoglobin 

38

B 

B 

A 

39

Protein Measurements in Food Products 

• The official method measures nitrogen to 

obtain protein content. 

• NIR does not sense nitrogen. 

• NIR senses overtones of NH vibrations of 

the amino acids to sense protein. 

41

Detecting Adulterants and Unwanted Synthetics in Ingredients 

by Cynthia Kradjel, Application Development Manager, Buchi Corporation 

42

References to use of NIR to detect melamine in food powders: 

Melamine Detection in Infant Formula Powder Using Near- and Mid-Infrared 

Spectroscopy 

Lisa J. Mauer, Alona A. Chernyshova, Ashley Hiatt, Amanda Deering and Reeta 

Davis 

J. Agric. Food Chem., 2009, 57 (10), pp 3974–3980 

Publication Date (Web): April 22, 2009 (Article) 

DOI: 10.1021/jf900587m 

doi: 10.1255/jnirs.829 

Rapid detection of melamine in milk powder by near infrared spectroscopy 

Chenghui Lu, Bingren Xiang, Gang Hao, Jianping Xu, Zhengwu Wang and 

Changyun Chen 

43

0.9 

2 Melamine Reflection Spectra 

0.8 

0.7 

Log(1/R) 

0.6 

0.5 

0.4 

9 nm bandpass monochromator- 

0.3 

0.2 

0.1 

-High Resolution FT 

0 

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 

Wavelength nm 

44

Proposed Method for Detecting Adulteration 

1. Develop 6 or more different calibrations for protein on the 

product to be tested. 

2. Perform an NIR scan of the sample to be tested. 

3. Predict the protein content of test sample with each of the 

different calibrations. 

4. Observe the range of the predicted proteins. 

5. If the predicted range exceeds a value (~5% of the average), 

the sample is declared as adulterated. 

Derivative-Ratio Regression 

• % Protein = A + B(WL1-WL2)/ WL3 

• WL1 is second derivative at wavelength 1 



• A, B, WL1, WL2, and WL3 are optimized 

to provide the minimum SEC for %Protein 

on a group of sample spectra. 

45

Results of Adulteration Tests on Flour 

% Protein = A + B(WL1-WL2)/ WL3 

Calibration No. 

1 

2 

3 

4 

5 

6 

7 

WL1 nm 

1977.0 

1687.0 

1223.5 

2209.0 

1068.5 

1845.0 

1139.0 

WL2 nm 

1625.0 

1961.5 

1978.0 

1971.5 

1976.5 

1978.5 

1978.0 

WL3 nm 

1333.0 

2293.0 

1965.5 

1966.5 

994.0 

872.5 

868.0 

SEC %PRO. 

0.102 

0.139 

.0114 

0.122 

0.118 

0.114 

0.124 

0% RMSEP 

0.008 

0.214 

0.135 

0.030 

0.084 

0.092 

0.072 

0.1%M RMSEP 

0.132 

0.246 

0.258 

0.335 

0.163 

0.097 

0.071 

1.0%M RMSEP 

1.300 

1.292 

0.987 

2.201 

1.425 

0.207 

0.272 

. 



Calibration # 

1 

2 

3 

4 

5 

6 

WL1 nm 

1692 

1982 

1736 

2184 

2112 

1188 

WL2 nm 

816 

748 

2170 

774 

2330 

1184 

WL3 nm 

1606 

1816 

1516 

1518 

1896 

976 

SEC %PRO. 

0.116 

0.076 

0.086 

0.076 

0.132 

0.123 

0% RMSEP 

0.070 

0.101 

0.037 

0.062 

0.046 

0.162 

0.1%M RMSEP 

0.075 

0.172 

0.127 

0.123 

0.153 

0.251 

1%M RMSEP 

0.181 

1.715 

1.627 

1.452 

1.474 

2.209 

46




1 

2 

3 

4 

5 

6 

WL1 nm 

1692 

1982 

1736 

2184 

2112 

1188 

WL2 nm 

816 

748 

2170 

774 

2330 

1184 

WL3 nm 

1606 

1816 

1516 

1518 

1896 

976 

SEC %PRO. 

0.116 

0.076 

0.086 

0.076 

0.132 

0.123 

0% RMSEP 

0.070 

0.101 

0.037 

0.062 

0.046 

0.162 

0.1%M RMSEP 

0.075 

0.172 

0.127 

0.123 

0.153 

0.251 

1%M RMSEP 

0.181 

1.715 

1.627 

1.452 

1.474 

2.209 

0.2%Urea RMSEP 

0.575 

0.181 

0.166 

0.116 

0.438 

0.222 

1.0%Urea RMSEP 

3.507 

1.078 

0.822 

0.621 

3.187 

0.992 


NIRSystems 6500, year 1990 


1 

2 

3 

4 

5 

6 

STD 

Max-Min 

SEC % Pro. 

0.116 

0.076 

0.086 

0.076 

0.132 

0.123 

0.025 

0.056 

NIR % P, Clean 

NIR % P, 0.1%M 

NIR % P, 1.0%M 


NIR % P, 0.1%M 

NIR % P, 1.0%M 


NIR % P, 0.1%M 

NIR % P, 1.0%M 

13.94 

13.92 

13.76 

10.78 

10.77 

10.64 

8.51 

8.50 

8.37 

13.76 

13.93 

15.45 

10.84 

11.01 

12.59 

8.59 

8.78 

10.45 

13.88 

14.07 

15.89 

10.75 

10.90 

12.26 

8.56 

8.69 

9.91 

13.87 

13.99 

15.72 

10.83 

10.84 

11.01 

8.52 

8.46 

7.60 

13.88 

13.87 

13.78 

10.76 

10.68 

9.80 

8.54 

8.39 

6.48 

13.72 

13.93 

16.08 

10.86 

11.05 

12.91 

8.72 

8.92 

10.92 

0.084 

0.069 

1.061 

0.046 

0.041 

1.249 

0.077 

0.206 

1.750 

0.22 

0.20 

2.32 

0.11 

0.37 

3.11 

0.21 

0.53 

4.44 

47




1 

2 

3 

4 

5 

6 

STD 

Max-Min 

SEC % Pro. 

0.116 

0.076 

0.086 

0.076 

0.132 

0.123 

0.025 

0.056 


NIR % P, 0.1%M 

NIR % P, 1.0%M 


NIR % P, 0.1%M 

NIR % P, 1.0%M 


NIR % P, 0.1%M 

NIR % P, 1.0%M 

13.94 

13.92 

13.76 

10.78 

10.77 

10.64 

8.51 

8.50 

8.37 

13.76 

13.93 

15.45 

10.84 

11.01 

12.59 

8.59 

8.78 

10.45 

13.88 

14.07 

15.89 

10.75 

10.90 

12.26 

8.56 

8.69 

9.91 

13.87 

13.99 

15.72 

10.83 

10.84 

11.01 

8.52 

8.46 

7.60 

13.88 

13.87 

13.78 

10.76 

10.68 

9.80 

8.54 

8.39 

6.48 

13.72 

13.93 

16.08 

10.86 

11.05 

12.91 

8.72 

8.92 

10.92 

0.084 

0.069 

1.061 

0.046 

0.041 

1.249 

0.077 

0.206 

1.750 

0.22 

0.20 

2.32 

0.11 

0.37 

3.11 

0.21 

0.53 

4.44 




SEC % Pro. 

1 

0.116 

2 

0.076 

3 

0.086 

4 

0.076 

5 

0.132 

6 

0.123 

STD 

0.025 

Max-Min 

0.056 


13.94 

13.76 

13.88 

13.87 

13.88 

13.72 

0.084 

0.22 

NIR % P, 0.2%Urea 

14.63 

14.01 

13.94 

13.98 

14.50 

13.94 

0.276 

0.69 


18.08 

15.11 

14.12 

14.40 

18.08 

14.92 

1.584 

3.96 


10.78 

10.84 

10.75 

10.83 

10.76 

10.86 

0.046 

0.11 


11.31 

11.03 

10.94 

10.96 

11.15 

11.03 

0.148 

0.37 


13.94 

11.87 

11.53 

11.45 

13.40 

11.78 

0.96 

2.41 


8.51 

8.59 

8.56 

8.52 

8.54 

8.72 

0.077 

0.21 


9.02 

8.77 

8.83 

8.68 

8.86 

8.88 

0.096 

0.24 


11.63 

9.52 

9.72 

9.29 

10.88 

9.57 

0.94 

2.34 

48




SEC % Pro. 

1 

0.116 

2 

0.076 

3 

0.086 

4 

0.076 

5 

0.132 

6 

0.123 

STD 

0.025 

Max-Min 

0.056 


13.94 

13.76 

13.88 

13.87 

13.88 

13.72 

0.084 

0.22 


14.63 

14.01 

13.94 

13.98 

14.50 

13.94 

0.276 

0.69 


18.08 

15.11 

14.12 

14.40 

18.08 

14.92 

1.584 

3.96 


10.78 

10.84 

10.75 

10.83 

10.76 

10.86 

0.046 

0.11 


11.31 

11.03 

10.94 

10.96 

11.15 

11.03 

0.148 

0.37 


13.94 

11.87 

11.53 

11.45 

13.40 

11.78 

0.96 

2.41 


8.51 

8.59 

8.56 

8.52 

8.54 

8.72 

0.077 

0.21 


9.02 

8.77 

8.83 

8.68 

8.86 

8.88 

0.096 

0.24 


11.63 

9.52 

9.72 

9.29 

10.88 

9.57 

0.94 

2.34 

Testing for Adulteration with Different Materials 

Model XDS-RCA, year 2002 

49


Model XDS-RCA, year 2002 

14.5 

14 

13.5 


on a high protein flour spectrum 

NIR % Protein 

13 

12.5 

12 

11.5 

11 

No Contamination 

0.1% Melamine 

1.0% Melamine 

1.0% Talc 

1.0% Urea 

5.0% Soy Protein 

10.5 

10 

9.5 

1 2 3 4 5 6 7 

Calibration Number 

50

Acknowledgements 

• Dr. David Hopkins for the melamine spectrum. 

• DR. Ron Rubinovitz, Buchi Corp., for a high 

resolution melamine spectrum. 

• Foss-NIRSystems for access to flour spectra. 

Conclusions 

NIR Technology can help Food Technology. 

A simple method is proposed using multiple 

protein calibrations on a single NIR scan to 

detect adulteration in food products. 

Test results are provided with computer simulation 

of wheat flour spectra adulterated with a 

melamine spectrum, and shows definitive 

detection at the 1.0% level. 

Results are also presented for detection of 

adulteration with talc, soy protein, and urea. 

The procedure needs to be tested with actual 

samples on different types of food products. 

51

“Intuitive” Chemometrics for 

Protein Measurements and 

Adulterant Detection 

David B. Funk, Ph.D. 

Technical Services Division 

Grain Inspection, Packers and Stockyards Administration 

U.S. Department of Agriculture 

Kansas City, Missouri 

52

What is an NIR Calibration? 

• An equation (or, particularly, a set of 

equation coefficients) that expresses a useful 

relationship between electro-optic parameters 

and a constituent of interest. 

• Many different mathematical methods exist 

for deriving “optimum” equations. 

• Most NIR calibration equations have a simple 

common form. 

NIRS Prediction Equation Forms 

Sum of products 

%P K 0 + K 1 ⋅L 1 + K 2 ⋅L 2 + .... + K n ⋅L n 

Summation Notation 

Vector/Matrix Notation 

∑ ( ) 

%P K 0 + K n ⋅L n %P K⋅L 

n 

L 0 1 

53

An NIR Calibration Defines: 

• The wavelengths used 

• Few wavelengths 

• Many wavelengths 

• All available wavelengths 

• The form of the equation 

• Usually linear sum of products 

• Data pretreatment required 

• Derivative math (smoothing, gap) 

• Scatter correction 

• Specific coefficients associated with data 

collected at each wavelength or linear 

combination of wavelengths 

Typical Wheat NIRT Spectrum 

200 

Coefficient Absorbance Value 

3.2 

100 

3.1 

0 

3 

100 

2.9 

0 

200 2.8 

860 880 900 920 940 960 980 1000 1020 1040 


. 

54

Wheat Protein Calibration Coefficients 

200 

Coefficient Value 

100 

0 

100 

0 

200 

860 880 900 920 940 960 980 1000 1020 1040 


. 

Products of Spectral Values and Coefficients 

+ 

∑ ( ) 

%P K 0 

n 

K n ⋅L n 

55

NIRS Calibration Visualization 

• “Simple” geometric interpretation 

• Extensible to many dimensions 

• Unifies calibration methods 

Calibration Contours: 

Surfaces of Constant Predicted Values 

% C K + K L + ⋅⋅⋅+ 

= 0 1 1 

K n L n 

0 = ( K + K L + K L 

0 

− % C) 

1 

1 

2 

2 

0 = 

B + Mx + 

Ny 

y= -(B/N)-(M/N)x y=a+bx 

56

Technicon 400 Wheat Moisture 

Calibration Contour Lines and Data 

0=(3.05-%M) + 63.4*L 1940 - 58.5*L 2230 

12% M 

K 1940 = 63.4 

Log (1/R) @ 1940 nm 

0.55 

10% M 

8% M 

No effect 

on results 

0.5 

0.4 0.45 

Log (1/R) @ 2230 nm 

K 2230 = -58.5 

Thinking in Spectral Space 

Log 1/R values shown as points in spectral space 

1680 nm 

Data shown as spectra 

0.5 

Log(1/R) 

0.4 

2100 nm 

0.3 

2180 nm 

( L 〈〉 2 , L 〈〉 3 , L 〈〉 5 ) 

0.2 

1600 1700 1800 1900 2000 2100 2200 


57

3-Wavelength NIRR Data and Calibration 

Contour Planes: 

12, 14, 16 % Protein 

1680 nm, K= -131.5 

2100 nm, 

K= -337.8 

2180 nm, K= 426.6 

L < 2> 

, L < 3> 

, L < 5> 

, ( XY , , Z12) 

, ( XY , , Z14) 

, ( XY , , Z16) 

3-Wavelength NIRR Data and 

Calibration Contour Planes: 

12, 14, 16 % Protein 

1680 nm , K= -131.5 

2180 nm, 

K= 426.6 

2100 nm, 

K= -337.8 

L < 2> 

, L < 3> 

, L < 5> 

, ( XY , , Z12) 

, ( XY , , Z14) 

, ( XY , , Z16) 

58

3-Wavelength NIRR Data and Calibration 

Contour Planes: 

12, 14, 16 % Protein 

1680 nm , K= -131.5 

2180 nm, 

K= 426.6 

2100 nm, 

K= -337.8 

L < 2> 

, L < 3> 

, L < 5> 

, ( XY , , Z12) 

, ( XY , , Z14) 

, ( XY , , Z16) 

Generalized Calibration Contours of 

Constant Predicted Values 

0 1 

= ( K −% 

C) 

+ K L + ... + K L 0 

1 

n n 

Dimensions (n) Contour Type 

1 Points 

2 Lines 

3 Planes 

>3 Hyperplanes 

59

Calibration Contours for 

Linear Equations 

• Contours are linear (straight/flat). 

• Contours are parallel. 

• Contours of regular prediction increments are 

evenly placed in spectral space. 

• Wavelength axes with zero coefficients are 

parallel to contours. 

Calibration Contours for Linear 

Equations 

• All linear calibration methods attempt to find 

the direction, spacing, and offset of the 

“calibration contours” that maximize 

sensitivity to the desired quantity while 

minimizing the effects of interfering factors. 

• Multiple linear regression, Fourier regression, 

principal components regression, and partial 

least squares regression are basically 

equivalent in that sense. 

• Each method has certain advantages for 

finding the optimum solution. 

60

Calibration Contours for 

Non-Linear Equations 

• Non-linear calibrations 

• Artificial Neural Networks 

• Support Vector Machines 

• Some data pre-treatments 

• Same as for linear calibrations except: 

• Contours are not linear (not straight/flat). 

• Contours are not parallel. 

• Contours of regular prediction increments are not 

evenly placed in spectral space. 

• The “curviness” of the contours depends on how 

non-linear the equations are. 

Statistics for Evaluating Calibrations 

61

Bias 

Bias or 

Mean Error 

N 

∑ 

i = 

1 

y pi − y 

( i ) 

N 

SD( y) 

Population 

Standard Deviation 

i 

N 

∑ 

= 

1 

( ) 2 

y i − mean( y) 

N − 1 

• Average prediction 

residuals for a data set. 

• The differences 

between predicted and 

reference values are 

“errors” or “residuals.” 

• A measure of the 

“scatter” in data set. 

y p : predicted values 

y : reference values 

N: number of observations 

Standard Error of Estimate or 

Standard Error of Calibration 

SEE 

SEC 

i 

N 

∑ 

= 1 

N 

( 

y pi − y i ) 2 

− K − 1 

• Standard deviation of the residuals within the 

calibration data set. 

• Corrected for the number of degrees of freedom by 

“K” in the denominator. K is number of coefficients. 

• Estimates calibration accuracy for unknown samples. 

62

Standard Error of Prediction 

Standard Error of Performance 

SEP 

i 

N 

∑ 

= 

1 

⎡ 

⎣ 

y pi 

− − mean y p − y 

y i 

( 

N − 1 

( ) 

2 

⎤ 

⎦ 

• Standard deviation of the residuals due to 

differences between predicted and reference 

values for samples NOT in calibration set. 

• Note exclusion of the mean difference. 

Coefficient of Multiple Determination 

“R-squared” 

N 

∑ 

R 2 i = 1 

N 

∑ 

i = 1 

( 

y pi − mean ( y ) ) 2 

( ) 2 

y i − mean( y) 

y p 

y 

predicted 

reference 

• Gives the fraction of the population variance that 

is “explained” by the regression. 

• Population variance= square of population 

standard deviation. 

63

Relative Performance Determinant (RPD) 

Estimate of Practical Value 

RPD 

SD pop 

SEC 

1 

1 − R 2 

(Approx.) 

RPD 

Practical Value 

10 Excellent 

5 Good 

2.5 Dubious (screening? 

1 

Relationship between RPD and R 2 

10 

9 

8 

7 

6 

10 

9 

8 

7 

6 

X 

RPD 

5 

RPD 

5 

4 

3 

2 

1 

4 

3 

2 

1 

X 

0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

R-squared 

0 

0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 

R-squared 

R 2 of 0.90 gives RPD of only 3 

RPD of 10 requires R 2 of 0.99 

64

Overview of Chemometric Methods 

• Spectra as Vectors 

• Multiple Linear Regression 

• Principal Component Regression 

• Partial Least Squares Regression 

• Artificial Neural Networks 

Dimensionality of Spectral Space 

• A spectrum defines a point in spectral space 

(but we can’t plot more than three 

dimensions). 

• The number of dimensions = number of 

variables (wavelengths) 

• Number of wavelengths is usually determined 

by the instrumentation. 

• How many wavelengths are needed? 

• Why not use all available wavelengths? 

65

Vector Characteristics 

Length of vector 

Normalized vector 

v 

vn 

v 

∑ ( n )2 

n 

v 

vn 1 

v 

Orthogonal vectors 

v1⋅v2 

0 

Orthonormal vectors 

vn1 1 vn2 1 vn1 ⋅vn2 

0 

Length = 1 and 

mutually “perpendicular” 

(vector product = 0) 

Basis vectors 

• A vector space is the combination of all possible 

vectors in the system considered. 

• Line: 1-D vector space 

• Plane: 2-D vector space 

• Volume: 3-D vector space 

• NIRS spectra: 10, 100, 700, etc.-D vector space 

• For n-dimensional vector space any vector (or 

any point in space) can be exactly expressed as a 

linear combination of n linearly independent basis 

vectors. That is, none of the basis vectors can be 

expressed as a linear combination of the other basis 

vectors. 

66

Multiple Linear Regression (MLR) 

y Xb b ( X T ⋅X) 

− 1 

⋅X T ⋅y 

• Mathematically extend simple linear 

regression to multiple independent variables 

• If rows or columns of X are not independent, 

the inverse “blows up” 

• High correlation between wavelengths 

(multicollinearity) makes regression noisy, 

unstable 

• Need to reduce correlation between 

independent variables 

Why reduce dimensionality 

( number of fitted coefficients)? 

• Using many independent variables leads to "overfitting" 

of the data. 

• Very large coefficients 

• Larger calibration transferability errors 

• Greater sensitivity to random noise 

• Overly optimistic performance estimates 

• General recommendations call for about 10 

independent calibration samples for each 

coefficient fitted. 

• Very large sample sets are needed to avoid overfitting 

and other problems with large numbers of 

variables. 

67

Why reduce dimensionality? 

Wheat Protein Coefficients 

1.5 . 10 4 PLS Coefficients 

1 . 10 4 

Coefficient Value) 

5000 

0 

5000 

1 . 10 4 

1.5 . 10 4 

860 880 900 920 940 960 980 1000 1020 1040 


. 

MLR with 50 wavelengths 



Multiple Linear Regression Model 

X 

Weights (B-coefficients) 

X 

Inputs 

X 

X 

∑ 

B0 

Output 

1 

68

Multiple Linear Regression 

• Find limited number of most useful 

independent variables 

• Step up 

• Step down 

• Step up, down, up, down 

• All possible combinations 

Another way: Define new “latent” variables 

(basis vectors) that are linear combinations of 

data from all wavelengths 

• Fourier regression 

• Sine and cosine functions 

• Principal components regression 

• Principal components 

• Directions in space that explain most variance 

• Partial least squares 

• Loading factors 

• Directions in space that are most highly correlated 

to constituent of interest 

• Each of these simply define orthonormal sets 

of basis vectors in spectral space. 

69

Assume that we want to 

“describe” a spectrum 

Sines and Cosines as Basis Vectors 

70

Calculate T-scores 

• Use projection (sum of products) to test “how much” of 

each basis vector is represented in each of the spectra. 

• The result is the Discrete Fourier Transform of the spectrum 

1 

SS q := 

CS q := 

〈〉 

L⋅SIFN q 

〈〉 

L⋅COFN q 

Fourier T Scores 

SS q 

CS q 

0.5 

0 

0.5 

1 

. 

1.5 

0 5 10 15 20 

q 

Factor Number 

Reconstruct the spectrum from basis 

vectors and T-Scores 

“Length” of residual 

(unexplained) spectrum 

71

Can’t we find something more efficient 

than sines and cosines to describe spectra? 

• Yes! Principal components 

• Eigenvectors of the variance-covariance matrix 

• Find the set of directions in spectral space that 

explain the most variance. 

• The first principal component is in the direction of the 

greatest variation. 

• Second is in the direction of the greatest variation 

after the first component direction is removed. 

• Etc. 

• It’s like squishing a football! 

First principal component 

72

Second principal component 

Third principal component 

73

Principal components in wavelength space 

Normalized Principal Components 

Display the final three principal component vectors. 

1 

0.5 

0 

0.5 

1 

1600 1700 1800 1900 2000 2100 2200 

PC1 

PC2 

PC3 


Principal Components in 100 Dimensions 

74

Partial Least Squares Factors: Directions most 

highly correlated with desired constituent 

Log 1/R Values, Normalized Weights, and B-Vector in Centered Log Space 

W3 

B-vector 

W1 

W2 

( LC 〈〉 0 , LC 〈〉 1 , LC 〈〉 2 ),( Wn1x, Wn1y, 

Wn1z) 

, ( Wn2x, Wn2y, 

Wn2z) 

,( Wn3x, Wn3y, 

Wn3z) 

, ( BVx, BVy, 

BVz) 

Bilinear Models 

Loading coefficients 

Sine & Cosines, PC’s, 

PLS factors, etc. 

“How much” of each basis vector 

is in the input spectrum? 

T-scores 

∑ 

Chemical 

Weights (from MLR) 

∑ 

“Raw” 

Inputs 

∑ 

∑ 

Output 

∑ 

1 

B0 

∑ 

75

Creating Bilinear Prediction Equations 

Find “chemical weights” from T-scores and 

chemistry with MLR. 

CW TS T − 1 

:= ( ⋅TS) 

⋅TS T ⋅C 

Form B-vector from basis vector matrix and 

chemical weights 

BV 

BasisVectors ⋅ChemicalWeights 

Predict chemical values from B-vector and 

spectra 

CPred 

L⋅BV 

Artificial Neural Network Models 

“Hidden” layer 

weights v i,j 

∑ 

∑ 

Non-Linear 

Transfer function 

∫ 

Output layer 

weights w j 

∑ 

1 

“Raw” 

Inputs 

∑ 

∑ 

∑ 

∑ 

1 

∑ 

∫ 

∫ 

∑ 

1 

∫ 

Output 

Pre-process to create orthogonal 

normalized input variables 

x p,i 

y p,j z p 

1 

76

Artificial Neural Networks 

• Massively interconnected “nodes” simulate 

neuron connections in the brain. 

• Inputs at each node are summed and applied 

to special non-linear “transfer function.” 

• May have multiple layers of nodes and 

weights. 

• May have multiple outputs. 

• Networks are “trained” by iterative search for 

minimum error. 

Methods for Detecting “Outliers” 

• Create calibrations to “look” for suspected 

adulterants. 

• Detect extreme examples of spectra (leverage). 

• Detect spectra that weren’t represented in the 

calibration (spectral residuals). 

• Other? 

77

1985! 

Mahalanobis Distance 

H= 2.5 

x 

2 σ 

3 σ 

1 σ 

Distances are normalized 

by the standard deviation of 

population variation in the 

specified “direction”. 

“Global” distance from center 

of population. 

“Local” distance between points 

representing different spectra. 

78

Triplets of Principal Component Scores in 

3D Space with Chemical Weight Vector 

Not Normalized 

Principal Component Scores Plotted 

Normalized 

TN i 

T i 

StDev( T) 

Global Mahalanobis Distances 

Normalized Distances from Population Center 

Global Mahalanobis Distance (normalized) 

X 

3 

2 

1 

0 

0 20 40 60 80 100 120 140 160 180 200 . 

Sample Index 

79

Maximum T-Scores 

Maximum T-Score for Sample 

15 

X 

10 

5 

0 

0 20 40 60 80 100 120 140 160 180 200 

Sample Index 

Local Mahalanobis Distances 

Comparing All Pairs of Spectra 

Sample Index 

Sample Index 

Greatest 

Distance 

Medium 

Distance 

Closest 

80

Principal Component Spectral Decomposition 

Spectral Residual (unexplained) 

(HRWW, 16 factors) 

1.4 

X 

1.2 

Sjpectral Residual (x 1000) 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 50 100 150 200 

. 

Sample Index 

81

Causes of Large Spectral Residuals 

• Instrument noise 

• Sample or instrument temperature extremes 

• Poor instrument standardization 

• Unusual sample physical condition 

• Sample adulteration 

Automatic Outlier Detection to 

Detect Adulteration 

• Instruments offer automatic outlier detection 

• Constituent ranges 

• Mahalanobis distance 

• Spectral residuals 

• Difficult to set outlier criteria to avoid false 

positives 

• Need to test outlier capabilities to detect 

adulteration 

• Need processes to follow up on “hits” 

82

Summary and Implications 

• Near-infrared spectroscopy has fostered 

many mathematical methods for dealing with 

difficult data such as found in NIRS. 

• The bilinear methods (FR, PCR, PLS) are 

fundamentally very similar except for the 

choice of basis vectors. 

• ANN calibrations can deal with greater data 

complexity and nonlinearity—at the price of 

needing many more calibration and validation 

samples. 

• Chemometric methods offer outlier detection. 

Wisconsin Center for Dairy Research 

A dairy perspective on the 

use and applications, 

challenges ahead 

USP Food Protein Workshop 

Developing a Toolbox of Analytical Solutions to Address Adulteration 

June 16 – 17, 2009 

USP Headquarters, Rockville, Maryland 

Juan Romero 

Coordinator Analytical Services 

83

Why Milk ? 

Amino 

acid 

score 

Digestibility 

% PDCAAS PER 

Egg 121 98 118 3.8 

Milk 127 95 121 3.1 

Beef 94 98 92 2.9 

Wheat 47 91 42 1.5 

International Dairy Federation 

• Membership 

• 56 Countries 

• 86% World Milk Production 

• Advisor to Codex 

• CCMMP 

• CCMAS 

• CCNFSDU 

• Analytical methods 

• ISO 

• CEN 

• DHIA 

• ICAR 

84

AFTERMATH OF THE CHINA MILK ADULTERATION 

SCANDAL 

Situation: 

• Trust in the (regional) dairy sector is suffering 

• Market requests to test dairy products for presence of 

melamine & cyanuric acid 

IDF actions aiming at (re)building trust: 

• Provision of methods to determine the presence of 

melamine & cyanuric acid in dairy products (short 

term) 

• Provision of effective and recognized approaches to 

counteract intentional adulteration (long term) 

OBJECTIVE OF THE IDF TASK FORCE 

Provide a non-prescriptive inventory of means (tools, 

procedures, methods, including screening 

techniques, etc) that: 

• can be used alone or in combination to indicate systematic 

and/or large scale adulteration of suppliers´ milk, 

• are internationally recognized as effective and appropriate, 

• are intended to assist the dairy sector in showing due diligence 

by selecting the most feasible approaches that suit the specific 

and local needs 

85

KEY PRINCIPLES IN FOOD CHAIN MANAGEMENT 

• Adequate food safety skills 

All players along the food chain should have a general understanding of the nature and impact of 

all hazards that may occur at all stages in the food chain, and an in-depth understanding of 

those hazards that need be controlled at the step(s) for which they are responsible 

• Reliable suppliers 

Suppliers of products (ingredients or raw materials) and services should only be accepted by the 

receiving food business if it has been approved and/or recognized (certified or else) as 

being able to meet agreed outputs and/or to follow agreed procedures 

• Shared responsibility 

The individual food business responsible for a particular step in the food chain has the primary 

responsibility for meeting the requirements with regard to the safety of their products and 

services, when they are supplied to the subsequent step of the food chain 

• Effective communication 

Food businesses involved in the same food chain* should establish effective communication with 

the other key players operating in the same chain 

DEVIATIONS 

Unintentional deviations 

• Caused by accidents, failures and/or ignorance 

• Foreseen/known from experience and risk assessments 

• Sporadic in nature 

• Preventive measures can be targeted (i.e. efficient) 

Intentional deviations 

• Driven by economic gains(fraud, crime) 

• Difficult to foresee 

• Systematic in nature 

• Preventive measures difficult to target (i.e. not likely to be 

efficient) 

86

COUNTERACTING FRAUD 

Assessment of vulnerability for fraud 

• Payment system 

Criminal gain related to the value of individual components of the 

payment scheme (fat, protein, snf, volume, quality parameters) 

• Milk collection infra-structure 

Number of steps involved, communication patterns, and degree of 

commitment (feeling of “ownership” and responsibility) 

COUNTERACTING FRAUD 

Preventive measures 

•Procurement strategy motivating high standards 

Examples include continuous communication, training & education (farmers & vets), 

providing advisory services, farm QA programs, etc) 

•Monitoring procedures on individual farmers milk 

– Benchmarking/trend analysis 

– Gathering of intelligence. 

Awareness of these activities reduces potential for fraud in the supply chain 

•Bio-security measures during transports 

To prevent milk from being tampered 

87

OUTLINE OF THE IDF INVENTORY 

1. Introduction 

2. Purpose, scope and use 

3. Maintaining the integrity of the food chain 

4. Assessment of the vulnerability for adulteration of 

milk 

5. Measures suitable for the purpose of counteracting 

possible adulteration 

– Preventive measures 

– Benchmarking and trend analyses 

– Intelligence gathering 

FTIR technology for routine 

milk screening 

Steve Holroyd 

Per Waaben Hansen (Foss) 

88

What is FTIR? 

• Fourier transform infrared 

spectroscopy 

• Allows rapid quantification of 

composition of complex 

matrices 

• Uses absorption of infrared 

frequency radiation 

• Fourier transform – 

mathematical function used 

for collecting data 

177 

Milk and water FTIR spectra 

178 

89

Milk – water difference spectrum 

Lactose 

Protein 

Fat 

179 

Quantification 

• The amount of infrared 

absorption is directly 

proportional to the 

concentration of an absorbing 

chemical bonds 

• Allows precise quantification 

of concentration 

• Must have reliable 

instrumentation and link to 

calibration from chemically 

known samples 

180 

90

How a rapid screening method can work 

• FTIR is already used widely for 

rapid compositional analysis of 

liquid dairy products 

Two potential methods to detect 

economic adulteration of milk: 

• Quantitative - a calibration for a 

specific adulterant 

• Qualitative – a method for 

differentiating adulterated and 

non-adulterated milk 

181 

Quantitative analysis 

• Use the relationship between absorption and 

concentration to measure specific adulterants 

• Adulterants must have different spectral signatures 

and absorption strengths 

• Detection will be dependent upon the type of 

adulterant 

• Must create a calibration set with known reference 

values 

182 

91

Difference spectrum - melamine 

Melamine dissolved in milk - FT6000 

absorbance difference 

0.1 

0.05 

0 ppm 

250 ppm 

500 ppm 

1000 ppm 

1500 ppm 

2000 ppm 

2500 ppm 

3000 ppm 

0 

1600 

1500 

1400 

1300 1200 

wavenumber 

1100 

1000 

183 

Difference spectrum – ammonium sulphate 

Ammonium sulphate dissolved in milk 


0.1 

0.05 

0 ppm 

500 ppm 

1000 ppm 

2000 ppm 

3000 ppm 

4000 ppm 

5000 ppm 

6000 ppm 

0 

1600 

1500 

1400 

1300 1200 

wavenumber 

1100 

1000 

184 

92

Difference spectrum - urea 

Urea dissolved in milk 


0.1 

0.05 

0 ppm 

250 ppm 

500 ppm 

1000 ppm 

1500 ppm 

2000 ppm 

2500 ppm 

3000 ppm 

0 

1600 

1500 

1400 

1300 1200 

wavenumber 

1100 

1000 

185 

FTIR to quantify melamine in milk 

Added melamine, ppm 

1000 

900 

800 

700 

600 

500 

57 samples in 3 replicates 

%CV: RMSEP=8.31 SEP=8.33 SEPCorr=8.35 SDrep=3.77 

Slope: 1.0013 

Intcpt: -1.8636 

r: 0.9969 

Bias : -1.4116 

400 

300 

200 

100 

0 

0 100 200 300 400 500 600 700 800 900 1000 

186 

FT-IR detected melamine, ppm 

93

Quantification of specific adulterants 

• With the current generation of FTIR instruments, the 

melamine LoD (Limit of Detection) is 75-100 ppm 

(0.0075-0.0100 %) 

• Hurdles for use in routine screening 

– The adulterant must be known 

– A large number of samples must be collected to 

ensure robustness over time 

– Good reference results must be available 

187 

Qualitative analysis 

• Assess the “quality” of normal milk via FTIR spectra 

• Use statistical techniques to distinguish variation 

greater than “routine” 

• Principal component analysis – PCA 

– A data reduction technique 

188 

94

2000 FTIR spectra of liquid milk 

189 

Principal component analysis 

• An orthogonal linear transformation 

• Separates out the major features of variation from a 

large data matrix 

• Transforms a large number of possibly correlated 

variables (spectra) into a smaller number of variables 

called principal components 

• Plot principal components against each other and 

establish trends 

190 

95

Deliberately adulterated milk by FTIR and PCA 

0.15 

0.1 

• Normal milk 

• Adulterated milk 

Melamine+water 

Cyanuric acid 

Score PC5 

0.05 

0 

Urea 

Melamine 

-0.05 

Ammonium sulphate 

-0.1 

191 

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 

Score PC2 

FTIR for qualitative analysis 

• Gather spectral information from ”normal milk” 

– Critical that no adulteration is present in such data sets 

• No reference measurements needed 

• Spectra from unknown samples, deviating from ”normal 

milk”, can be identified to a certain level 

• The LoD for un-specific adulteration is higher than for 

specific adulteration 

– The LoD for melamine as unknown adulterant is 250- 

500 ppm (0.025-0.050%) 

192 

96

Essential features 

• FTIR must be in widespread and effective routine use 

– Instruments properly maintained 

– Calibrations up to date 

• Sensitivity to potential adulteration must be 

demonstrated 

• Calibration models may be created to cover individual 

sample types, sites, regions, countries, breeds. Some 

current composition models are global in nature 

193 

Challenges 

• FTIR not sensitive at low concentration levels – will 

not detect trace adulteration or contamination 

• Knowledge of normal variation vs variation due to 

adulteration 

• Must ensure unadulterated milks used to define 

“normal” and data must cover routine variation in milk 

spectral data 

• Must keep model up to date with new “normal” data 

194 

97

FTIR Summary 

• FTIR is an excellent rapid tool for determining liquid 

milk composition 

• FTIR can be used to detect economic adulteration of 

milk using commercially available equipment 

• Quantitative analysis 

– Requires knowledge of the adulterant 

• Qualitative analysis 

– Requires an understanding of FTIR variation in 

“good” milk 

195 

The Future of FTIR 

• A robust network of FTIR instruments measuring milk 

• Each instrument compares the spectrum of every 

sample to a spectral database of normal milks 

• A red flag appears if any samples deviate 

• A mechanism is in place to identify an causes of 

deviation 

196 

98

IDF-ISO Guideline to the measurement of MEL and 

CYA 

• Guideline to the measurement of Melamine and 

Cyanuric Acid 

• LC-MS/MS 

• Performance Criteria 

– 2002/657/EC. Performance of analytical 

methods and the interpretation of results 

– ISO 11843. Capability of detection – Parts 1-5 

LC-MS/MS 

• Any method which combines either high performance liquid 

chromatography (HPLC) or ultra performance liquid 

chromatography (UPLC) with a triple quadrupole mass 

spectrometry instrument. 

• Ionization shall be performed by either electrospray (ESI), 

atmospheric pressure chemical ionization (APCI), atmsopheric 

pressure photospray ionization (APPI) or any other ionization 

mode with appropriate performance. 

• The acquisition mode shall be carried out in the selected 

reaction monitoring (SRM) mode. The quantification of both 

melamine and cyanuric acid is based on an isotope dilution 

approach using isotope stable internal standards for both 

analytes. 

99

A dairy perspective on the use and 

applications, challenges ahead 

Questions 



USP Headquarters, Rockville, Maryland 

USP Meeting Center 

Wednesday, June 17, 2009 

6a. Breakout Session A 

Possibilities of FTIR and NIR for the detection of adulteration 

in food and feed 

By 

Jürgen Möller 

FOSS Analytical, Sweden 

Dedicated Analytical Solutions 

100

TCD – Team Chemometric Devopment contribution 

”FT-IR for the detection of adulteration in milk” 

Per Waaben Hansen, Ph. D., R & D Scientist, 

FOSS Analytical A/S, Denmark 

”NIR possibilities for the detection of adulterants” 

Martin Lagerholm, Ph.D., R & D Scientist 

FOSS Analytical AB, Sweden 


Where we come from… 

Höganäs 

• …. 

Hilleröd 

Copenhagen 


101

Adulteration of milk 

• Milk trade is often based on weight 

- Fraudulently added water is a general problem 

• Authorities therefore check for added water by analysing for a protein decrease 

- Kjeldahl (total N) is used 

• Kjeldahl detects all types of N – i.e. not only protein 

- Melamine is rich in N and therefore a popular adulterant 

- 0.3 % added melamine corresponds to an artificial protein increase of 

approx. 1.2 % 


Consumer protection and prevention of adulterations 

• Detection of adulterants at contamination levels is 

obviously important for consumer protection and 

tracking of adulterations 

• For the prevention of adulteration fast and simple 

analytical solutions are needed to detect fraud already 

at collection points and payment stations for the raw 

material 

• Different analytical challenges? 

- Contamination levels: ppb – ppm 

- Fraudulent addition levels: 0,1% - 1% 


102

Melamine 

66% Nitrogen 

”Protein” content = >400% 

Solubility in water: 3,2 g/l 


FTIR spectra of Melamine 



0.1 

0.05 

0 ppm 

250 ppm 

500 ppm 

1000 ppm 

1500 ppm 

2000 ppm 

2500 ppm 

3000 ppm 

0 

1000 1100 1200 1300 1400 1500 1600 

wavenumber 


103

FTIR spectra of Urea 

Urea dissolved in milk - FT6000 


0.1 

0.05 

0 ppm 

250 ppm 

500 ppm 

1000 ppm 

1500 ppm 

2000 ppm 

2500 ppm 

3000 ppm 

0 

1000 1100 1200 1300 1400 1500 1600 

wavenumber 


FTIR spectra of Ammonium sulphate 

Ammonium sulphate dissolved in milk - FT6000 


0.1 

0.05 

0 ppm 

500 ppm 

1000 ppm 

2000 ppm 

3000 ppm 

4000 ppm 

5000 ppm 

6000 ppm 

0 

1000 1100 1200 1300 1400 1500 1600 

wavenumber 


104

FTIR: Adulterants have different mid-IR signatures 


Urea dissolved in milk - FT6000 


0.1 

0.05 

0 ppm 

250 ppm 

500 ppm 

1000 ppm 

1500 ppm 

2000 ppm 

2500 ppm 

3000 ppm 


0.1 

0.05 

0 ppm 

250 ppm 

500 ppm 

1000 ppm 

1500 ppm 

2000 ppm 

2500 ppm 

3000 ppm 

0 

1000 1100 1200 1300 1400 1500 1600 

0 

1000 1100 1200 1300 1400 1500 1600 

wavenumber 

wavenumber 

Ammonium sulphate dissolved in milk - FT6000 

0 ppm 

500 ppm 

1000 ppm 

2000 ppm 

3000 ppm 

• Adulterants have different spectral 

signatures, making it easy to distinguish 

even related compounds 


0.1 

0.05 

4000 ppm 

5000 ppm 

6000 ppm 

• Adulterants show different absorption at 

similar concentrations – detection limits 

are therefore dependent on the adulterant 

0 

1000 1100 1200 1300 1400 1500 1600 

wavenumber 


Example: Limit of detection is lower if the adulterant is known 

1000 

900 

800 

57 samples in 3 replicates 

%CV: RMSEP=8.31 SEP=8.33 SEPCorr=8.35 SDrep=3.77 

Slope: 1.0013 

Intcpt: -1.8636 

r: 0.9969 

Bias : -1.4116 

Added melamine, ppm 

700 

600 

500 

400 

300 

200 

100 

0 

0 100 200 300 400 500 600 700 800 900 1000 

FT-IR detected melamine, ppm 

• With the current generation of FT-IR instruments, the melamine LoD (Limit of Detection) is 75-100 ppm 

(0.0075-0.0100 %) 

• However, 

- The adulterant must be known (in this case melamine) 

- A large number of samples must be collected to ensure robustness over time 

- Good reference results must be available 


105

FTIR: Spectral integrity / Principle Component Analysis (PCA) 

0.15 

• Normal milk 

• Adulterated milk 

Cyanuric acid 

Score PC5 

0.1 

0.05 

0 

Melamine+water 

(diluted samples) 

Urea 

Melamine 

-0.05 

-0.1 

This particular score combination (PC 2 vs. 

PC 5) is good for detecting cyanuric acid or 

ammonium sulphate; other combinations 

can be used for detecting adulteration caused 

by melamine or urea 

Ammonium sulphate 

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 

Score PC2 


FTIR: Spectral integrity 

• By reasonable choice of ”factor” (% of unexplained variance) and ”threshold” (determines 

the detection limit and the number of acceptable false positives) PCA calibrations can 

successfully detect ”unnormal” samples 


106

Spectral integrity in general (FTIR) 

• Only spectral information from ”normal milk” is needed 

- Critical that no adulteration is present in such data sets 

• No reference measurements needed (unless required to confirm that 

the milk samples are un-adulterated) 

• Spectra from unknown samples, deviating from ”normal milk”, can be 

identified 

• The reason for the deviation must be identified by alternative means 

• Models can be created to cover individual sample types, sites, 

regions, countries, depending on relevance 


Spectral integrity, ctd. 

• The LoD (defined as 3xSEP) for un-specific 

adulteration is higher than for specific adulteration 

- The LoD for melamine as unknown adulterant is 

250-500 ppm (0.025-0.050 %) 

- The LoD for melamine as a known adulterant is 75- 

100 ppm 

• The LoD for other adulterants will be different from the 

LoD for melamine, and will depend on the absorptive 

intensity of the adulterant and on how much the 

spectrum differs from that of milk. 


107

NIR spectra of pure Melamine and skim milk powder 

• Melamine 

• Skim milk powder 


NIR example: PCA plot incl adulterated samples 

Cal set 

Test set 1 

Test set 2 


108

NIR: Validation of melamine calibration 

Values in 

% melamine 

• Test set 1: 27 samples, SEP 0.03 


NIR: Validation of NIR calibration 

Values in % melamine 

• Test set 2, 144 samples, SEP 0.05 


109

NIR 

• Spiking experiments indicate a LoD of about 0,1 % 

melamine in milk powder 

• Using this LoD, a computer simulation of a database 

of 5000 spectra gave no false positive results 

• Using a similar spectral integrity technique as 

described above might be used to identify deviating 

patterns at higher levels, e.g. 0,3% melamine 


Other sample types 

• Soybean meal 

- At levels around 45% protein Dumas and Kjeldahl methods show a standard 

deviation of reproducibility of about sdR = 0,4% protein, corresponding to 

about 0,1% melamine 

• Wheat flour 

- At levels around 13% protein the N-based methods show a standard 

deviation of reproducibility of about sdR = 0,2% protein, corresponding to 

about 0,05% melamine 

• Fraudulent additions of adulterants are supposingly made at levels above the 

measurement uncertainty of current N-based methods 

• Screening for adulterants at these levels seems to be feasable with NIR 


110

Conclusions 

• FTIR and NIR are usually optimized for measuring quality 

parameters such as protein, fat etc 

• As these platforms are fast, cost-effective and widely used in the 

dairy and food processing industries it is of interest to further 

evaluate the extent to which they can be used to detect adulterations 

• Specific calibrations maybe developed and used for known 

adulterants 

• The analysis of spectral integrity offers a good chance of detecting 

higher levels of a broad range of adulterants, also of unknown nature 

• Spectra and reference data of unadulterated samples must be 

available 

• FTIR and NIR should only be used for screening at the raw material 

source or intake and should be complemented with other methods 

for final prove 


“Advanced” Vibrational 

Spectroscopic Techniques for the 

Investigation of Adulterants 

Peter R. Griffiths 

Department of Chemistry 

University of Idaho 

Moscow, ID 83844-2343 



June 16 and 17, 2009: Breakout Session: “Theory and application 

of rapid mid-infrared, near-infrared and Raman methods and 

chemometrics to measure protein and prevent adulteration.” 

111

Mid-Infrared Spectroscopy: 

Attenuated Total Reflection 

Spectroscopy 

Melamine in infant formula 

Low-Concentration Melamine Detection with the IdentifyIR Diamond ATR Spectrometer, S. 

Sumair, P. E. Leary and J. A. Reffner, Spectroscopy Application Notebook, Feb 1, 2009. 

112

Detection limits: melamine in infant 

formula 

Low-Concentration Melamine Detection with the IdentifyIR Diamond ATR Spectrometer, S. 

Sumair, P. E. Leary and J. A. Reffner, Spectroscopy Application Notebook, Feb 1, 2009. 

Mid- and Near Infrared Diffuse 

Reflection Spectroscopy 

113

NIR/DR Spectrum of Pure Melamine, 

6 nm resolution 

Courtesy of Eli Margalith, Opotek Corporation 

NIR/DR Spectrum of Pure Melamine, 



114

NIR/DR Spectra of Pure Gluten 




NIR/DR Spectra of Melamine 

and Infant Formula 

L. J. Mauer et al. J. Agric. Food Chem., 2009, 57 (10), pp 3974–3980 

115

Mid-Infrared Spectra of 

Melamine and Infant Formula 

Diffuse reflection 

spectra (no 

dilution in KBr) 

ATR spectra 

L. J. Mauer et al. J. Agric. Food Chem., 2009, 57 (10), pp 3974–3980 

Raman Spectroscopy 

116

RTA’s Portable Raman Analyzer 

Sample Compartment 

Advantages: 

• No sample preparation 

• Simple integration via fiber optics 

• Remote analysis, multi-component 

• Complete spectral coverage 

• Wavelength stability 

• Confident spectral subtraction 

• and library search/match 

• Real-time, On-demand analysis 

• Long term stability 

• Temperature and vibration immune 

• Shock resistant 

• 25 Pounds 

• 5 hour battery 

Providing Chemical Information When & Where You Need It 

Raman Spectra of Baby Milk Formula 

785 nm Laser 

1064 nm Laser 


117

1% Melamine in Baby Milk Formula 

1% in Formula 

Pure Formula 

Difference spectrum 

Pure Melamine 


Surface-Enhanced Raman 

Scattering (SERS) 

118

Surface-Enhanced Raman Scattering 

(SERS) 

Enhancement of the 

Raman spectrum of 

molecules on the 

surface of roughened 

silver or gold metal 

plates, colloids or 

nanoparticles. 

5-nm thick layer of silver 

SERS Spectrum of a Self-Assembled Monolayer 

of p-Nitrothiophenol on Silver Surface 

Produced by Physical Vapor Deposition (10 s) 

Counts (Normalized and Corrected) 

2000 

1000 

0 

2000 

1500 

1000 

Raman Shift / cm -1 

500 

119

SEM of Klarite 

Melamine on Klarite by SERS 

Aliquots of melamine 

solutions were applied to 

Klarite and the SERS 

spectrum measured in 

10 s (20 mW, 785-nm 

laser.) 

A. 10 -2 M; 

B. 10 -3 M; 

C. 10 -4 M; 

D. 10 -5 M; 

E. 10 -6 M; 

F. 10 -7 M; 

G. 10 -3 M on bare gold 

He et al., Sens. & Instrum. Food Qual. (2008), 2:66-71. 

120

Quantitative Result 


SERS Spectra of Melamine and Cyanurate 

1 x 10 -3 M melamine 

1 x 10 -3 M cyanuric acid 

Solid cyanuric acid (not SERS) 

Mixture of 1 x 10-3 M 

melamine and 1 x 10-3 M 

cyanuric acid on Karite 


121

4 Ag + Ge 4+ 

4 e - 

Ag + Ge 4+ 

e - 

Ag + 4 e - 

Ge 4+ 

e - 

“Electroless Deposition” 

AgNPs Formed by Electroless Deposition 

1 mM AgNO 3 

20 min 10 mM AgNO 3 

3 min 

10 mM AgNO 3 

15 min 10 mM AgNO 3 

24 min 

122

AuNPs Formed by Electroless Deposition 

SERS of Melamine 

0.5 PPM in Solvent 

(X20) 

Glass 

Luminescence 

5 PPM in Solvent 

250 PPM extracted 

from Baby Formula 

Simple SERS 

Sample Vial 


123

Mid-Infrared Hyperspectral Imaging 

Either the transmission (thin section) or ATR mode 

Wheat Kernel 

124

Wheat Kernel 

Feline Kidney Tissue 

125

λ 

40 x 40 µm 

FT and 

Background Subtraction 

FPA detector 

128x128 pixels 

Absorbance 

Wavenumber cm -1 

IR Spectra from Multiple Kidney Crystals 

Crystal 

Surrounding 

tissue 

Absorbance 

Sample 

3500 3000 2500 2000 1500 1000 

Wavenumber (cm -1 ) 

•Most of the crystals in the kidney tissue are the same material 

• Kidney crystals inconsistent with pure crystalline melamine 

•Hypothesis = melamine is paired with something 

melamine cyanurate 

Absorbance 

Wheat Gluten 

Particle 

Kidney 

Particle 

Urine 

Crystal 

Melamine 

cyanurate 

3500 3000 2500 2000 1500 1000 

Wavenumber (cm -1 ) 

• Crystals recovered from wheat gluten, kidney tissue, 

and urine samples match melamine cyanurate 

126

Section of the extensive two-dimensional 

hydrogen bond network (dashed) between 

melamine (blue) and cyanuric acid (red) 

NIR Hyperspectral Imaging 

(usually in the diffuse reflection mode.) 

127

Why Use Near-IR Imaging? 

• Universal –works with all organic molecules 

• Reflection or transmission modes 

• Thick samples (no sample preparation) 

• Large range of spatial resolutions 

• Low cost room temp. large format high performance arrays 

• Rugged, no‐moving parts 

• Real‐time capability 

• Technology driven by communications industry 

Spectral Imaging as a Data Cube 

A spectrum is collected at every pixel 

in a 2‐dimensional image, resulting in 

3‐dimensional cube of data: x, y, λ 

(x, y) = x, y spatial description 

(z) = λ spectral description 

Wavelength (λ, nm) 

Image Pixel (y) 

Two ways to think of this: 

• Each pixel in the image stack results in a full spectrum at that point. 

• Each image is recorded at a different wavelength of light. 

Image Pixel (x) 

128

Current Technology for NIR 

Hyperspectral Imaging 

• “Push‐Broom” or “Stare‐Down” configurations 

• The presentation is focused on “Stare‐Down” 

• Broadband light sources to illuminate the target then filter 

the reflected light 

• Tunable filters (e.g., LCTF or AOTF): ~6 nm spectral resolution 


Broadband Light Sources 

• Slow Data Collection 

• Dark noise, low Signal to Noise 

• Small Field of View (FOV) 

• Limited Spectral Resolution 

• Generation of Heat, may be harmful to the sample 


129

Concept of Using Tunable Laser 

for Hyperspectral Imaging 

• The tunable laser (Optical 

Parametric Oscillator ‐ OPO) scans 

the NIR wavelength range 

• The light is transmitted via fiber(s) 

to the target 

• The reflected light is collected by 

an IR camera 


Tunable Laser (OPO) Illumination 

Attribute Effect Advantage 

Wide Tuning 

Range 

Narrow Spectral 

Linewidth 

410 – 2400nm 

High Spectral Resolution 

(1 –2.5nm) 

Short Pulse (5ns) Short Integration Time Camera Gating 

Not affected by Ambient Light 

High Intensity Single frame data acquisition Short scan time 

Large FOV 

Low Avg. Power Sample not subjected to heat No sample damage 

Wavelength 

Measurement 

Real‐time calibrated wavelength 

High spectral accuracy 

Fiber Delivery Easy and efficient fiber delivery Flexible configurations 


130

Opotek HySPEC TM System 

Camera: 

FOV: 

Wavelength: 

Spectral 

Resolution: 

InSb or InGaAs 

640x512 or 320x256 

13 –50 mm, 20 cm 

1000 – 2400 nm (InSb) 

1000 – 1700 nm (InGaAs) 

1 ‐ 3nm 


Melamine in Gluten 


Image of Scores on PC 4 (3.10%) 



50 

50 

50 

100 

100 

100 

150 

150 

150 

200 

200 

200 

250 

250 

250 

50 100 150 200 250 300 

50 100 150 200 250 300 

0% 0.05% 0.5% % !% 1% 

50 100 150 200 250 300 





50 

50 

50 

50 

100 

100 

100 

100 

150 

150 

150 

150 

200 

200 

200 

200 

250 

250 

250 

250 

50 100 150 200 250 300 

50 100 150 200 250 300 

50 100 150 200 250 300 

50 100 150 200 250 300 

2% 3% 4% 5% 


131

30 ppm Melamine in Wheat Gluten 

Image of 1454 

50 

100 

150 

200 

9000 

8500 

10 frames, 1 nm step 

2 of 64,074 pixels 

250 

300 

8000 

350 

400 

450 

Mean 

7500 

7000 

500 

100 200 300 400 500 600 

6500 

6000 

1300 1350 1400 1450 1500 1550 1600 

Wavelength 

Raman Hyperspectral Imaging 

132

Chemical Imaging Instrumentation 

Falcon II TM , Condor TM and Macro Raman Chemical Imaging 

Wide‐field Chemical 

Imaging offers: 

• High Spatial and spectral 

resolution 

• Little to no sample 

preparation 

• Nondestructive analysis 

• Fast, automated acquisition 

• Multiple imaging 

modalities 

• Near-IR 

• Fluorescence 

265 

• Colorimetric 

System 

Macro Raman Chemical 

Imaging System 

Falcon II TM 

Condor TM 

Food & Beverage Adulteration 

Raman Chemical Imaging for the Detection of Melamine 

670 cm ‐1 

Brightfield Reflectance Raman Chemical Image Brightfield/Raman Fusion Image 

670 cm ‐1 

Raman Intensity 

Average spectra from region 1 

Average spectra from region 2 

600 700 800 900 1000 

Raman Shift (cm ‐1 ) 

Y. Liu, K. Chao, M. Kim, D. Tuschel, O. Olkhovyk and R. Priore “Potential of Raman spectroscopy and imaging 

266methods for rapid and routine screening of the presence of melamine in animal feed and foods,” Appl. Spec., 

63(4), 477-480 (2009). 

133

Macroscopic Raman Chemical 

Imaging 

Melamine Identification in Wheat Flour 

Brightfield Reflectance (880 nm) Raman Chemical Image Brightfield/Raman Fusion Image 

267 

Courtesy of Ryan Priore, ChemImage Corp. 

Condor TM NIR Chemical Imaging 


100 % 

0 % 

0.2 % 0.5 % 

1.0 % 

3.0 % 

6.0 % 

Calibration 

Validation 

268 

6.0 % 3.0 % 

Raw NIR image 

1.0 % 

0.5 % 

0.2 % 

100 % 

0.25 in 

• Six wheat flour samples were prepared with 

melamine concentrations ranging from 0.0 – 6.0 % 

• Samples were placed in a 96-well plate in two 

groups: Calibration (model development) and 

Validation (model challenge) sets 

0 % 


134

Condor TM NIR Chemical Imaging 


Mixture Spectra 

(Calibration) 

Calibration 

Spectra ROIs 

269 

1 st Derivative Absorbance (1490 nm) 

0.25 in 


Falcon II TM Fusion Imaging via CIdentify TM 

Melamine Identification in Skim Milk Powder (w/ HDPE interferent) 

270 

• Raman and NIR imaging data sets may be fused together for 

improved accuracy in class discrimination 

– Raman possesses higher specificity than NIR but requires a 

longer integration time than NIR 

– NIR alone may not offer 100% discrimination due to 

spectroscopic similarities between the analyte and matrix 

• Can be used for either macroscopic or microscopic data 

analysis and provides a visual output for decision making 

• Useful for determining intentional vs. unintentional 

adulteration identification 

– melamine is a known intentional adulterant 

– HDPE may be an unintentional adulterant from a can liner 


135



Melamine & HDPE 

Melamine & Skim Milk Powder 

Brightfield Reflectance 

50 µm 50 µm 

Brightfield Reflectance 

HDPE 

Melamine 

Skim Milk Powder 

Concatenated ROIs 

Ground Truth 

50 µm 

271 




272 

• Pure components were measured using Raman 

and NIR over identical fields of view; 

• Raman full spectral range was measured; 

• NIR absorbance data was converted to 1 st 

© ChemImage Corporation 2009. All Rights Reserved. ChemImage Products and Services are protected by U.S. and International issued and pending patents. 

derivative and cropped from 1350-1600 nm. 

136

Acknowledgements 

• Curtis Marcott, Light Light Solutions 

• Eli Margalith, Opotek Corporation 

• Stu Farquharson, Real-Time Analyzers 

• Ryan Priore and Pat Treado, ChemImage Corporation 

137

Classification Technology 

Changing from diagnosing calibrations to 

diagnosing products 

Dr. David Honigs- Perten Instruments, 

Springfield, IL 

Two Types of Classification 

• Similarity -Is this sample similar to other samples? 

• Dissimilarity - Is this sample unlike another samples? 

• Samples are always both similar and 

dissimilar in some respects. 

– e.g.. an apples and oranges are placed together in the 

breakfast fruit basket. However, they are as different 

as “comparing apples to oranges”. 

138

Similarity 

• Looking at similarity of vectors 

Correlation (R) 

Melamine in Correlation 

Gluten (%) Coefficient 

0.000 0.9996 

0.001 0.9996 

0.005 0.9996 

0.010 0.9996 

0.050 0.9996 

0.100 0.9996 

0.500 0.9996 

1.000 0.9995 

5.000 0.9981 

10.000 0.9954 

100.000 0.7784 

139

Going From ‘How Similar?’ 

to 

‘How Different?’ 

• Similarity is measured by cross multiplication 

(correlation) 

• Differences are measured by subtraction 

• Generally express the difference as a root 

mean square sum (RMS) difference 

Looking At Differences 

140

RMS Difference as an Indicator of 

Contamination 

Melamine RMS Diff. 

0 0.010 

0.001 0.111 

0.005 0.119 

0.01 0.122 

0.05 0.125 

0.1 0.121 

0.5 0.113 

1 0.132 

5 0.538 

10 1.285 

100 55.166 

Is the difference abnormal or an 

adulteration 

• What is a normal difference? 

– This requires more than 1 sample and subtraction. 

141

How to View Atypical Differences 

• Need to subtract several spectra 

• The problem is that the different spectra are so 

very similar to one another. 

Create Ideal Sample Spectra to 

Subtract (Loadings) 

142

Sample Scores are typically 

calculated and measured already 

• H statistic, M distance, Leverage 

M, H, or Leverage Measure the 

Distance from the Mean of the Data 

143

Typical NIR Manufacturer 

• If a sample has a large distance, don’t trust the 

answer and send it to the lab. Use it for future 

calibration. 

• In calibration the first step is to throw out the 

high M distance samples and use the rest. 

Distance Depends on What is 

Called ‘Typical’ 

144

Pet Food Melamine Contamination 

• Measured by NIR, showed high M distances, 

sent to wet lab, showed reasonable 

Kjeldahl/Dumas results. 

• The apple-cart is upset because of human 

intervention. The odds of an accidental 

contamination matching the Kjeldahl/Dumas 

techniques are slim. 

• Someone has practiced to make oranges look 

like apples in the eye of this beholder. 

First Things to Fix 

• The M distance is not commonly used for 

product qualification because the calibrations 

are not fully trusted. 

• Best practices are that one must find the 

reason for the high M distances. 

• This becomes challenging on evenings, 

weekends and with just-in-time product flow. 

• Ultimately, one must separate out high M 

distances caused by contamination from those 

caused by instrumentation, calibration and 

operator error. 

145

Qualify before Quantify - SIMCA 

• Create Several Categories of Materials and 

Measure the Distance to Them. 

• Each distance is measured based on that own 

model ellipse. 

SIMCA Analysis of Gluten 

Samples 

146

Gluten and Melamine Models 

Discrimination Power 

Similarity/Dissimilarity 

• Looking at dissimilarity of vectors 

147

A Commercial Set of 45 Corn 

Gluten Samples 

Harmonized Spectra of Corn 

Gluten Data Set 

148

Conclusion 

• Changing the standard practice response to 

an incorrect M Distance onexisting equipment 

would do a lot 

• One can easily add classification testing for 

modest cost and effort to current testing. 

• Both similarity and difference techniques are 

useful. One does not have to use only one. 

• These Classification techniques are applicable 

to NIR, FTIR, or other spectral signatures of 

the products. 

149

Breakout Session A - US Pharmacopeial Convention

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