3. Spectroscopy
Ferenc Firtha
Corvinus University of Budapest
Faculty of Food Science
Department of Physics and Control
Place of spectroscopy
1. Colour: what like?
quick, but contact :
-> average RGB/Lab/Lch
3. Spectroscopy: what?
contact + statistical analysis:
NIR -> water, fat, oil, protein,…
2. Image processing: where?
remote sensing + data reduction:
-> position: colour, shape, pattern
4. Spectral imaging: where and what?
remote + stat. analysis + image processing
-> position: distribution of compounds
Light as
Electromagnetic wave:
ν excitation frequency + c velocity  λ wavelength:
c   v
~ 300 000 km/s
Some ranges:
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•
•
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radio (30kHz–30MHz):
λ big
TV (50-1000MHz), GSM (380-1900MHz)
radar / WiFi, LAN, SAT
microwave oven (water: 18-27GHz)
IR, VIS, UV
X-ray (<1nm), gamma (<1pm): E big
30MHz
300MHz
3GHz
30GHz
300THz
300PHz
Photon:
Quantum theory: Energy of wave packet and
ν frequency:
E  hv
Mass:
E  m  c2
h: Planck constant
Electromagnetic ranges
Interactions of light
Measuring:
Absorption:
of chemical components
Transmission:
getting through
Transmission  Absorbance
Reflection:
specular (reflexió)
diffuse (remisszió)
scattering (physical properties)
Reflection
Emission:
after inducing (atomic level)
 Absorbance
Fraunhofer lines (1814)
absorption lines in sunlight
 thousands of lines
Transmission spectrum of blue sky
Explanation: energy levels of hydrogen atom:
- emission spectra
- absorption spectra, absorption lines
1. Atomic emission spectroscopy
Flame or inductively excited atoms and ions emit EM radiation.
Spectrum is characteristic to the different energy levels of electrons, atomic components
emission spectra
of elements (VIS)
2. Refraction
• refraction of X-ray or electron ray
CT: Computed Tomography
3D type of x-ray by examining slices from various angle
3. Raman spectroscopy
sample is illuminated with a laser beam
radiation from the illuminated spot is collected
wavelength of laser (Rayleigh scattering) is filtered out
shift of frequency is measured
Energy-level diagram (line thickness
is proportional to the signal strength)
4. Scattering
• Scattering image of laser beam
is characteristic to the physical structure,
like cell walls, rheological properties
5. Absorption spectroscopy
Let’s go back to the sunlight: there are also valleys, not only absorption lines
Explanation
In a molecule, the atoms can rotate and vibrate respectively to each other.
These vibrations and rotations also have discrete energy levels, which
can be considered as being packed on top of each electronic level.
Water absorption:
- electronic:
UV < 200nm
Intramolecular transitions restricted by hydrogen bonds:
- vibrations:
IR 1µ-10µ
- rotations
FIR 10µ-1mm
- intermol. vibrations
MW 1mm-10cm
1. Spectral lines are broader causing overlap of many of the absorption peaks
2. Overtones and combinations also appear
VIS 380-780nm
flavonoid,
anthocyanin
blackberries, red
grapes, red cabbage,
red onions, beets,
radishes
carotenoid
carrot, tomato, lemon,
orange, spinach, corn
quinone
mushroom
pyrrole
chlorophile
melanin
skin
Chem: http://www.kfki.hu/chemonet/hun/eloado/kemia/festek2.html
Kép : http://www.healthymoncton.com/taste-the-rainbow-why-we-want-to-eat-fruits-veggies-from-all-of-the-colours-of-the-rainbow/
VIS
Cranberry juice
(anthocyanin)
β,β-carotene degradation
chlorophyll
melanin
NIR range (NIR: 780-2500nm, MIR: 2,5-15µm, FIR: 0,015-1mm)
Absorption comes from the O-H, C-H, N-H bonds:
water, hydrocarbon, lipid, protein, alcohol, etc.  Food Science
NIR 900–1700nm
OH:
970, 1450, 1980
Fiber:
1100, 1300, 1350, 1403, 1483, 1500, 1534
Cellulose:
1490
Lignin (wood): 1170, 1410, 1417, 1420, 1440
water free / bound:
HDW (hydrofil) / LDW (hydrofob)
alcohol:
metil,etil,propil…
aromatic alcohol:
e.g. benzyl
aromatic hydrocarbon:
protein
amid, amin
e.g. benzene
lipids:
triacilglicerin (fat)
secondary amid
Absorbance: Lambert-Beer law
I  I 0  e k (  )cd
  k 2.303
I  I 0 10
 (  )cd
ε: molar absorptivity (moláris abszorpciós tényező)
Absorbance is proportional:
to length (Bougue, 1729)
and concentration (Beer, 1852)
I0
A  lg
IT
A   cd
How to measure absorption?
1.Absorption spectroscopy:
Transmittance:
IT
T
I0
A  lg
I
1
 lg 0
T
IT
let’s suppose that reflectance is zero
2. Reflection spectroscopy:
Absolute reflectance: all reflected / incidence
IR
R
I0
I0
1
A  lg  lg
R
IR
Reflectance (reflection factor): sample / standard
Ix
R
I0
I0
1
A  lg  lg
R
Ix
Questions: non-homogen  grain inspected
uneven surface  sample rotated
Reflection spectroscopy
geometry:
• 45/0 (illumination/observation)
• d/8
angle of view: usually 2 or 10 degree
Reflectance standards (VIS..NIR)
d/8 geometry
Instrumentation
Snell (1620) refraction
Newton (1666) spectrum: birth of spectroscopy
Bougue, Lambert (1729) absorbance
1. Spencer spectrometer (1868),
spectroscope
Hartley (1880) chemical analysis of mixtures
Beer (1852)
A gas jet (C) is positioned at the right hand side of the
picture along with a sample holder (B).
In the foreground a candle (F) is illuminating an
arbitrary scale that is reflected off the back surface of
the prism and is superimposed on the spectrum when
viewed through the telescope.
Some laws of spectroscopy
Kirchhoff (1860)
1. radiation of solid is continuous (black body)
2. radiation of gas consist of lines
3. solid in gas: has missing lines
Stefan (1879)
black-body radiant exitance is proportional
to the fourth power of its Th. temperature
Wien (1896)
Displacement is inversely proportional to
the temperature. Distribution:
Planck (1900)
describes the complete spectrum of
thermal radiation:
2. Spectrophotometer
Light source:
electrically heated Nernst glower ()
Mirror,lens,cuvette: alkali-halid glass (alkáli-halogenidből)
Monochromator:
grating
Detector:
thermal / pyroelectric / photoconducting
3. FT-NIR (Fourier transform infrared spectroscopy)
Interferometer:
interferogram
Combination of wavelengths  interferogram
Responses to different combinations  recontruction of spectrum
How to process spectrum?
normalization: get spectra set to same level
• Standard Normal Variate (SNV): subtract mean and devide by variance
smoothing: before differentiation
• Moving average
• Savitzky-Golay: polinomial regression
derivatives: to eliminate shift of peaks
1. kind of normalization
2. curvature (görbület)
assignation: of compounds
- statistical models, like PLS, DA
- artificial neuron network
Statistical analysis of spectral data
a.) Principal Component Analysis (PCA): Dimension reduction (not supervised)
Finds the main axes (eigenvalues) of data space, those separate best data points.
These PCs come as the linear combination of n dimensional source space.
b.) Fisher’s Discriminant Analysis (FDA): Dimensionality reduction and classification
Finds a linear combination of features, which separates two or more classes.
Steps: finds linear/quadratic classifier -> dimensionality reduction -> classification
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Analysis of Variance (ANOVA):
Fisher’s Discriminant Analysis (FDA):
Discriminant Correspondence Analysis:
Partial Least Squares (PLS)
PCA:
categorical independent and continuous dependent variables
continuos independent and categorical dependent variables
categorical independent and categorical dependent variables
continuous independent and continuous dependent variables
LDA:
[loadings,scores] = princomp(X);
[Z,W] = FDA(X, Y, 2);
cqs = fitcdiscr(X,Y,'DiscrimType','quadratic');
QDA:
% coeff of linear combinations
% dimensionality reduction by FDA script
% create classifier
c.) Partial Least Squares (PLS) regression builds a linear modell between
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X source space (independent variables) and
Y target space (dependent, predicted variables)
absorbance on different bands
like moisture, fat, protein content
Inside, it makes a PCA on X space, a PCA on Y space, then builds a linear regression
between the first p dim (latent variables, factors) of two PCA spaces.
The optimal number of latent variables are
determined by cross validation (building
model on calibration data set, then checking
prediction on validation set) on the base of
minimal Root Mean Squared Error of Prediction
(RMSEP):
2
RMSEP 
[XL,YL, XS,YS, beta, PCTvar, mse] = …
plsregress(X,Y, LVno, 'cv',20, 'mcreps',10000);
(y  o )
i
i
n
number of latent variables
The coefficient of determination (r2) characterizes the efficiency of PLS model.
The significant wavelengths can be assigned by the loading values of regression.
Loading values of enzym and fat content in cheese
d.) Partial Least Squares Discriminant Analys (PLS DA): variant for classification
PLS-DA consists in a classical PLS regression,
where the response variable is a categorical one (replaced by the set of dummy
variables describing the categories) expressing the class membership.
PCA space is rotated so that a maximum separation among classes is obtained,
and to understand which variables carry the class separating information. (Camo)
3D score plot of a two-class PLS-DA model of
GREEN versus RED/BLUE:
pls_model = pls(x,y,vl,'da');
e.) Orthogonal PLS DA (OPLS-DA)
Class-orthogonal variation is combined
with traditional PLS-DA.
It gives better performance if such
within-class variation exists.
(J.of Chemometrics)
Matlab toolboxes, like Eigenvector
other chemometric tools: SIMCA-P, Unscrambler, R (gnu), …
Artificial neural networks (ANN): for industrial application
used to connect some input cells (sensors) with some output cells (actuators).
•like statistical models they are teached on calibration set, then tested on validation set
•contrary to statistical models they use non-linear relations, with much more efficiency
ANN is a black box. We don’t exactly know, how it works, but it works well.
They are used therefore mostly not in scientific work, but for industrial applications.
Multilayer back-propagation neural network (MBPN):
Using calibration data set, weight values of synapses are set backwards (output to input)
in every cycle to get less error in prediction.
HIDDEN layers
logistic function:
Some NIR application on food:
moisture, protein in cereals (Norris, 1950)
moisture, fat, protein in meat (Kaffka, 1983)
sugar, acidity in fruits, sorting systems
food quality control in lab: any compound
Thank you for your attention