3. Spectroscopy Ferenc Firtha Corvinus University of Budapest Faculty of Food Science Department of...
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Transcript of 3. Spectroscopy Ferenc Firtha Corvinus University of Budapest Faculty of Food Science Department of...
3. Spectroscopy
Ferenc FirthaCorvinus University of Budapest
Faculty of Food ScienceDepartment of Physics and
Control
1. Colour: what like?
quick, but contact :-> average RGB/Lab/Lch
remote sensing + data reduction: -> position: colour, shape, pattern
2. Image processing: where?
4. Spectral imaging: where and what?
remote + stat. analysis + image processing-> position: distribution of compounds
contact + statistical analysis:NIR -> water, fat, oil, protein,…
3. Spectroscopy: what?
Place of spectroscopy
Light as
Electromagnetic wave:
ν excitation frequency + c velocity λ wavelength:
~ 300 000 km/s
Some ranges:• radio (30kHz–30MHz): λ big 30MHz• TV (50-1000MHz), GSM (380-1900MHz)300MHz• radar / WiFi, LAN, SAT 3GHz• microwave oven (water: 18-27GHz) 30GHz• IR, VIS, UV 300THz• X-ray (<1nm), gamma (<1pm): E big 300PHz
Photon:
Quantum theory: Energy of wave packet andν frequency:
h: Planck constant
Mass:
vc
vhE
2cmE
Electromagnetic ranges
Interactions of light
Absorption: of chemical components
Transmission: getting through
Reflection: specular (reflexió)diffuse (remisszió)scattering (physical properties)
Emission: after inducing (atomic level)
Measuring:
Transmission Absorbance
Reflection Absorbance
Fraunhofer lines (1814)
absorption lines in sunlight thousands of lines
Transmission spectrum of blue sky
- emission spectra- absorption spectra, absorption lines
Explanation: energy levels of hydrogen atom:
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 spectraof elements (VIS)
2. Refraction
•refraction of X-ray or electron ray
CT: Computed Tomography3D type of x-ray by examining slices from various angle
3. Raman spectroscopy
sample is illuminated with a laser beamradiation from the illuminated spot is collectedwavelength of laser (Rayleigh scattering) is filtered outshift of frequency is measured
Energy-level diagram (line thickness is proportional to the signal strength)
4. Scattering
•Scattering image of laser beamis 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, anthocyaninblackberries, red grapes, red cabbage, red onions, beets, radishes
carotenoidcarrot, tomato, lemon,orange, spinach, corn
quinone mushroom
pyrrole chlorophile
melaninskin
Chem: http://www.kfki.hu/chemonet/hun/eloado/kemia/festek2.htmlKép : http://www.healthymoncton.com/taste-the-rainbow-why-we-want-to-eat-fruits-veggies-from-all-of-the-colours-of-the-rainbow/
Cranberry juice (anthocyanin)
β,β-carotene degradation
melanin
VIS
chlorophyll
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
lipids:triacilglicerin (fat)
alcohol: metil,etil,propil…
protein
water free / bound: HDW (hydrofil) / LDW (hydrofob)
amid, amin
secondary amid
aromatic alcohol: e.g. benzyl
NIR 900–1700nmOH: 970, 1450, 1980Fiber: 1100, 1300, 1350, 1403, 1483, 1500, 1534Cellulose: 1490Lignin (wood): 1170, 1410, 1417, 1420, 1440
aromatic hydrocarbon: e.g. benzene
dckeII )(0
TI
IA 0lgdcII )(
0 10
303.2k ε: molar absorptivity (moláris abszorpciós tényező)
dcA
Absorbance: Lambert-Beer law
Absorbance is proportional:
to length (Bougue, 1729)
and concentration (Beer, 1852)
How to measure absorption?
1.Absorption spectroscopy:
Transmittance:
let’s suppose that reflectance is zero
TI
I
TA 0lg
1lg
0I
IT T
2. Reflection spectroscopy:
Absolute reflectance: all reflected / incidence
Reflectance (reflection factor): sample / standard
RI
I
RA 0lg
1lg
0I
IR R
0I
IR x
xI
I
RA 0lg
1lg
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) refractionNewton (1666) spectrum: birth of spectroscopyBougue, Lambert (1729) absorbance
1. Spencer spectrometer (1868), spectroscope Hartley (1880) chemical analysis of mixturesBeer (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 lines3.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: gratingDetector: thermal / pyroelectric / photoconducting
3. FT-NIR (Fourier transform infrared spectroscopy)Interferometer:
Combination of wavelengths interferogram Responses to different combinations recontruction of spectrum
interferogram
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.
PCA:
b.) Fisher’s Discriminant Analysis (FDA): Dimensionality reduction and classificationFinds a linear combination of features, which separates two or more classes.Steps: finds linear/quadratic classifier -> dimensionality reduction -> classification
•Analysis of Variance (ANOVA): categorical independent and continuous dependent variables•Fisher’s Discriminant Analysis (FDA): continuos independent and categorical dependent variables•Discriminant Correspondence Analysis: categorical independent and categorical dependent variables•Partial Least Squares (PLS) continuous independent and continuous dependent variables
LDA: QDA:[loadings,scores] = princomp(X); % coeff of linear combinations[Z,W] = FDA(X, Y, 2); % dimensionality reduction by FDA scriptcqs = fitcdiscr(X,Y,'DiscrimType','quadratic'); % create classifier
c.) Partial Least Squares (PLS) regression builds a linear modell between
• X source space (independent variables) and absorbance on different bands• Y target space (dependent, predicted variables) 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):
n
oyRMSEP ii
2)(
number of latent variables[XL,YL, XS,YS, beta, PCTvar, mse] = …plsregress(X,Y, LVno, 'cv',20, 'mcreps',10000);
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:
e.) Orthogonal PLS DA (OPLS-DA)
Class-orthogonal variation is combinedwith traditional PLS-DA.It gives better performance if such within-class variation exists.(J.of Chemometrics)
pls_model = pls(x,y,vl,'da');
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.
logistic function:HIDDEN layers
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