3. Spectroscopy Ferenc Firtha Corvinus University of Budapest Faculty of Food Science

1. 3. Spectroscopy Ferenc Firtha Corvinus University of Budapest Faculty of Food Science Department of Physics and Control

2. 2. Image processing: where? remote sensing + data reduction: -> position: colour, shape, pattern Place of spectroscopy 1. Colour: what like? quick, but contact : -> average RGB/Lab/Lch 3. Spectroscopy: what? 4. Spectral imaging: where and what? remote + stat. analysis + image processing -> position: distribution of compounds contact + statistical analysis: NIR -> water, fat, oil, protein,…

3. Light as Electromagnetic wave: νexcitation 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 and ν frequency: h: Planck constant Mass:

4. Electromagnetic ranges

5. 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

6. Fraunhofer lines (1814) absorption lines in sunlight  thousands of lines Transmission spectrum of blue sky

7. Explanation: energy levels of hydrogen atom: - emission spectra - absorption spectra, absorption lines

8. 1. Atomic emission spectroscopy Flame or inductivelyexcited atoms and ions emit EM radiation. Spectrum is characteristic to the different energy levels of electrons, atomic components emission spectra of elements (VIS)

9. 2. Refraction • refraction of X-rayorelectronray CT: Computed Tomography 3D type of x-ray by examining slices from various angle

10. 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)

11. 4. Scattering • Scattering image of laserbeamis characteristictothephysicalstructure,likecellwalls, rheologicalproperties

12. 5. Absorption spectroscopy Let’s go back to the sunlight: there are also valleys, not only absorption lines

13. 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 • Spectral lines are broader causing overlap of many of the absorption peaks • Overtones and combinations also appear

14. VIS 380-780nm flavonoid, anthocyaninblackberries, red grapes, red cabbage, red onions, beets, radishes carotenoidcarrot, tomato, lemon,orange, spinach, corn quinonemushroom pyrrole chlorophile melaninskin 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/

15. Cranberry juice (anthocyanin) β,β-carotene degradation VIS chlorophyll melanin

16. 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

17. OH: 970, 1450, 1980 Fiber: 1100, 1300, 1350, 1403, 1483, 1500, 1534 Cellulose: 1490 Lignin (wood): 1170, 1410, 1417, 1420, 1440 NIR 900–1700nm water free / bound: HDW (hydrofil) / LDW (hydrofob) alcohol: metil,etil,propil… aromatic alcohol: e.g. benzyl protein amid, amin aromatic hydrocarbon: e.g. benzene secondary amid lipids: triacilglicerin (fat)

18. Absorbance: Lambert-Beer law ε: molar absorptivity (moláris abszorpciós tényező) Absorbance is proportional: to length (Bougue, 1729) and concentration (Beer, 1852)

19. How to measure absorption? • Absorption spectroscopy: Transmittance: let’s suppose that reflectance is zero Reflectionspectroscopy: Absolutereflectance: allreflected / incidence Reflectance (reflectionfactor): sample / standard Questions: non-homogen  grain inspected uneven surface  sample rotated

20. Reflectionspectroscopy geometry: • 45/0 (illumination/observation) • d/8 angle of view: usually 2 or 10 degree d/8 geometry Reflectance standards (VIS..NIR)

21. 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.

22. Somelawsof spectroscopy Kirchhoff(1860) radiation of solidis continuous (black body) radiation of gasconsistoflines solidingas: has missinglines Stefan (1879) black-bodyradiantexitance 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:

23. 2. Spectrophotometer Light source: electrically heated Nernst glower () Mirror,lens,cuvette: alkali-halid glass (alkáli-halogenidből) Monochromator: grating Detector: thermal / pyroelectric / photoconducting

24. 3. FT-NIR(Fourier transform infrared spectroscopy) Interferometer: interferogram Combination of wavelengths  interferogram Responses to different combinations  recontruction of spectrum

25. Howtoprocessspectrum? normalization: getspectrasettosamelevel • Standard NormalVariate (SNV): subtractmean and devidebyvariance smoothing: beforedifferentiation • Movingaverage • Savitzky-Golay: polinomialregression derivatives: toeliminate shift of peaks 1. kind of normalization 2. curvature (görbület) assignation: of compounds - statisticalmodels, like PLS, DA - artificial neuron network

26. 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 DiscriminantAnalysis (FDA): Dimensionalityreduction and classification • Finds a linear combination of features, which separates two or more classes. • Steps: findslinear/quadraticclassifier -> dimensionalityreduction -> classification • Analysis of Variance (ANOVA): categoricalindependent and continuousdependentvariables • Fisher’s DiscriminantAnalysis (FDA): continuosindependent and categoricaldependentvariables • DiscriminantCorrespondenceAnalysis: categoricalindependent and categoricaldependentvariables • PartialLeastSquares (PLS) continuousindependent and continuousdependentvariables LDA: QDA: PCA: [loadings,scores] = princomp(X); % coeff of linear combinations [Z,W] = FDA(X, Y, 2); % dimensionality reduction by FDA script cqs = fitcdiscr(X,Y,'DiscrimType','quadratic'); % create classifier

27. c.) PartialLeastSquares (PLS)regressionbuilds a linear modell between • Xsourcespace (independentvariables) and absorbanceondifferentbands • Ytargetspace (dependent, predictedvariables)likemoisture, 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): [XL,YL,XS,YS, beta,PCTvar, mse] = … plsregress(X,Y, LVno, 'cv',20, 'mcreps',10000); number of latent variables

28. 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

29. 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 Eigenvectorother chemometric tools: SIMCA-P, Unscrambler, R (gnu), …

30. 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:

31. 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

32. Thank you for your attention