Hyperspectral Chemical Imaging: principles and Chemometrics.

Transcription

1 Hyperspectral Chemical Imaging: principles and Chemometrics

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3 University College Dublin

4 University College Dublin 1,596 PhD students 6,17 international students 8,54 graduate students 31,84 total students 24 % international staff

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6 Environment Biosystems Engineering Food Primary production Quality control Smart systems Processing Novel preservation Process control Post-Processing Env. impact Waste utilisation Water quality Air quality Food safety Risk analysis Green technology Energy systems Sensor Technology Mathematical modelling

7 WHAT IS HYPERSPECTRAL IMAGING?

8 Conventional Imaging Red ~645 nm Green ~ 51 nm Blue ~ 4 nm

9 Multispectral Imaging

10 Hyperspectral Imaging

11 Conventional spectroscopy A λ

12 Hyperspectral imaging A λ

13 Hyperspectral cube Single wavelength image λ Y X Pixel spectrum

14 HOW TO GET A HYPERCUBE?

15 Point Mapping λ High spectral resolution Excellent spatial resolution Very slow (up to hours)

16 Point Mapping Sample Fiber Optic Bundle Dispersive Optics Detector

17 Staring Face λ Medium-Low spectral resolution Medium-Low spatial resolution Very fast (up to seconds)

18 Staring Face Detector to PC Tunable Filter Optics Sample 18

19 Pushbroom Mirror Objective Slit Spectrograph Camera λ Medium-High spectral resolution Excellent spatial resolution Much faster (up to minutes)

20 Pushbroom λ Detector to PC Spectrograph Optics Sample

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22 APPLICATIONS OF HSI

23 Applications Food Agriculture Pharmaceutical Environment Biofuels Materials Medicine Cosmetics

24 HSI

25 Remote sensing Vis-NIR n-airborne-hyperspectral/ Macroscopic Fluorescence Microscopic Raman Detection of skin tumors on chicken carcasses using hyperspectral fluorescence imaging Kim, et al 24 Transactionsamerican society of agricultural engineers 47 (5), Bacterial mixture identification using Raman and surface-enhanced Raman chemical imaging, Guicheteau et al., 21, Journal of Raman Spectroscopy 41 (12),

26 HYPERSPECTRAL IMAGE ANALYSIS

27 Data Analysis Packages for HSI Matlab R NIR Hyperspectral Image analysis using R (NIR News) Hyperspectral data analysis in R (RS package)

28 Overview λ λ c λ x =

29 1. EXPLORATORY ANALYSIS

30 Number of samples (n) Log(1/T) Non-imaging Data structure Number of wavelengths (λ) = m X λ

31 Correlated variables In NIR, λ variables are highly correlated with each other 1... n λ 1 λ 2 X λ m λ λ λ 1 Poor model performance using Multi-Linear Regression need to describe data variability by a few uncorrelated variables containing relevant information

32 Principal components analysis Principal Component Analysis (PCA) combines original variables to make new variables (PCs) that maximise variance in X PC m X i a ij j 1 j 1 λ 1 λ 2 λ m PCs are orthogonal to each other, i.e. independent n

33 How to do PCA? Calculate covariance matrix o Mean center X: X MC = X rowmean (X) o COV =( X MC *X MC )/(n-1) o n = number of rows in X Calculate eigenvalues (EV) and eigenvectors (L) of COV: Singular value decomposition: COV = L*P*L o EV = diag(p) o EV represents variance of each eigenvector ( loading ) o %Var = 1*EV/sum(EV)

34 How to do PCA? In order to construct PC scores, S Project original data to eigenvector space S = X mn *L A shortcut to all of this is using: the princomp function in Matlab: [L,S,Ev]=princomp(X) Or the princomp function in R: X_PC<-princomp(X,scores=TRUE)

35 PCA 1 λ 1 λ 2 λ m 1 PC 1 PC 2 PC p 1 λ 1 λ 2 λ m L X = S p + E n n Original data matrix Scores Loadings

36 X2 3. PCA: example PC2 PC X1 Simulated data: X2 =.5X1 +E, E = random variable

37 log (1/T) PC 2 PCA: Example 2 NIR spectra of Water and Salt water 15 variables 2 variables Wavelength (nm) PC 1

38 PCA on hypercube λ X λ

39 PCA on hypercube 1 λ 1 λ 2 λ m PC 1 PC 2 PC p 1 X PCA S PC n n

40 PCA on hypercube NIR-CI of tablet with drop of water in centre >1 λs: nm

41 PCA HSI: Example nm % Variance in 1 st 3 PCs

42 2. Preprocessing de-trending derivatives asymmetric least squares scaling multiplicative signal correction standard normal variate

43 2. Preprocessing

44 Reflectance 2. Preprocessing Wavelength (nm)

45 Log(1/R)

46 Min scaling

47 13 nm scaling

48 Max scaling

49 SNV

50 EMSC

51 SG

52 2. Preprocessing Most suitable pre-treatment: Within-class variability Number of outliers Between-class distance

53 2. Preprocessing

54 3. Classification

55 CI cube 3. Classification

56 3. Classification CI cube CI cube

57 3. Classification 2 Class 1 2 Class Selected regions

58 3. Classification

59 3. Classification

60 3. Classification

61 3. Classification

62 3. Classification

63 4. Regression

64 General Scheme λ Mean spectra Image 1 Pred 1 Image 2... Image N... N spectra model Y model Pred 2... Pred N? Image corrections Pretreatments??? Spectra selection Model selection

65 Spatial Y axis Reflectance Reflectance Simulated data 1.8 g1 g2 1.8 s Wavelength Wavelength S1 S1 + = WGN S1N S1 Spatial X axis

66 Real data Soak times 3 min 1 h 2 h 3 h 4 h 5 h 6 h 4 ± 1 o C

67 Selection of #LV in PLSR λ Mean spectra #LV Image 1 Pred 1 Image 2... Image N... N spectra Y Regression vector (b) RMSECV b Pred 2... Pred N

68 RMSEP of prediction images LV LV Pred 1 Res 1 Pred 2... Res 2... Pred N Res N RMSEP Imk = (( Gowen, Burger, Esquerre et al, 214. Near infrared hyperspectral image regression: on the use of prediction maps as a tool for detecting model overfitting. doi: /jnirs.1114 n p i 1 Res ik 2 )/n p ).5

69 4 LV Simulated data

70 5 LV Simulated data

71 Chickpea data

72 Selection of calibration spectra λ Mean spectra Image 1 Pred 1 Image 2... Image N... N spectra model Y model Pred 2... Pred N? Mean spectra Spectra selection Median spectra Random spectra

73 Prediction maps: Mean spectra 1 LV 2 LV 3 LV 4 LV 5 LV 6 LV 7 LV 8 LV 9 LV

74 Prediction maps: Random spectra 1 LV 2 LV 3 LV 4 LV 5 LV 6 LV 7 LV 8 LV 9 LV

75 PLS Regression vectors.1 1 LV 2 LV 3 LV LV 5 LV 6 LV LV 8 LV 9 LV Mean Spectra Random Spectra

76 Predicted Value Predicted Value Predicted Y values (5 LV model) Actual Value Actual Value Mean Spectra Random Spectra

77 Chickpea data: 7 LV Mean Spectra

78 Chickpea data: 7 LV Random Spectra

79 Chickpea data: 1 LV Mean Spectra

80 Chickpea data: 1 LV Random Spectra

81 Predicted Value Chickpeas 7 LV model Actual Value Mean Spectra Random Spectra

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83 5. Compression

84 5. Compression PCA Principal Components Analysis ICA Independent Components Analysis MNF Maximum Noise Fraction RP Random projection PP Projection Pursuit

85 Compression 1 λ 1 λ 2 λ m 1 PC 1 PC p 1 p λ 1 λ 2 L λ m X = S + E n n Original data matrix Scores Loadings

86 Compressed/Original Compression Components retained

87 Compression 3 x 14 2 x x x x x

88 Compression 1 λ 1 λ 2 λ 1 PC 1 PC 5 1 PC λ 1 1 λ 2 λ L 1 PC 5 X PCA S n n

89 II. Compression 12 nm 12 nm Original - compressed

90 Reflectance Compression Wavelength

91 PCA in Spatial domain 1 PCs 12 nm 12 nm Original - compressed

92 PCA in Spatial domain 2 PCs 12 nm 12 nm Original - compressed

93 PCA in Spatial domain 5 PCs 12 nm 12 nm Original - compressed

94 6. Data Fusion CI cube

95 Data Fusion PP HDPE NIR (1-17 nm): 134x131 pixels PS LDPE Vis (4-1 nm): 49x126 pixels

96 Data Fusion Vis (4-1 nm): 134x131 pixels NIR (1-17 nm): 134x131 pixels

97 Data Fusion λ vis VIS λ nir NIR

98 Data Fusion λ vis VIS λ nir NIR

99 Data Fusion PC vis PC nir VIS VIS NIR PC vis PC nir NIR

100 Data Fusion % Misclassified pixels Spectra PCs Vis NIR Low Level Fusion Vis-NIR Mid-level Fusion

101 Further reading Near infrared hyperspectral image analysis using R, Gowen, A. (214). NIR news 25 (2), Hyperspectral Imaging and Chemometrics: A Perfect Combination for the Analysis of Food Structure, Composition and Quality, Amigo, J., Martı, I., Gowen, A. (213) in Chemometrics in Food Chemistry (Edited by Federico Marini) Suppressing sample morphology in near infrared spectral imaging of agriculture products by chemometric pre-treatments, Esquerre, C., Gowen, A., et al. (212). Chemometrics and Intelligent Laboratory Systems. Time series hyperspectral chemical imaging data: challenges, solutions and applications. Gowen, A., Marini, F., et al. (211). Analytica Chimica Acta, 75: Data handling in Hyperspectral Image Analysis. Burger, J., Gowen, A. (211). Chemometrics and Intelligent Laboratory Systems, 18: The application of hyperspectral chemical imaging to chemometrics. Burger, J., Gowen, A. (211). NIR news, 22: /35

102 Acknowledgements Dr. James Burger

103