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Pat Training Course Chemical Imaging

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Image Contrast Based on Composition, Structure ... Using a ChemIcon CONDOR NIR Chemical Imaging Macroscope. A. B. C. D. E. F. Jim Drennen, Carl Anderson ... – PowerPoint PPT presentation

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Title: Pat Training Course Chemical Imaging


1
Pat Training CourseChemical Imaging

Gratefully acknowledge Dr. Pat Treado ChemIcon
for use of these slides
2
Chemical Imaging
- Molecular spectroscopy and digital imaging for
chemical analysis of materials
Conventional Imaging Low Contrast
Image Contrast Based on Composition, Structure
Concentration Without Sample Preparation
Component B Nylon
Component A Polystyrene
Raman image of Component B
Raman image of Component A
Chemical Imaging Massively Parallel Spectroscopy
Raman spectrum from vector XI1,J1 Component A
(Polystyrene)
n-1
XI1,J1
y
3
2
x
XI2,J2
1
Intensity
Raman spectrum from vector XI2,J2 Component B
(Nylon)
0
  • Why Chemical Imaging?
  • Fast
  • Noncontact nondestructive
  • High information content
  • Spectroscopy provides fingerprint for material

1000
800
1200
1400
1600
Wavenumber (cm-1)
3
Image Generating Approaches
  • Confocal sequential point scan
  • 512x512 pixels _at_ 4 secs/pixel
  • Time 1,024,000 secs (12 days)
  • Spatial Resolution 2 mm
  • Spectral Rich,

?1
Spectrometer
Intensity at ?1
  • Sequential line scan
  • 512x512 pixels _at_ 4 secs/line
  • Time 2,048 secs (30 mins)
  • Spatial Resolution 5 mm

?1
y
Spectrometer
x
Intensity at ?1
Tuned to ?1
  • LCTF widefield
  • 512x512 pixels _at_ 4 secs/l
  • 8 ls
  • Time 32 secs
  • Spatial Resolution 0.25 mm

Signal intensity
Filter
Intensity at ?1
4
Chemical Imaging Technology Techniques Comparison
3
5
Chemical Imaging Techniques
Raman
Mid-IR
SEM/EDS
NIR
Chemical Information Content
Luminescence
UV-Visible Absorbance
SEM
10-3
10-2
1
10-1
101
10-4
10-5
10-6
10-7
10-8
Feature Size (m)
6
Application Pharmaceutical Formulation
Characterization Content Uniformity IR 2D Raman
2D
7
Chemical Imaging for Pharmaceutical Screening
(Macro and Micro Views)
  • 1 month delay to market - 10s M in lost revenue
  • Chemical imaging reveals tablet composition,
    structure and architecture in seconds.
  • Chemical Imaging speeds drugs to market
  • Drug Discovery
  • Tablet Formulation
  • Controlled Release Engineering
  • Manufacturing scale-up

Whole Tablet Image
1 mm
Brightfield Reflectance Optical Microscopy
25 mm
8
Chemical Imaging for Pharmaceutical
Screening Raman Chemical Imaging - Raw Data
Analysis
Aspirin Raman Image Raw Data 1044 cm-1
Microscale Brightfield Image
Excipient Raman Image Raw Data 1060 cm-1
C
A
B
25 mm
9
Chemical Imaging for Pharmaceutical
Screening Raman Chemical Imaging - Raw Data
Analysis
Aspirin Excipient
Intensity (Normalized)
1000
1010
1020
1030
1040
1050
1060
1070
1080
1090
1100
Raman Shift cm-1
25 mm
10
Chemical Imaging for Pharmaceutical
Screening Raman Imaging of Aspirin Tablet MCR
Factor Rotation Pure Component Images
Aspirin Excipient
Intensity (Normalized)
1000
1010
1020
1030
1040
1050
1060
1070
1080
1090
1100
Raman Shift cm-1
11
Chemical Imaging for Pharmaceutical
Screening Raman Imaging of Whole Aspirin/Lactose
Tablet
CC
CH2
Reflectance Image
2 mm
Offset Intensity
Aspirin
Raman Image
Aspirin Lactose
Lactose
500
1000
1500
2000
2500
3000
3500
Raman Shift (cm-1)
12
Chemical Imaging for Pharmaceutical Screening
Raman / NIR Imaging of Whole Aspirin/Lactose
Tablet
NIR Image
Aspirin Lactose
NIR Raman Drug Particles 122 67 Area
(mm2) 70,728 /- 138,507 108,561 /-
141,273 Perimeter (mm) 1268.5 /-
1788.6 1485.4 /- 1260.8 Max. Chord (mm) 337.7
/- 360.83 435.2 /- 335.06 Shape .51 /-
.19 .6 /- .15
2 mm
Raman Image
Aspirin Lactose
13
PLSR Quantitative Analysis of Salicylic Acid in
Pharmaceutical Blends
  • 11 known blend concentrations (0 to 50 salicylic
    acid in lactose)
  • Each whole tablet analyzed with Macro NIR
    Chemical Imaging (reflectance mode) from 1000 to
    1700 nm
  • Calibration sets of 10 spectra per tablet were
    generated for regions of interest (ROIs)
  • Calibration spectra and corresponding
    concentration values were used to build a
    calibration model via partial least-squares
    regression (PLSR)
  • Calibration model used to predict concentrations
    of each known tablet and across set of six
    unknown blended tablets

Absorbance Spectra
0.26
0.21
0.16
Absorbance
0.11
0.06
0.01
1100
1200
1300
1400
1500
1600
1700
Wavelength (nm)
14
Macro NIR Chemical Imaging of Pharmaceutical
Tablets
Using a ChemIcon CONDOR? NIR Chemical Imaging
Macroscope
Calibration Image
Prediction Image
A
B
C
D
E
F
Sample
Salicylic Acid ( /- StdDev)
Salicylic Acid
Known Conc ()
Sample
A1
0.56 /- 1.09
47.96 /- 2.74
0
Tab 00
A2
5.78 /- 2.57
0.68 /- 1.05
5
Tab 01
B1
9.68 /- 2.42
9.79 /- 2.51
10
Tab 02
B2
15.11 /- 2.64
34.63 /- 2.69
15
Tab 03
C1
21.22 /- 2.79
25.10 /- 2.81
20
Tab 04
C2
24.80 /- 2.82
14.77 /- 2.59
25
Tab 05
D
29.03 /- 2.83
44.23 /- 3.14
30
Tab 06
E
34.92 /- 3.02
22.47 /- 2.58
35
Tab 07
F1
39.77 /- 3.20
6.16 /- 4.43
40
Tab 08
F2
44.21 /- 3.59
38.23 /- 2.73
45
Tab 09
50.13 /- 3.40
50
Tab 10
Jim Drennen, Carl Anderson Duquesne University
15
Acknowledgement
Dr. Pat Treado ChemIcon Inc. 7301 Penn Avenue,
Pittsburgh, PA 15208 Tel. (412) 241-7335 Fax.
(412) 241-7311 Web. www.chemimage.com
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