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Monitoring Physical Properties of Pharmaceutical Products Using Fringing Field Dielectric Spectrosco

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API Content in Powder Sample. Tablet Coating Thickness. 9/25/09 ... Tablet Coating Thickness. 9/25/09. SEAL, EE Department, University of Washington. 13 ... – PowerPoint PPT presentation

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Title: Monitoring Physical Properties of Pharmaceutical Products Using Fringing Field Dielectric Spectrosco


1
Monitoring Physical Properties of Pharmaceutical
Products Using Fringing Field Dielectric
Spectroscopy
  • Kishore Sundara-Rajan and Prof. Alexander
    Mamishev
  • Sensors, Energy and Automation Laboratory
  • Department of Electrical Engineering
  • University of Washington
  • mamishev_at_ee.washington.edu
  • http//www.ee.washington.edu/faculty/mamishev/pers
    onal
  • http//www.ee.washington.edu/research/seal

2
Outline
  • Introduction to Interdigital Sensors and
    Dielectrometry
  • Experimental Results
  • Conclusion and future work

3
Introduction to Dielectrometry
  • Fringing Field Interdigital Sensor
  • Dielectric Spectroscopy

4
Fringing Field Interdigital Sensor
  • For a semi-infinite homogeneous medium placed on
    the surface of the sensor, the periodic variation
    of the electric potential along the X-axis
    creates an exponentially decaying electric field
    along the Z-axis, which penetrates the medium.

5
Multiple Penetration Depth Sensors
  • The spatial wavelength (?) of the sensor is
    defined as the distance between the centers of
    two adjacent electrodes of same type.
  • Penetration depth is commonly defined as ?/3.

6
Dielectric Spectroscopy
  • Dielectric Spectroscopy is defined as the study
    of dielectric behavior of a material when
    subjected to an external electric field, over a
    range of frequencies.
  • Dielectric response is quantified in terms of
    complex dielectric permittivity.
  • Debye relaxation model - assumes a relaxation
    process that is governed by first order dynamics
    ? single time constant (t).

7
Chemometric Challenges
  • Dielectrometry sensing can measure various
    characteristics of a sample.
  • The chemometric challenge extract the effect of
    parameters of interest in the presence of
    disturbance factors.

8
Dielectrometry Sensing
9
Di-SPEC
  • Application specific sensor design
  • In-house designed multi channel data acquisition
    circuit.
  • Custom designed user interface.
  • Integrated real time data processing units.
  • Modular software architecture.

10
Experimental Results
  • Tablet Hardness
  • Drug Signature
  • API Content in Powder Sample
  • Tablet Coating Thickness

11
Experimental Setup
  • Fringing Electric Field Sensor with wavelength of
    500 microns was used.
  • Parallel plate sensor of dimensions 40 x 40 x 15
    mm was used.
  • Measurements were made using Fluke manufactured
    RCL meter (Model PM 6304).
  • Sensor was NOT optimized for pharmaceutical
    samples.

12
Tablet Coating Thickness
13
Capacitance Plots
14
Conductance Plots
15
Conductance Plots
16
Phase Plots
17
Phase Plots
18
FEF vs. Parallel Plate Sensor
Parallel plate
FEF
  • The FEF offers greater measurement sensitivity
    than the parallel plate sensor.
  • Parallel plate sensor measurements are more
    linear than those of the FEF sensor.

19
Tablet Hardness
20
Tablet signature using FEF sensors
Use capacitance measurements to differentiate
between different types of tablets.
21
API Content in Powder Sample
  • Most solvent is removed during the first 2 hours
    of drying, as is evident in the relative high
    dynamic range of the curve on the top.
  • At frequencies above 1 kHz, capacitance
    measurements decrease strictly with drying time.

22
API Content in Powder Sample - II
  • Monotonic dependence exists between capacitance
    and drying time.
  • One-to-one mapping can be established between
    capacitance and drying time to calibrate the
    sensor.

23
Future work
  • Design and fabrication of MEMS based sensor
    heads.
  • Novel multi-channel FEF sensor designs.
  • Multivariable analysis to compensate for
    disturbance factors.
  • Increased sophistication of parameter estimation
    algorithms for solving the inverse problem Deeper

24
Acknowledgements
  • A special thanks goes out to
  • Aventis
  • Wyeth Pharmaceuticals
  • Centre for Process Analytical Chemistry, UW.
  • IEEE DEIS Society
  • Leslie Byrd II
  • Abhinav Mathur
  • Nick Semenyuk
  • Cheuk Wai-Mak
  • Alexei Zyuzin
  • Xiaobei Li
  • Mike Hegg
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