---
_id: '10216'
abstract:
- lang: eng
  text: Wet granulation is a frequent process in the pharmaceutical industry. As a
    starting point for numerous dosage forms, the quality of the granulation not only
    affects subsequent production steps but also impacts the quality of the final
    product. It is thus crucial and economical to monitor this operation thoroughly.
    Here, we report on identifying different phases of a granulation process using
    a machine learning approach. The phases reflect the water content which, in turn,
    influences the processability and quality of the granule mass. We used two kinds
    of microphones and an acceleration sensor to capture acoustic emissions and vibrations.
    We trained convolutional neural networks (CNNs) to classify the different phases
    using transformed sound recordings as the input. We achieved a classification
    accuracy of up to 90% using vibrational data and an accuracy of up to 97% using
    the audible microphone data. Our results indicate the suitability of using audible
    sound and machine learning to monitor pharmaceutical processes. Moreover, since
    recording acoustic emissions is contactless, it readily complies with legal regulations
    and presents Good Manufacturing Practices.
article_number: '2153'
author:
- first_name: Ruwen
  full_name: Fulek, Ruwen
  id: '79527'
  last_name: Fulek
- first_name: Selina
  full_name: Ramm, Selina
  id: '68713'
  last_name: Ramm
  orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Christian
  full_name: Kiera, Christian
  last_name: Kiera
- first_name: Miriam
  full_name: Pein-Hackelbusch, Miriam
  id: '64952'
  last_name: Pein-Hackelbusch
  orcid: 0000-0002-7920-0595
- first_name: Ulrich
  full_name: Odefey, Ulrich
  id: '74218'
  last_name: Odefey
citation:
  ama: Fulek R, Ramm S, Kiera C, Pein-Hackelbusch M, Odefey U. A machine learning
    approach to qualitatively evaluate different granulation phases by acoustic emissions.
    <i>Pharmaceutics</i>. 2023;15(8). doi:<a href="https://doi.org/10.3390/pharmaceutics15082153">https://doi.org/10.3390/pharmaceutics15082153</a>
  apa: Fulek, R., Ramm, S., Kiera, C., Pein-Hackelbusch, M., &#38; Odefey, U. (2023).
    A machine learning approach to qualitatively evaluate different granulation phases
    by acoustic emissions. <i>Pharmaceutics</i>, <i>15</i>(8), Article 2153. <a href="https://doi.org/10.3390/pharmaceutics15082153">https://doi.org/10.3390/pharmaceutics15082153</a>
  bjps: <b>Fulek R <i>et al.</i></b> (2023) A Machine Learning Approach to Qualitatively
    Evaluate Different Granulation Phases by Acoustic Emissions. <i>Pharmaceutics</i>
    <b>15</b>.
  chicago: Fulek, Ruwen, Selina Ramm, Christian Kiera, Miriam Pein-Hackelbusch, and
    Ulrich Odefey. “A Machine Learning Approach to Qualitatively Evaluate Different
    Granulation Phases by Acoustic Emissions.” <i>Pharmaceutics</i> 15, no. 8 (2023).
    <a href="https://doi.org/10.3390/pharmaceutics15082153">https://doi.org/10.3390/pharmaceutics15082153</a>.
  chicago-de: Fulek, Ruwen, Selina Ramm, Christian Kiera, Miriam Pein-Hackelbusch
    und Ulrich Odefey. 2023. A machine learning approach to qualitatively evaluate
    different granulation phases by acoustic emissions. <i>Pharmaceutics</i> 15, Nr.
    8. doi:<a href="https://doi.org/10.3390/pharmaceutics15082153">https://doi.org/10.3390/pharmaceutics15082153</a>,
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Fulek, Ruwen</span> ; <span
    style="font-variant:small-caps;">Ramm, Selina</span> ; <span style="font-variant:small-caps;">Kiera,
    Christian</span> ; <span style="font-variant:small-caps;">Pein-Hackelbusch, Miriam</span>
    ; <span style="font-variant:small-caps;">Odefey, Ulrich</span>: A machine learning
    approach to qualitatively evaluate different granulation phases by acoustic emissions.
    In: <i>Pharmaceutics</i> Bd. 15. Basel, MDPI (2023), Nr. 8'
  havard: R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, U. Odefey, A machine learning
    approach to qualitatively evaluate different granulation phases by acoustic emissions,
    Pharmaceutics. 15 (2023).
  ieee: 'R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, and U. Odefey, “A machine
    learning approach to qualitatively evaluate different granulation phases by acoustic
    emissions,” <i>Pharmaceutics</i>, vol. 15, no. 8, Art. no. 2153, 2023, doi: <a
    href="https://doi.org/10.3390/pharmaceutics15082153">https://doi.org/10.3390/pharmaceutics15082153</a>.'
  mla: Fulek, Ruwen, et al. “A Machine Learning Approach to Qualitatively Evaluate
    Different Granulation Phases by Acoustic Emissions.” <i>Pharmaceutics</i>, vol.
    15, no. 8, 2153, 2023, <a href="https://doi.org/10.3390/pharmaceutics15082153">https://doi.org/10.3390/pharmaceutics15082153</a>.
  short: R. Fulek, S. Ramm, C. Kiera, M. Pein-Hackelbusch, U. Odefey, Pharmaceutics
    15 (2023).
  ufg: '<b>Fulek, Ruwen u. a.</b>: A machine learning approach to qualitatively evaluate
    different granulation phases by acoustic emissions, in: <i>Pharmaceutics</i> 15
    (2023), H. 8.'
  van: Fulek R, Ramm S, Kiera C, Pein-Hackelbusch M, Odefey U. A machine learning
    approach to qualitatively evaluate different granulation phases by acoustic emissions.
    Pharmaceutics. 2023;15(8).
date_created: 2023-08-15T10:48:15Z
date_updated: 2025-07-29T13:21:40Z
department:
- _id: DEP4022
- _id: DEP4028
- _id: DEP4014
doi: https://doi.org/10.3390/pharmaceutics15082153
external_id:
  isi:
  - '001119084200001'
  pmid:
  - '37631367'
intvolume: '        15'
isi: '1'
issue: '8'
keyword:
- wet granulation
- acoustic classification
- machine learning
- convolutional neural networks
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/1999-4923/15/8/2153
oa: '1'
place: Basel
pmid: '1'
publication: Pharmaceutics
publication_identifier:
  eissn:
  - '1999-4923 '
publication_status: published
publisher: MDPI
quality_controlled: '1'
status: public
title: A machine learning approach to qualitatively evaluate different granulation
  phases by acoustic emissions
type: scientific_journal_article
user_id: '83781'
volume: 15
year: '2023'
...
---
_id: '9627'
author:
- first_name: Selina
  full_name: Ramm, Selina
  id: '68713'
  last_name: Ramm
  orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Ruwen
  full_name: Fulek, Ruwen
  id: '79527'
  last_name: Fulek
- first_name: Veronika Anna
  full_name: Eberle, Veronika Anna
  last_name: Eberle
- first_name: Christian
  full_name: Kiera, Christian
  last_name: Kiera
- first_name: Ulrich
  full_name: Odefey, Ulrich
  id: '74218'
  last_name: Odefey
- first_name: Miriam
  full_name: Pein-Hackelbusch, Miriam
  id: '64952'
  last_name: Pein-Hackelbusch
  orcid: 0000-0002-7920-0595
citation:
  ama: Ramm S, Fulek R, Eberle VA, Kiera C, Odefey U, Pein-Hackelbusch M. <i>Compression
    Density as an Alternative to Identify an Optimal Moisture Content for High Shear
    Wet Granulation as an Initial Step for Spheronisation</i>.; 2023.
  apa: Ramm, S., Fulek, R., Eberle, V. A., Kiera, C., Odefey, U., &#38; Pein-Hackelbusch,
    M. (2023). <i>Compression Density as an Alternative to Identify an Optimal Moisture
    Content for High Shear Wet Granulation as an Initial Step for Spheronisation</i>.
    Ph.D. Student’s Symposium on Tabletting Technology 2023, Rosenberg.
  bjps: <b>Ramm S <i>et al.</i></b> (2023) <i>Compression Density as an Alternative
    to Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial
    Step for Spheronisation</i>. .
  chicago: Ramm, Selina, Ruwen Fulek, Veronika Anna Eberle, Christian Kiera, Ulrich
    Odefey, and Miriam Pein-Hackelbusch. <i>Compression Density as an Alternative
    to Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial
    Step for Spheronisation</i>, 2023.
  chicago-de: Ramm, Selina, Ruwen Fulek, Veronika Anna Eberle, Christian Kiera, Ulrich
    Odefey und Miriam Pein-Hackelbusch. 2023. <i>Compression Density as an Alternative
    to Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial
    Step for Spheronisation</i>.
  din1505-2-1: '<span style="font-variant:small-caps;">Ramm, Selina</span> ; <span
    style="font-variant:small-caps;">Fulek, Ruwen</span> ; <span style="font-variant:small-caps;">Eberle,
    Veronika Anna</span> ; <span style="font-variant:small-caps;">Kiera, Christian</span>
    ; <span style="font-variant:small-caps;">Odefey, Ulrich</span> ; <span style="font-variant:small-caps;">Pein-Hackelbusch,
    Miriam</span>: <i>Compression Density as an Alternative to Identify an Optimal
    Moisture Content for High Shear Wet Granulation as an Initial Step for Spheronisation</i>,
    2023'
  havard: S. Ramm, R. Fulek, V.A. Eberle, C. Kiera, U. Odefey, M. Pein-Hackelbusch,
    Compression Density as an Alternative to Identify an Optimal Moisture Content
    for High Shear Wet Granulation as an Initial Step for Spheronisation, 2023.
  ieee: S. Ramm, R. Fulek, V. A. Eberle, C. Kiera, U. Odefey, and M. Pein-Hackelbusch,
    <i>Compression Density as an Alternative to Identify an Optimal Moisture Content
    for High Shear Wet Granulation as an Initial Step for Spheronisation</i>. 2023.
  mla: Ramm, Selina, et al. <i>Compression Density as an Alternative to Identify an
    Optimal Moisture Content for High Shear Wet Granulation as an Initial Step for
    Spheronisation</i>. 2023.
  short: S. Ramm, R. Fulek, V.A. Eberle, C. Kiera, U. Odefey, M. Pein-Hackelbusch,
    Compression Density as an Alternative to Identify an Optimal Moisture Content
    for High Shear Wet Granulation as an Initial Step for Spheronisation, 2023.
  ufg: '<b>Ramm, Selina u. a.</b>: Compression Density as an Alternative to Identify
    an Optimal Moisture Content for High Shear Wet Granulation as an Initial Step
    for Spheronisation, o. O. 2023.'
  van: Ramm S, Fulek R, Eberle VA, Kiera C, Odefey U, Pein-Hackelbusch M. Compression
    Density as an Alternative to Identify an Optimal Moisture Content for High Shear
    Wet Granulation as an Initial Step for Spheronisation. 2023.
conference:
  end_date: 2023-03-15
  location: Rosenberg
  name: Ph.D. Student's Symposium on Tabletting Technology 2023
  start_date: 2023-03-13
date_created: 2023-03-16T09:46:10Z
date_updated: 2025-07-29T13:23:12Z
department:
- _id: DEP4022
- _id: DEP4028
- _id: DEP4014
language:
- iso: eng
publication_status: published
status: public
title: Compression Density as an Alternative to Identify an Optimal Moisture Content
  for High Shear Wet Granulation as an Initial Step for Spheronisation
type: conference_speech
user_id: '83781'
year: '2023'
...
---
_id: '9568'
abstract:
- lang: eng
  text: Pellet production is a multi-step manufacturing process comprising granulation,
    extrusion and spheronisation. The first step represents a critical control point,
    since the quality of the granule mass highly influences subsequent process steps
    and, consequently, the quality of final pellets. The most important parameter
    of wet granulation is the liquid requirement, which can often only be quantitatively
    evaluated after further process steps. To identify an alternative for optimal
    liquid requirements, experiments were conducted with a formulation based on lactose
    and microcrystalline cellulose. Granules were analyzed with a Powder Vertical
    Shear Rig. We identified the compression density (ρpress) as the said alternative,
    linking information from the powder material and the moisture content (R2 = 0.995).
    We used ρpress to successfully predict liquid requirements for unknown formulation
    compositions. By means of this prediction, pellets with high quality, regarding
    shape and size distribution, were produced by carrying out a multi-step manufacturing
    process. Furthermore, the applicability of ρpress as an alternative quality parameter
    to other placebo formulations and to formulations containing active pharmaceutical
    ingredients (APIs) was demonstrated.
article_number: '2303'
author:
- first_name: Selina
  full_name: Ramm, Selina
  id: '68713'
  last_name: Ramm
  orcid: https://orcid.org/0000-0002-0502-8032
- first_name: Ruwen
  full_name: Fulek, Ruwen
  id: '79527'
  last_name: Fulek
- first_name: Veronika Anna
  full_name: Eberle, Veronika Anna
  last_name: Eberle
- first_name: Christian
  full_name: Kiera, Christian
  last_name: Kiera
- first_name: Ulrich
  full_name: Odefey, Ulrich
  id: '74218'
  last_name: Odefey
- first_name: Miriam
  full_name: Pein-Hackelbusch, Miriam
  id: '64952'
  last_name: Pein-Hackelbusch
  orcid: 0000-0002-7920-0595
citation:
  ama: Ramm S, Fulek R, Eberle VA, Kiera C, Odefey U, Pein-Hackelbusch M. Compression
    Density as an Alternative to Identify an Optimal Moisture Content for High Shear
    Wet Granulation as an Initial Step for Spheronisation. <i>Pharmaceutics</i>. 2022;14(11).
    doi:<a href="https://doi.org/10.3390/pharmaceutics14112303">https://doi.org/10.3390/pharmaceutics14112303</a>
  apa: Ramm, S., Fulek, R., Eberle, V. A., Kiera, C., Odefey, U., &#38; Pein-Hackelbusch,
    M. (2022). Compression Density as an Alternative to Identify an Optimal Moisture
    Content for High Shear Wet Granulation as an Initial Step for Spheronisation.
    <i>Pharmaceutics</i>, <i>14</i>(11), Article 2303. <a href="https://doi.org/10.3390/pharmaceutics14112303">https://doi.org/10.3390/pharmaceutics14112303</a>
  bjps: <b>Ramm S <i>et al.</i></b> (2022) Compression Density as an Alternative to
    Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial
    Step for Spheronisation. <i>Pharmaceutics</i> <b>14</b>.
  chicago: Ramm, Selina, Ruwen Fulek, Veronika Anna Eberle, Christian Kiera, Ulrich
    Odefey, and Miriam Pein-Hackelbusch. “Compression Density as an Alternative to
    Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial
    Step for Spheronisation.” <i>Pharmaceutics</i> 14, no. 11 (2022). <a href="https://doi.org/10.3390/pharmaceutics14112303">https://doi.org/10.3390/pharmaceutics14112303</a>.
  chicago-de: Ramm, Selina, Ruwen Fulek, Veronika Anna Eberle, Christian Kiera, Ulrich
    Odefey und Miriam Pein-Hackelbusch. 2022. Compression Density as an Alternative
    to Identify an Optimal Moisture Content for High Shear Wet Granulation as an Initial
    Step for Spheronisation. <i>Pharmaceutics</i> 14, Nr. 11. doi:<a href="https://doi.org/10.3390/pharmaceutics14112303">https://doi.org/10.3390/pharmaceutics14112303</a>,
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Ramm, Selina</span> ; <span
    style="font-variant:small-caps;">Fulek, Ruwen</span> ; <span style="font-variant:small-caps;">Eberle,
    Veronika Anna</span> ; <span style="font-variant:small-caps;">Kiera, Christian</span>
    ; <span style="font-variant:small-caps;">Odefey, Ulrich</span> ; <span style="font-variant:small-caps;">Pein-Hackelbusch,
    Miriam</span>: Compression Density as an Alternative to Identify an Optimal Moisture
    Content for High Shear Wet Granulation as an Initial Step for Spheronisation.
    In: <i>Pharmaceutics</i> Bd. 14. Basel, MDPI (2022), Nr. 11'
  havard: S. Ramm, R. Fulek, V.A. Eberle, C. Kiera, U. Odefey, M. Pein-Hackelbusch,
    Compression Density as an Alternative to Identify an Optimal Moisture Content
    for High Shear Wet Granulation as an Initial Step for Spheronisation., Pharmaceutics.
    14 (2022).
  ieee: 'S. Ramm, R. Fulek, V. A. Eberle, C. Kiera, U. Odefey, and M. Pein-Hackelbusch,
    “Compression Density as an Alternative to Identify an Optimal Moisture Content
    for High Shear Wet Granulation as an Initial Step for Spheronisation.,” <i>Pharmaceutics</i>,
    vol. 14, no. 11, Art. no. 2303, 2022, doi: <a href="https://doi.org/10.3390/pharmaceutics14112303">https://doi.org/10.3390/pharmaceutics14112303</a>.'
  mla: Ramm, Selina, et al. “Compression Density as an Alternative to Identify an
    Optimal Moisture Content for High Shear Wet Granulation as an Initial Step for
    Spheronisation.” <i>Pharmaceutics</i>, vol. 14, no. 11, 2303, 2022, <a href="https://doi.org/10.3390/pharmaceutics14112303">https://doi.org/10.3390/pharmaceutics14112303</a>.
  short: S. Ramm, R. Fulek, V.A. Eberle, C. Kiera, U. Odefey, M. Pein-Hackelbusch,
    Pharmaceutics 14 (2022).
  ufg: '<b>Ramm, Selina u. a.</b>: Compression Density as an Alternative to Identify
    an Optimal Moisture Content for High Shear Wet Granulation as an Initial Step
    for Spheronisation., in: <i>Pharmaceutics</i> 14 (2022), H. 11.'
  van: Ramm S, Fulek R, Eberle VA, Kiera C, Odefey U, Pein-Hackelbusch M. Compression
    Density as an Alternative to Identify an Optimal Moisture Content for High Shear
    Wet Granulation as an Initial Step for Spheronisation. Pharmaceutics. 2022;14(11).
date_created: 2023-03-03T11:23:01Z
date_updated: 2025-07-29T13:22:53Z
department:
- _id: DEP4022
- _id: DEP4028
- _id: DEP4014
doi: https://doi.org/10.3390/pharmaceutics14112303
external_id:
  isi:
  - '000883998100001'
  pmid:
  - '36365122'
intvolume: '        14'
isi: '1'
issue: '11'
keyword:
- wet granulation
- liquid requirement
- granulation endpoint
- compression density
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.mdpi.com/1999-4923/14/11/2303
oa: '1'
place: Basel
pmid: '1'
publication: Pharmaceutics
publication_identifier:
  eissn:
  - 1999-4923
publication_status: published
publisher: MDPI
status: public
title: Compression Density as an Alternative to Identify an Optimal Moisture Content
  for High Shear Wet Granulation as an Initial Step for Spheronisation.
type: scientific_journal_article
user_id: '83781'
volume: 14
year: '2022'
...