Machine vision for the quality assessment of emulsions in pharmaceutical processing

Saritha Unnikrishnan; John Donovan; Russell Macpherson; David Tormey

doi:10.1109/UV.2018.8642158

Machine vision for the quality assessment of emulsions in pharmaceutical processing

Saritha Unnikrishnan
, John Donovan
, Russell Macpherson
, David Tormey

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Emulsion quality evaluation using machine vision techniques depends on the efficiency of the image segmentation algorithms. Two different machine vision techniques are investigated to determine their competency in detecting droplets from in-process microscopic images of a cream emulsion. Histogram-based segmentation shows promising potential compared to edge and symmetry detection. A statistical study of the droplet characteristics was conducted. The results demonstrate that the histogram-based approach is more proficient in the progressive analysis of droplet evolution during emulsification. A real-time integration of the technique is proposed, as a soft sensor, to predict the optimum process time and to increase manufacturing efficiency in chemical industries.

Original language	English
Title of host publication	4th IEEE International Conference on Universal Village 2018, UV 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538651971
DOIs	https://doi.org/10.1109/UV.2018.8642158
Publication status	Published - 2 Jul 2018
Event	4th IEEE International Conference on Universal Village, UV 2018 - Boston, United States Duration: 21 Oct 2018 → 24 Oct 2018

Publication series

Name	4th IEEE International Conference on Universal Village 2018, UV 2018

Conference

Conference	4th IEEE International Conference on Universal Village, UV 2018
Country/Territory	United States
City	Boston
Period	21/10/18 → 24/10/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

automated image analysis
droplet characteristics
emulsion
machine vision
quality evaluation
sustainable manufacturing

Access to Document

10.1109/UV.2018.8642158

Cite this

Unnikrishnan, S., Donovan, J., Macpherson, R., & Tormey, D. (2018). Machine vision for the quality assessment of emulsions in pharmaceutical processing. In 4th IEEE International Conference on Universal Village 2018, UV 2018 Article 8642158 (4th IEEE International Conference on Universal Village 2018, UV 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UV.2018.8642158

@inproceedings{02db4f7b145b4038bb6f0f58016f9ea1,

title = "Machine vision for the quality assessment of emulsions in pharmaceutical processing",

abstract = "Emulsion quality evaluation using machine vision techniques depends on the efficiency of the image segmentation algorithms. Two different machine vision techniques are investigated to determine their competency in detecting droplets from in-process microscopic images of a cream emulsion. Histogram-based segmentation shows promising potential compared to edge and symmetry detection. A statistical study of the droplet characteristics was conducted. The results demonstrate that the histogram-based approach is more proficient in the progressive analysis of droplet evolution during emulsification. A real-time integration of the technique is proposed, as a soft sensor, to predict the optimum process time and to increase manufacturing efficiency in chemical industries.",

keywords = "automated image analysis, droplet characteristics, emulsion, machine vision, quality evaluation, sustainable manufacturing",

author = "Saritha Unnikrishnan and John Donovan and Russell Macpherson and David Tormey",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Conference on Universal Village, UV 2018 ; Conference date: 21-10-2018 Through 24-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/UV.2018.8642158",

language = "English",

series = "4th IEEE International Conference on Universal Village 2018, UV 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "4th IEEE International Conference on Universal Village 2018, UV 2018",

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Unnikrishnan, S, Donovan, J, Macpherson, R & Tormey, D 2018, Machine vision for the quality assessment of emulsions in pharmaceutical processing. in 4th IEEE International Conference on Universal Village 2018, UV 2018., 8642158, 4th IEEE International Conference on Universal Village 2018, UV 2018, Institute of Electrical and Electronics Engineers Inc., 4th IEEE International Conference on Universal Village, UV 2018, Boston, United States, 21/10/18. https://doi.org/10.1109/UV.2018.8642158

Machine vision for the quality assessment of emulsions in pharmaceutical processing. / Unnikrishnan, Saritha; Donovan, John; Macpherson, Russell et al.
4th IEEE International Conference on Universal Village 2018, UV 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8642158 (4th IEEE International Conference on Universal Village 2018, UV 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine vision for the quality assessment of emulsions in pharmaceutical processing

AU - Unnikrishnan, Saritha

AU - Donovan, John

AU - Macpherson, Russell

AU - Tormey, David

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Emulsion quality evaluation using machine vision techniques depends on the efficiency of the image segmentation algorithms. Two different machine vision techniques are investigated to determine their competency in detecting droplets from in-process microscopic images of a cream emulsion. Histogram-based segmentation shows promising potential compared to edge and symmetry detection. A statistical study of the droplet characteristics was conducted. The results demonstrate that the histogram-based approach is more proficient in the progressive analysis of droplet evolution during emulsification. A real-time integration of the technique is proposed, as a soft sensor, to predict the optimum process time and to increase manufacturing efficiency in chemical industries.

AB - Emulsion quality evaluation using machine vision techniques depends on the efficiency of the image segmentation algorithms. Two different machine vision techniques are investigated to determine their competency in detecting droplets from in-process microscopic images of a cream emulsion. Histogram-based segmentation shows promising potential compared to edge and symmetry detection. A statistical study of the droplet characteristics was conducted. The results demonstrate that the histogram-based approach is more proficient in the progressive analysis of droplet evolution during emulsification. A real-time integration of the technique is proposed, as a soft sensor, to predict the optimum process time and to increase manufacturing efficiency in chemical industries.

KW - automated image analysis

KW - droplet characteristics

KW - emulsion

KW - machine vision

KW - quality evaluation

KW - sustainable manufacturing

UR - https://www.scopus.com/pages/publications/85063126143

U2 - 10.1109/UV.2018.8642158

DO - 10.1109/UV.2018.8642158

M3 - Conference contribution

AN - SCOPUS:85063126143

T3 - 4th IEEE International Conference on Universal Village 2018, UV 2018

BT - 4th IEEE International Conference on Universal Village 2018, UV 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Universal Village, UV 2018

Y2 - 21 October 2018 through 24 October 2018

ER -

Unnikrishnan S, Donovan J, Macpherson R, Tormey D. Machine vision for the quality assessment of emulsions in pharmaceutical processing. In 4th IEEE International Conference on Universal Village 2018, UV 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8642158. (4th IEEE International Conference on Universal Village 2018, UV 2018). doi: 10.1109/UV.2018.8642158

Machine vision for the quality assessment of emulsions in pharmaceutical processing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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