Formulated liquids, such as detergents and fabric softeners, are of great relevance in the industry of everyday products. Although thermodynamically unstable, these liquids must guarantee a minimum stable 'shelf life' for commercialization. Therefore, the industry devotes a major effort to assess their sta-bility, and to develop reliable instability detectors. Among the many approaches proposed to this end, solutions based on the visual inspection of samples present numerous advantages: they are cheap, easy to perform, non-intrusive, repeatable. However, conventional image-processing-based detectors do not ensure a sufficient reliability. In this paper, we propose to use deep learning-based classifiers, thus we explore several architectural choices to guarantee the desired performance with a limited computational cost, despite the relatively small datasets available for this task. Experimental results are extremely encouraging and show that deep learning is a reliable and very promising solution for detecting instabil-ities in formulated liquids. (c) 2022 Elsevier Ltd. All rights reserved.

Stability assessment of liquid formulations: A deep learning approach

Gragnaniello, D;
2022-01-01

Abstract

Formulated liquids, such as detergents and fabric softeners, are of great relevance in the industry of everyday products. Although thermodynamically unstable, these liquids must guarantee a minimum stable 'shelf life' for commercialization. Therefore, the industry devotes a major effort to assess their sta-bility, and to develop reliable instability detectors. Among the many approaches proposed to this end, solutions based on the visual inspection of samples present numerous advantages: they are cheap, easy to perform, non-intrusive, repeatable. However, conventional image-processing-based detectors do not ensure a sufficient reliability. In this paper, we propose to use deep learning-based classifiers, thus we explore several architectural choices to guarantee the desired performance with a limited computational cost, despite the relatively small datasets available for this task. Experimental results are extremely encouraging and show that deep learning is a reliable and very promising solution for detecting instabil-ities in formulated liquids. (c) 2022 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4860070
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