Use of hyperspectral imaging for cake moisture and hardness prediction

Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually...

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Bibliographic Details
Published in:IET image processing 2019-05, Vol.13 (7), p.1152-1160
Main Authors: Polak, Adam, Coutts, Fraser K, Murray, Paul, Marshall, Stephen
Format: Article
Language:English
Subjects:
cake moisture
cake production environment
cake quality monitoring
food processing industry
food products
hardness prediction
hyperspectral imaging systems
image processing
image processing techniques
image sensors
industrial baking
lengthy process
moisture
product quality
production line
quality control
quality indicators
Research Article
single sensor hyperspectral imaging
sophisticated signal
sponge cakes
sponge moisture content
texture properties
time 20.0 min
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Description
Summary:Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on-line, real-time, stand-off cake quality monitoring.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2018.5106