Open AccessUse of hyperspectral imaging for cake moisture and hardness prediction
Author(s) -
Polak Adam,
Coutts Fraser K.,
Murray Paul,
Marshall Stephen
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5106
Subject(s) - hyperspectral imaging , water content , production line , production (economics) , computer science , process engineering , quality (philosophy) , moisture , environmental science , remote sensing , measure (data warehouse) , process (computing) , agricultural engineering , artificial intelligence , data mining , materials science , engineering , mechanical engineering , geography , composite material , geotechnical engineering , philosophy , epistemology , economics , macroeconomics , operating system
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.