Use of hyperspectral imaging for cake moisture and hardness prediction

Adam Polak, Fraser Kenneth Coutts, Paul Murray, Stephen Marshall

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
52 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1152-1160
Number of pages9
JournalIET Image Processing
Issue number7
Early online date22 Mar 2019
Publication statusE-pub ahead of print - 22 Mar 2019


  • hyperspectral images
  • image processing techniques
  • prediction algorithms
  • food industry


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