In-line Monitoring of Printing Processes by NIR Reflection Spectroscopy

Mirschel, Gabriele; Daikos, Olesya; Heymann, Katja; Steckert, Carsten; Genest, Beatrix; Sommerer, Carolin

doi:10.17648/nir-2015-31693

Cited by 1 publication

(1 citation statement)

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“…The inherent spectral data acquired by these systems, combined with appropriate chemometric analysis can provide a powerful tool for quality control. On-line applications of HSI range from the mining industry [3]- [5], through to the textile, pharmaceuticals [6]- [10], and many other key industry sectors. The application of HSI also has enormous potential in the food industry [3], [11]- [14]; here, the most common on-line applications of HSI are for the inspection and segregation of agricultural crops and produce [13], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Use of hyperspectral imaging for cake moisture and hardness prediction

Polak

Coutts

Murray

et al. 2019

IET Image Processing

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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 minutes to complete per sponge. In this paper, we 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 paper has the potential to provide on-line, real-time, stand-off cake quality monitoring.

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