Quality assessment of butter cookies applying multispectral imaging
Article first published online: 12 JUN 2013
© 2013 The Authors. Food Science & Nutrition published by Wiley Periodicals, Inc.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Food Science & Nutrition
Volume 1, Issue 4, pages 315–323, July 2013
How to Cite
Andresen, M. S., Dissing, B. S. and Løje, H. (2013), Quality assessment of butter cookies applying multispectral imaging. Food Science & Nutrition, 1: 315–323. doi: 10.1002/fsn3.46
- Issue published online: 14 JUL 2013
- Article first published online: 12 JUN 2013
- Manuscript Accepted: 21 APR 2013
- Manuscript Revised: 2 APR 2013
- Manuscript Received: 4 MAR 2013
- Danish Ministry of Food, Agriculture and Fisheries
- food technology;
- food quality;
- multispectral imaging;
- water content
A method for characterization of butter cookie quality by assessing the surface browning and water content using multispectral images is presented. Based on evaluations of the browning of butter cookies, cookies were manually divided into groups. From this categorization, reference values were calculated for a statistical prediction model correlating multispectral images with a browning score. The browning score is calculated as a function of oven temperature and baking time. It is presented as a quadratic response surface. The investigated process window was the intervals 4–16 min and 160–200°C in a forced convection electrically heated oven. In addition to the browning score, a model for predicting the average water content based on the same images is presented. This shows how multispectral images of butter cookies may be used for the assessment of different quality parameters. Statistical analysis showed that the most significant wavelengths for browning predictions were in the interval 400–700 nm and the wavelengths significant for water prediction were primarily located in the near-infrared spectrum. The water prediction model was found to correctly estimate the average water content with an absolute error of 0.22%. From the images it was also possible to follow the browning and drying propagation from the cookie edge toward the center.