Ljungqvist, Martin Georg1; Nielsen, Otto Højager Attermann1; Frosch, Stina7; Nielsen, Michael Engelbrecht8; Clemmensen, Line Katrine Harder1; Ersbøll, Bjarne Kjær1
1 Department of Applied Mathematics and Computer Science, Technical University of Denmark2 Image Analysis & Computer Graphics, Department of Applied Mathematics and Computer Science, Technical University of Denmark3 National Food Institute, Technical University of Denmark4 Division of Industrial Food Research, National Food Institute, Technical University of Denmark5 Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark6 Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark7 Department of Systems Biology, Technical University of Denmark8 Enzyme and Protein Chemistry, Department of Systems Biology, Technical University of Denmark
We present a study on predicting the concentration level of synthetic astaxanthin in fish feed pellet coating using multi- and hyperspectral image analysis. This was done in parallel using two different vision systems. A new instrument for hyperspectral imaging, the SuperK setup, using a super-continuum laser as the light source was introduced. Furthermore, a parallel study with the commercially available multispectral VideometerLab imaging system was performed. The SuperK setup used 113 spectral bands (455–1,015 nm), and the VideometerLab used 20 spectral bands (385–1,050 nm). To predict the astaxanthin concentration from the spectral image data, the synthetic astaxanthin content in the pellets was measured with the established standard technique; high-pressure liquid chromatography (HPLC). Regression analysis was done using partial least squares regression (PLSR) and the sparse regression method elastic net (EN). The ratio of standard error of prediction (RPD) is the ratio between the standard deviation of the reference values and the prediction error, and for both PLSR and EN both devices gave RPD values between 4 and 24, and with mean prediction error of 1.4–8.0 parts per million of astaxanthin concentration. The results show that it is possible to predict the synthetic astaxanthin concentration in the coating well enough for quality control using both multi- and hyperspectral image analysis, while the SuperK setup performs with higher accuracy than the VideometerLab device for this particular problem. The spectral resolution made it possible to identify the most significant spectral regions for detection of astaxanthin. The results also imply that the presented methods can be used in general for quality inspection of various coating substances using similar coating methods.
Machine Vision and Applications, 2014, Vol 25, Issue 2, p. 327-343