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Hyperspectral imaging based on diffused laser light for prediction of astaxanthin coating concentration

机译：基于散射激光的高光谱成像预测虾青素涂层浓度

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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.
机译：我们提出了使用多光谱和高光谱图像分析预测鱼饲料颗粒涂层中合成虾青素浓度水平的研究。这是使用两个不同的视觉系统并行完成的。介绍了一种新的用于高光谱成像的仪器，即SuperK设置，它使用超连续激光作为光源。此外，使用市售的多光谱VideometerLab成像系统进行了平行研究。 SuperK设置使用113个光谱带（455-1,015 nm），而VideometerLab使用20个光谱带（385-1,050 nm）。为了从光谱图像数据中预测虾青素的浓度，采用建立的标准技术测量了颗粒中合成虾青素的含量。高压液相色谱（HPLC）。使用偏最小二乘回归（PLSR）和稀疏回归方法弹性网（EN）进行回归分析。预测标准误差（RPD）的比率是参考值的标准偏差与预测误差之间的比率，对于PLSR和EN，两种设备的RPD值均在4到24之间，平均预测误差为1.4-虾青素浓度为百万分之8.0。结果表明，使用多光谱和高光谱图像分析可以很好地预测涂料中合成虾青素的浓度，以进行质量控制，而针对此特定问题，SuperK装置的性能要比VideometerLab设备更高。光谱分辨率使得可以鉴定用于虾青素检测的最重要的光谱区域。结果还暗示，所提出的方法可以总体上用于使用类似涂覆方法的各种涂覆物质的质量检查。

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