In this paper, the near-infrared spectral data of five different types of starch were collected, and the starch species identification model was constructed by using a quaternion convolutional neural network (QCNN), we proved that the qualitative model based on QCNN has obtained higher prediction accuracy than traditional qualitative models. In the experimental results, the classification accuracy of QCNN for five different starches reached 0.996. The results show that the combination of the quaternion spectral fusion method and deep learning is more conducive to extracting and mining the deep information of NIR spectra and has important research significance and application value in the field of near-infrared spectroscopy technology
Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter σ of gasoline, diesel and kerosene model were 48.1418 and 0.1067, 53.2820 and 0.1095, 59.1689 and 0.1000 respectively. The decision coefficient R2 of the prediction model were 0.9983, 0.9907 and 0.9942 respectively. RMSEP values were 0.0753, 0.1539 and 0.0789 respectively. For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176±0.0636 μL/mL, -0.0084±0.1941 μL/mL, and 0.00338±0.0726 μL/mL respectively. The results showed that each component’s concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.
We studied the optical forces acting on a microscopic object generated by the laser beams from lensed optical fiber ends
to research the parametric influence on the optical trapping. From these investigations, we verified that there was only
one stable point of equilibrium located below the beam-crossing point, and revealed that the radius of a hemispherical
microlens of an optical fiber end, the horizontal distance between each optical fiber end and the particle size are
important parameters for strong three-dimensional trapping without physical contact.
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