Hybrid tilted fiber grating based refractive index and liquid level sensing system

We report a refractive index (RI) and liquid level sensing system based on a hybrid grating structure comprising of a 45° and an 81° tilted fiber gratings (TFGs) that have been inscribed into a single mode fiber in series. In this structure, the 45°-TFG is used as a polarizer to filter out the transverse electric (TE) component and enable the 81°-TFG operating at single polarization for RI and level sensing. The experiment results show a lower temperature cross-sensitivity, only about 7.33 pm/°C, and a higher RI sensitivity, being around 180 nm/RIU at RI=1.345 and 926 nm/RIU at RI=1.412 region, which are significantly improved in comparison with long period fiber gratings. The hybrid grating structure has also been applied as a liquid level sensor, showing 3.06 dB/mm linear peak ratio sensitivity.

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.