Thickness measurement of insulation coating by NIR spectrometry based on boosting-KPLS.

A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored. The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable filter (AOTF) NIR spectrometer. To make full use of the effective information of NIR spectral data, discrete binary particle swarm optimization (DBPSO) algorithm was used to select the optimal wavelength variates.

[Analysis of mixed alkane gas based on Tikhonov regularization spectra selection and optimal neural network parameters selection].

Feature variable selection and modeling are two of the most principal research contents in spectral analysis. In the present paper, beginning from the introduction of feature spectrum selection based on Tikhonov regularization and discussion on it's application in multi-component mixed alkane gas analysis, 7 sets of feature spectra were abstracted from the absorption spectra of 7 kinds of alkane gas, including methane, ethane, propane, iso-butane, n-butane, iso-pentane and n-pentane. In order to overcome the problem of over-training of neural network, a method called optimal parameter selection of neural netework (NN) was presented to build analysis model of analyte.

[Exploratory research on quantitative analysis of gaseous mixtures by AOTF-NIR spectrometer].

Due to its many advantages, such as miniaturization, high accuracy, high resolution, fast scanning speed, increased robustness and good stability, acousto-optic tunable filter (AOTF)-near infrared (NIR) spectrometer has been successfully applied in many fields. However, up to now, the commercial AOTF-NIR spectrometers can only be used for liquid and solid detection, but not for the detection of gaseous samples. In the present paper, the feasibility of quantitative analysis of gaseous mixtures by using AOTF-NIR spectrometer was investigated.

[Quantitative analysis of multi-component gas mixture based on KPCA and SVR].

In the present paper, the authors present a new quantitative analysis method of mid-infrared spectrum. The method combines the kernel principal component analysis (KPCA) technique with support vector regress machine (SVR) to createa quantitative analysis model of multi-component gas mixtures. Firstly, the spectra of multi-component gas mixtures samples were mapped nonlinearly into a high-dimensional feature space through the use of Gaussian kernels.