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Hong-fu Yuan

Publications and source records attributed to Hong-fu Yuan.

4 recordsLinked to original sources

[A new calibration transfer method based on target factor analysis].

A new calibration transfer method based on target factor analysis is proposed.The performance of the new method compared with the piecewise direct standardization method. This method was applied to two data sets, of which one is a simulation data set, and the other is an NIR data set composed of benzene, toluene, xylene and isooctane. The results obtained with this new method are at least as well as those obtained by PDS with the biggest improvement occurring when the spectra have some non-linear responses.

Algorithms↗

[Near infrared spectra (NIR) analysis of octane number by wavelet denoising-derivative method].

Derivative can correct baseline effects and also increase the level of noise. Wavelet transform has been proven an efficient tool for de-noising. This paper is directed to the application of wavelet transfer and derivative in the NIR analysis of octane number (RON). The derivative parameters, as well as their effects on the noise level and analytic accuracy of RON, have been studied in detail. The results show that derivative can correct the baseline effects and increase the analytic accuracy. Noise from the derivative spectra has great detriment to the analysis of RON. De-noising of wavelet transform can increase the S/N and improve the analytical accuracy.

Algorithms↗

[Effects of the accuracy of reference data on NIR prediction results].

Reference data are indispensable to build near-infrared spectroscopy (NIR)calibration models. In the present paper, the effects of the accuracy of reference data on NIR calibration models and its prediction results were studied through two routine applications based on partial least square regression methods. The results indicate that the best NIR calibration statistics and the most accurate prediction results were aligned with the most accurate reference data. However, based on statistical analysis of numerous calibration samples, it is possible for NIR calibration models to obtain more accurate prediction results than the laboratory reference data used in the calibration sets. It is better to make less search for high accurate reference data and instead to introduce more calibration samples to improve the ruggedness of the calibration models.

Benzene↗

[Developing robust near infrared calibration models].

There are three approaches to developing robust near infrared calibration models, including spectral pretreatment such as differentiation, Piecewise Multiplicative Scatter Correction (PMSC), Finite Impulse Response (FIR), and Orthogonal Signal Correction (OSC), to remove external variations, selecting wavelengths which are insensitive to external variations, and constructing temperature-hybrid calibration models. In this paper, these three strategies were investigated based on reforming gasoline NIR spectra collected at different temperatures in order to develop robust RON and benzene calibration models against temperature. It has been found that with only spectral pretreatment even OSC method fails to obtain satisfactory results, which could not remove the effects caused by temperature fluctuation. Selecting wavelengths by genetic algorithms and constructing temperature-hybrid calibration models, in which spectra measured at different temperature are combined into one calibration set, are both good approaches to developing robust NIR calibration models against temperature. The latter seems better because it needs no special knowledge and extra software, but thenon-linear effects should be considered in practical applications.

Calibration↗