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Hu Y.,CAS Institute of Automation | Peng S.,CAS Institute of Automation | Peng J.,CAS Institute of Automation | Wei J.,Food Research Institute of Tianshili Group
Talanta | Year: 2012

Traditional ensemble regression algorithms such as BAgging Partial Least Squares (BAPLS) and BOosting Partial Least Squares (BOPLS) do not perform very well in the data set that is relatively small or contaminated by random noise. To make the method robust and improve its prediction ability, inspired from bias-variance-covariance decomposition, we propose an improved ensemble partial least squares method based on the diversity. The new method is applied to quantitative analysis of Near InfraRed (NIR) data sets. A comparative study between the proposed method and other previous methods including BAPLS and BOPLS on two NIR data sets is provided. Experimental results show that the proposed method can achieve better performance than other methods. © 2012 Elsevier B.V. All rights reserved.

Peng J.,CAS Institute of Automation | Peng S.,CAS Institute of Automation | Xie Q.,CAS Institute of Automation | Wei J.,Food Research Institute of Tianshili Group
Analytica Chimica Acta | Year: 2011

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7-19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33-68%). © 2011 Elsevier B.V.

Jiang A.,CAS Institute of Automation | Peng J.-T.,CAS Institute of Automation | Peng S.-L.,CAS Institute of Automation | Wei J.-P.,Food Research Institute of Tianshili Group | Li C.-W.,Food Research Institute of Tianshili Group
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2010

Chinese liquor is a complex mixture and contains a large amount of microconstituents, which affects the quality and flavor of liquor. In order to discriminate liquor flavors rapidly, the spectra of liquors were obtained by FTIR and employed as the input patterns of pattern classification algorithms, then liquor flavor discrimination models were built. This paper introduces liquor flavor pattern recognition algorithms comprehensively and systematically for the first time, and the algorithms contain statistical classifications (linear discriminant function, quadratic discriminant function, regularized discriminant analysis, and K nearest neighbor), prototype learning algorithm (learning vector quantization), support vector machine and adaboost algorithm. Experimental results show that the liquor flavor classification algorithms demonstrate good performance and achieve high accuracy, recognition rate and rejection rate.

Peng J.,CAS Institute of Automation | Peng S.,CAS Institute of Automation | Jiang A.,CAS Institute of Automation | Wei J.,Food Research Institute of Tianshili Group | And 2 more authors.
Analytica Chimica Acta | Year: 2010

In this paper, based on asymmetric least squares smoothing, a new algorithm for multiple spectra baseline correction is proposed. By means of the similarity among the multiple spectra, the algorithm estimates the baselines by penalizing the differences in the baseline corrected signals, which makes the algorithm possible to eliminate scatter effects on the spectra. In addition, a relaxation factor which measures the similarity of the baseline corrected spectra is incorporated into the optimization model and an alternate iteration strategy is used to solve the optimization problem. The proposed algorithm is fast and can output multiple baselines simultaneously. Experimental results on both simulated data and real data demonstrate the effectiveness and efficiency of the algorithm. © 2010 Elsevier B.V.

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