Dual stacked partial least squares for analysis of near-infrared spectra. 2013

Yiming Bi, and Qiong Xie, and Silong Peng, and Liang Tang, and Yong Hu, and Jie Tan, and Yuhui Zhao, and Changwen Li
Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China.

A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications.

UI MeSH Term Description Entries
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016018 Least-Squares Analysis A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model. Rietveld Refinement,Analysis, Least-Squares,Least Squares,Analyses, Least-Squares,Analysis, Least Squares,Least Squares Analysis,Least-Squares Analyses,Refinement, Rietveld
D019265 Spectroscopy, Near-Infrared A noninvasive technique that uses the differential absorption properties of hemoglobin and myoglobin to evaluate tissue oxygenation and indirectly can measure regional hemodynamics and blood flow. Near-infrared light (NIR) can propagate through tissues and at particular wavelengths is differentially absorbed by oxygenated vs. deoxygenated forms of hemoglobin and myoglobin. Illumination of intact tissue with NIR allows qualitative assessment of changes in the tissue concentration of these molecules. The analysis is also used to determine body composition. NIR Spectroscopy,Spectrometry, Near-Infrared,NIR Spectroscopies,Near-Infrared Spectrometries,Near-Infrared Spectrometry,Near-Infrared Spectroscopies,Near-Infrared Spectroscopy,Spectrometries, Near-Infrared,Spectrometry, Near Infrared,Spectroscopies, NIR,Spectroscopies, Near-Infrared,Spectroscopy, NIR,Spectroscopy, Near Infrared

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