A strategy for enhancing the reliability of near-infrared spectral analysis |
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| Institution: | 1. Faculty of Foundry Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland;2. Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, 30-059 Krakow, Poland;3. Department of Chemistry, Hankuk University of Foreign Studies, Yongin, Kyunggi-Do 449-791, Republic of Korea;1. Department of Physics, SCE⬝Shamoon College of Engineering, Beer-Sheva 84100, Israel;2. Department of Physics, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel;3. Department of Medicine, University of Virginia, Charlottesville, VA 22901, USA;4. Surgery B Department, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva 84101, Israel;1. Department of Neurobiology, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, 31-343 Kraków, Poland;2. Faculty of Foundry Engineering, AGH University of Science and Technology, ul. Reymonta 23, 30-059 Kraków, Poland;3. Institute of Nuclear Physics, Polish Academy of Science, 31-342 Krakow, Poland;4. Faculty of Chemistry, Jagiellonian University, ul. Ingardena 3, 30-060 Kraków, Poland;5. Department of Chemistry, Hankuk University of Foreign Studies, Yongin, Kyunggi-Do, 449-791, South Korea |
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| Abstract: | Near-infrared (NIR) spectroscopy will present a more promising tool for quantitative measurement if the reliability of the calibration model is further improved. To achieve this purpose, a new partial least squares (PLSs) technique based on Monte Carlo (MC) resampling is proposed, which is named as MCPLS. In this method, the outliers are firstly removed based on probability statistics. Then, the models without outliers are averaged and combined into a single prediction model as done in a consensus modeling, which can greatly enhance the reliability of PLS calibration. To validate the effectiveness and universality of the proposed method, it was applied to two different sets of NIR spectra. It was found that MCPLS could effectively avoid the swamping and masking effects caused by multiple outliers. The results show that the method is of value to enhance the reliability of PLS model involving complex NIR matrices with a small number of outliers. |
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