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• W2916967278 abstract "Rapid monitoring with near-infrared (NIR) spectroscopy of Saccharomyces cerevisiae cultivations was implemented to monitor yeast concentrations. The measurement of one spectrum by using of FT-NIR spectrometer can obtain 1557 wavelength variables. To distinguish which wavelength variables of the collected FT-NIR spectra carry important and relevant information regarding the yeast concentrations, there are three different variables selection approaches, namely genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and variable combination population analysis (VCPA), were compared in this study. The selected wavelength variables from each method were evaluated using partial least squares (PLS) models to seek the most significant variable combinations for predicting the yeast concentrations. Experimental results showed that the VCPA-PLS model with the best predictive performance was found when using ten principal components (PCs) based on selected eleven characteristic wavelength variables by VCPA algorithm. And the predictive performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0680, the coefficient of determination (Rp2) was 0.9924, and the ratio performance deviation (RPD) was 11.8625 in the validation process. Based on the results, it is promising to develop a specific inexpensive NIR sensor for the yeast cultivation process using several light-emitting diodes." @default.
• W2916967278 created "2019-03-02" @default.
• W2916967278 creator A5027466981 @default.
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• W2916967278 date "2019-05-01" @default.
• W2916967278 modified "2023-10-12" @default.
• W2916967278 title "Comparison of algorithms for wavelength variables selection from near-infrared (NIR) spectra for quantitative monitoring of yeast (Saccharomyces cerevisiae) cultivations" @default.
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• W2916967278 doi "https://doi.org/10.1016/j.saa.2019.02.038" @default.
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