Rapid prediction of multiple wine quality parameters using infrared spectroscopy coupling with chemometric methods |
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| Affiliation: | 1. Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding, 071003, PR China;2. MOE Key Laboratory of Resources and Environmental Systems Optimization, Beijing, 102206, PR China;1. Department of Chemistry, College of Natural and Computational Sciences, Haramaya University, Oromia, Ethiopia;2. School of Water Resources and Environment, Research Center of Environmental Science and Engineering, China University of Geosciences (Beijing), 100083 Beijing, China;3. School of Plant Sciences, College of Agriculture and Environmental Sciences, Haramaya University, Oromia, Ethiopia;1. University of Gdańsk, Environmental Chemistry and Ecotoxicology, 63 Wita Stwosza Street, 80-308 Gdańsk, Poland;2. Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, Cartagena, Colombia;3. Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China;4. Institute of Meteorology and Water Management – Maritime Branch, National Research Institute, 42 Waszyngtona Av., 81-342 Gdynia, Poland;5. School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK;1. College of Food and Biological Engineering, Jimei University, Xiamen 361021, China;2. Xiamen Huaxia University, Xiamen 361021, China;1. Program of Research and Analysis of Chemical Residues and Contaminants (PRINARC), Faculty of Chemical Engineering, National University of Littoral, Santa Fe, Argentina;2. National Institute of Agricultural Technology (INTA), EEA Concepción del Uruguay, Entre Ríos, Argentina |
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| Abstract: | Infrared spectroscopy (IRs) coupling with chemometric methods were used to predict principal quality parameters in wine. A new strategy of variable (wavelength) selection named as Fisher Discriminant-Variable Selection (FD-VS) model was constructed. Characteristic variables were selected from Infrared spectra based on the absolute values of eigenvector obtained by Fisher Discriminant Function. The FD-VS method was combined with quantitative models including Principal Component Regression (PCR), Partial Least Squares (PLS) and Least Squares Support Vector Regression (LSSVR), which were utilized for prediction of multiple principal quality parameters of red wine. It is shown that FD-VS method obviously improves the performances of PCR, PLS and LSSVR models. Then four variable selection methods based on PLS regression including Competitive Adaptive Reweighted Sampling (CARS)-PLS, Uninformative Variable Elimination (UVE)-PLS, Interval Partial Least Squares (IPLS) and Moving Windows Partial Least Squares (MWPLS) were also compared. The results also show good performance of FD-VS-LSSVR in terms of prediction accuracy or robustness. Therefore, the FD-VS method provides an effective and credible variable selection way for IR spectrum to predict quality parameters of wine. |
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| Keywords: | Infrared spectra Wine Quantitative analysis Chemometrics Multivariate calibration model Variable selection |
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