Name
Abstract | ||
|---|---|---|
A rapid quantitative analysis method of glucose in aqueous solution was established by using the FTIR/ATR spectroscopy, partial least squares (PLS) regression and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavenumber model, calibration set and prediction set were divided. By extending the number of smoothing points and the degree of polynomial, 483 smooth modes were calculated. The PLS models corresponding to all combinations of 483 SG smoothing modes and 1-40 PLS factor were established respectively. The optimal smoothing parameters were the first order derivative smoothing, 5 or 6 degree polynomial, 63 smoothing points, the optimal PLS factor, root mean squared error of predication (RMSEP), correlation coefficient of predication (RP) and relative root mean squared error of predication (RRMSEP) were 3, 0.3729 (mmol/L), 0.9995 and 2.48% respectively, which was obviously superior to the direct PLS model without SG smoothing and the optimal SG smoothing model within 25 smoothing points (the original smoothing method). This demonstrates that the extending of SG smoothing modes and large-scale simultaneous optimization selection of SG smoothing parameters and PLS factor was all very necessary, and can be effectively applied to the model optimization of FTIR/ATR spectroscopy analysis. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/FSKD.2010.5569754 | FSKD |
Keywords | Field | DocType |
savitzky-golay smoothing method,relative root mean squared error,correlation coefficient,savitzky-golay smoothing,root mean squared error of predication,ftir/atr spectroscopy analysis,optimal single wavenumber model,sugar,aqueous solution,glucose solution,optical correlation,biological techniques,ftir/atr spectroscopy,least squares approximations,attenuated total reflection,partial least squares regression,calibration set,glucose,infrared spectroscopy,partial least squares,quantification method,prediction set,fourier transform spectroscopy,quantitative analysis,calibration,polynomials,predictive models,spectroscopy,chemicals,first order | Applied mathematics,Polynomial,Partial least squares regression,Mean squared error,Artificial intelligence,Correlation coefficient,Pattern recognition,Fourier transform spectroscopy,Degree of a polynomial,Smoothing,Statistics,Calibration,Mathematics | Conference |
Volume | ISBN | Citations |
5 | 978-1-4244-5931-5 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiemei Chen | 1 | 0 | 1.35 |
| Lingling Wu | 2 | 0 | 1.01 |
| Tao Pan | 3 | 0 | 1.35 |
| Jun Xie | 4 | 8 | 6.15 |
| Huazhou Chen | 5 | 13 | 3.53 |