A Simultaneous Quantification Method of Thalassemia Screening Multiple Indicators Using FTIR/ATR Spectroscopy

Pan

2010 Symposium on Photonics and Optoelectronics

et al. 2010

Self Cite

The rapid and simple analytical method for soil organic matter by near infrared spectroscopy (NIRS) is very significant in precision agriculture. In this paper, the rapid determination method and the analysis model of soil organic matter were established by using the NIRS technology, partial least squares (PLS) regression and Savitzky-Golay (SG) smoothing method. Based on the prediction effect of the optimal single wavelength 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 1-30 PLS factor were established respectively. The optimal smoothing parameters were the second order derivative smoothing, 2 or 3 degree polynomial, 61 smoothing points, the optimal PLS factor, root mean squared error of predication (RMSEP) and correlation coefficient of predication (R P ) were 19, 0.197 (%) and 0.925 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 NIRS analysis.

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization of SG Smoothing Parameters and PLS Factor Was Applied to NIRS Analysis of Soil Organic Matter

Pan

2010 Symposium on Photonics and Optoelectronics

et al. 2010

Self Cite

“…Modeling optimization of spectral analysis is further necessary work. Specially, wavenumbers combination selection of NIRS analysis was very important for improving model prediction effect, reducing model complexity and designing special NIRS instruments with high signal noise ratio [7][8][9][10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Wavenumbers Combination Optimization for NIRS Analysis of Glucose in Human Serum

Xie

Pan

2010 Symposium on Photonics and Optoelectronics

et al. 2010

Self Cite

The wavenumbers combination selection of near infrared spectroscopy (NIRS) analysis was very important for improving model prediction effect, reducing model complexity and designing special NIRS instruments with high signal noise ratio. Based on the prediction effect of single wavenumber linear regression model, a wavenumbers combination selection method of NIRS analysis of glucose in human serum was developed. 25 wavenumbers with good prediction effect were selected. All wavenumber combinations of the twenty-five wavenumbers were used to establish multiple linear regression (MLR) models respectively. According to the prediction effect, the optimal MLR model was the eleven wavenumbers combination of 7340, 7328, 7311, 7253, 7251, 7234, 7228, 7220, 7218, 7207, 7203 (cm -1 ), the corresponding root mean squared error of predication (RMSEP) was 0.384 mmol/L. And the prediction effect was obvious better than one of partial least squares (PLS) model. These indicated that the wavenumbers combination selection method based on the prediction effect of single wavenumber linear regression model could be applied to the NIRS analysis and could provide valuable reference for designing minitype special NIRS instruments.

“…The reagent free, rapid and simple analytical method for soil organic matter by NIRS analysis is very significant. Wavelength combination selection of NIRS analysis is very important for improving model prediction effect, reducing model complexity and designing special NIR spectroscopy instruments with high signal to noise ratio [7][8][9][10][11][12][13][14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Wavelengths combination optimization for NIRS analysis of soil organic matter

Pan²,

Chen³

et al. 2010

SPIE Proceedings

Self Cite

Wavelengths combination optimization in near infrared spectroscopy (NIRS) analysis was very important for improving model prediction effect, simplifying high dimension problems, reducing model complexity and designing special NIRS instruments with high signal noise ratio. Based on the prediction effect of single wavelength linear regression model, a special wavelength set with 25 information data points was filtered out. All wavelengths combinations of these 25 wavelengths were used to establish multiple linear regression (MLR) models respectively. With a prediction effect close to the PLS model based on whole spectral region, the simplest MLR model is the 7-wavelengths combination of 1105.5, 1108, 1895, 2150.5, 2278.5, 2284, 2286.5 (nm), RMSEP, R P , RRMSEP was 0.2505 (%), 0.8753, 15.73% respectively. This indicated that the wavelengths combination selection method based on the prediction effect of single wavelength linear regression model could be applied to the NIRS analysis and could provide valuable reference for designing minitype special NIRS instruments.