Wavelengths combination optimization for NIRS analysis of soil organic matter

Abstract

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.