NIR Combined with Linear Regression Algorithm for Rapid Prediction of Dry Matter and Weight in Wheat Grain

Abstract

In order to realize simultaneous and rapid nondestructive detection of wheat quality (dry matter, weight), 35 wheat varieties samples were subjected to near-infrared (NIR) system scanning, and the spectral information were acquired and pretreated by three methods including Gaussian filtering smoothing (GFS), normalize (N) and baseline correction (BC), respectively. Partial least squares (PLS) algorithm was adopted to build a quantitative relationship between spectra and reference value of dry matter and weight, respectively. Two methods such as regression coefficients (RC) and successive projections algorithm (SPA) were applied to select optimal wavelengths from the full 900~1700 nm range for PLS model optimization. Based on the selected optimal wavelengths, PLS and multiple linear regression (MLR) prediction models were built respectively. The results indicated that the RC-RAW-PLS models based on 20 optimal wavelengths selected from RAW spectra by RC method had better performance in dry matter prediction, with rP of 0.93 and RMSEP of 0.03%. The SPA-RAW-MLR model built with 12 optimal wavelengths selected from RAW spectra by SPA method had better performance in weight prediction, with rP of 0.89 and RMSEP of 0.32 g. In conclusion, NIR spectroscopy combined with PLS and MLR algorithm could be used for rapid prediction of dry matter and weight in wheat grain.

Keywords

• near-infrared (nir)
• wheat
• partial least square (pls)
• dry matter
• weight