食品工业科技2022，Vol.43Issue(4)：323-331,9.DOI:10.13386/j.issn1002-0306.2021060159
近红外结合线性回归算法快速预测小麦籽粒干物质和重量
NIR Combined with Linear Regression Algorithm for Rapid Prediction of Dry Matter and Weight in Wheat Grain
摘要
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.关键词
近红外/小麦/偏最小二乘/干物质/重量Key words
near-infrared (NIR)/wheat/partial least square (PLS)/dry matter/weight分类
轻工纺织引用本文复制引用
陈岩,何鸿举,欧阳娟,欧行奇,郭景丽,王玉玲,乔红,李新华..近红外结合线性回归算法快速预测小麦籽粒干物质和重量[J].食品工业科技,2022,43(4):323-331,9.基金项目
河南省重大科技专项(191110110700,151100110700) （191110110700,151100110700）
新乡市重大科技专项(ZD18007) （ZD18007）
河南科技学院横向科研——小麦、甘薯专用肥联合研发项目(2021410707000060) （2021410707000060）
河南科技学院高层次人才引进项目(2015015,2015003). （2015015,2015003）
