何鸿举1*
,王玉玲2
,乔 红3
,欧行奇2
,刘 红4
,王 慧1
,蒋圣启1
,王 魏1.NIR光谱法快速预测小麦籽粒干物质含量[J].海南师范大学学报自科版,2019,32(1):33-38 |
NIR光谱法快速预测小麦籽粒干物质含量 |
NIR Spectra Method for Rapid Prediction of Dry Matter Content inWheat Grain |
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DOI:10.12051/j.issn.1674-4942.2019.01.006 |
中文关键词: 光谱 检测 小麦 干物质 |
英文关键词: spectrum detection wheat dry matter |
基金项目:河南省重大科技专项(151100110700);新乡市重大科技专项(ZD18007);河南科技学院高层次人才引进项目
(2015015,2015003);河南科技学院重大科研培育项目(2015ZD02);河南科技学院标志性创新工程项目(2015BZ03) |
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中文摘要: |
通过采集百农201、百农207、百农307、百旱207、AK-58、冠麦1号、周麦18等7个不同
品种完整小麦籽粒的近红外光谱(900~1700 nm)信息,经高斯滤波平滑(Gaussian Filtering
Smoothing,GFS)、标准化校正(Normalization Correction)和卷积平滑(Savitzky -Golay Convolution
Smoothing,SGCS)三种预处理后,利用偏最小二乘回归(Partial Least Squares Regression,PLSR)算法
寻找光谱信息与小麦籽粒干物质含量之间的定量关系。结果显示,经GFS预处理的近红外光谱
(100 个波长)构建的全波段 PLSR 模型(PLSR)预测相关系数(RP
)为 0.952,预测误差(RMSEP)为
0.158%,RMSEC与RMSEP绝对值差(ΔE)为0.082,预测效果优于其他两种预处理光谱。从GFS光
谱中经 PLSR-β 法筛选获得 17 个最优波长,构建的优化模型(O-PLSR)RP 为 0.928,RMSEP 为
0.191%,ΔE 为0.049,其预测效果接近于PLSR模型。试验表明,利用900~1700 nm光谱可被潜在
用于快速无损预测小麦籽粒干物质含量。 |
英文摘要: |
Bainong 201, Bainong 207, Bainong 307, Baihan 207, AK-58, Guanmai 1 and Zhoumai 18 were used as materi⁃
als. Gaussian Filtering Smoothing (GFS), Normalization Correction (NC) and Savitzky-Golay convolution smoothing (SGCS)
were used to pretreat near-infrared spectral information (900~1700 nm) of whole wheat grain. Partial least squares regres⁃
sion was used to relate the spectral data to dry matter content of wheat grain. The results showed that the full-band PLSR re⁃
gression model (PLSR) constructed by GFS pretreatment (100 wavelengths) had better performance (RP = 0.952, RMSEP=0.158%, ΔE=0.082), than other two pretreated spectra.PLSR-β method was used to select 17 optimal wavelengths to opti⁃
mize the PLSR model (building O-PLSR model), resulting in the RP of 0.928, RMSEP of 0.191% and ΔE of 0.082, similar
to PLSR model. The whole experiment showed that ear-infrared spectra in the range of 900~1700 nm could be used to rap⁃
id and nondestructive prediction of dry matter content in wheat grains. |
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