摘要
利用近红外（NIR）高光谱（900～1700nm）成像技术对灵武长枣含水 量的无损检测进行了研究。通过900～1700nm 高 光谱成像系统采集了128个长枣图像,对原始光谱与Savitzky-Golay 平滑处理后的光谱反 射率R曲线、吸收率A曲线和Kubelka-Munk函数（KM ）等曲线的偏最小二乘回归（PLSR）模型进行对比分析；采 用PLSR的加权β系数分别提取不同光谱参数下的特征波长,建立R-PLSR、A-PLSR和KM-PLSR的长 枣 含水量预测模型。结果表明,采用原始光谱建立的PLSR模型优于Savitzky-Golay平滑的PLS R模 型；原始光谱的特征波长建立的PLSR模型优于全波段的PLSR模型,特征波长建立的KM-PLSR模型优于R- PLSR、A-PLSR模型,决定系数（R2）和预测均 方根误差（RMSEP）分别为0.793、1.828。这表明,NIR 高光谱成像技 术提取特征波长进行长枣水分检测是可行的,同时也为今后长枣品质在线检测提供了理论依据。
A near-infrared （NIR） hyperspectral imaging technique is investigated for non-destructive determination of moisture composition of jujubes produced in Li ngwu.The hyperspectral images of jujubes over the spectral region between 900nm and 1700nm a re acquired for 128jujube samples and the difference between raw wavelength and savitzky-golay smoothing wavelength is obtained by partial least-squares regression （PLSR）.The important wavelengths are selected using weighted β-co efficients of PLSR,and the R-PLSR,A-PLSR and KM-PLSR models are th en established using these feature wavelengths related with the spectral information by PLSR to predict moisture of jujubes.The results suggest that the raw wavelength is superior the to savitzky -golay smoothing wavelength.Compared with full wavelengths,the results show that the op timal wavelengths by R-PLSR,A-PLSR and KM-PLSR models have an excellent ability to predict the content of moisture in jujube and K M-PLSR model is superior to R-PLSR and A-PLSR models to predict moisture composition.Their coefficient of det ermination （R2） and root mean square error of prediction （RMSEP） are 0.793and 1.828,respectively.Hence,it′s possible to determine the moisture of jujube by hyperspectral imaging technique.The image of feature wavelengths abstracted f rom the hyperspectral image and the model using these optimal wavelengths can provide a good prediction of jujube′s moisture.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2014年第1期135-140,共6页
Journal of Optoelectronics·Laser
基金
国家科技支撑计划(2012BAF07B06)
国家自然科学基金(31060233)
2011年度宁夏回族自治区科技攻关计划项目
关键词
高光谱成像技术
长枣
含水量
无损检测
hyperspectral imagingl
jujube
moisture content
non-destructive determination
