林业工程学报2017，Vol.2Issue(6)：45-49,5.DOI:10.13360/j.issn.2096-1359.2017.06.008
油桐籽含油率近红外光谱检测模型的构建
Modeling on determination of oil content of Vernicia fordii seeds by near-infrared spectroscopy
摘要
Abstract
Near-infrared spectroscopy(NIR) and chemometrics methods were used for a rapid determination of oil content of Verniciafordii seeds.There were 107 samples,including 21 V.montana Wils and 86 V.fordii Hemsley,being collected from tung oil tree germplasm in Yongshun County of Hunan Province.The near-infrared spectra of samples were collected by using scattered reflection mode through a antaris Ⅱ near-infrared spectrophotometer in the range of 10 000-4 000 cm-1.The oil content was determined by Soxhlet extraction.The 107 samples were divided into a calibration set (80) and a validation set (27) by Kennard-Stone algorithm.A combination of first derivative coupled with mean centering was utilized as an optimized spectral pretreatment method.Eight key variables were selected by competitive adaptive reweighted sampling (CARS),and their wavenumber of correspondence were 4 019.3,4 023.1,4 088.7,4 196.7,4 917.8,5 762.2,5 766.0 and 5 847.0 cm-1.Wavelet transform (WT) was adapted to compressed spectral data.Partial least squares (PLS) and radial basis function neural networks (RBFNN) were used to develop calibration models.The PLS combined with eight variables was finally used as the optimal model.The correlation coefficient ?,root mean square error prediction (RMSEP) and relative standard deviation (RSD) of validation set were 0.927,2.08 and 3.99％,respectively.The results showed that the accuracy of oil content prediction was improved by using NIR model combining PLS with CARS method.The method was suitable for the rapid determination of oil content of V.fordii seeds.关键词
油桐籽/含油率/近红外光谱/化学计量学Key words
Vernicia fordii seeds/oil content/near-infrared spectroscopy/chemometric分类
农业科技引用本文复制引用
李水芳,李一帆,付红军,李姣娟..油桐籽含油率近红外光谱检测模型的构建[J].林业工程学报,2017,2(6):45-49,5.基金项目
湖南省教育厅重点项目(14A155). （14A155）
