文献类型：期刊文献

英文题名：Rapid detection of oil content in Camellia oleifera kernels based on hyperspectral imaging and machine learning

作者：Zhong, Huiqi[1,2] Chai, Jingyu[1,2] Yu, Chunlian[3] Wang, Kailiang[1,2] Wang, Kunxi[3] Lin, Ping[1,2]

第一作者：Zhong, Huiqi

通信作者：Lin, P[1]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, State Key Lab Tree Genet & Breeding, Hangzhou 311400, Zhejiang, Peoples R China;[2]Chinese Acad Forestry, Res Inst Subtrop Forestry, Zhejiang Key Lab Forest Genet & Breeding, Hangzhou 311400, Peoples R China;[3]Changshan Country Oil Tea Ind Dev Ctr, Quzhou Doctoral Innovat Workstn, Quzhou 323900, Peoples R China

年份：2025

卷号：137

外文期刊名：JOURNAL OF FOOD COMPOSITION AND ANALYSIS

收录：;Scopus(收录号：2-s2.0-85207345217);WOS:【SCI-EXPANDED(收录号:WOS:001349093700001)】；

基金：This research was supported by the National Key Research and Development Program of China (2023YFD2200702) and Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding (2021C02070-2) .

语种：英文

外文关键词：Camellia oleifera; Oil content; Hyperspectral imaging; Prediction; Partial least squares regression

摘要：The oil content (OC) of kernels is one of the primary targets in the breeding of Camellia oleifera. However, the OC determination is labor-consuming and time-costing using traditional methods. In this study, a rapid and efficient OC detecting method was developed based on hyperspectral imaging (HSI). The OCs of 220 C. oleifera clones were first determined using the Soxtec extraction method and hyperspectral images of all samples were obtained. Five spectral preprocessing methods and two dimensionality reduction methods was performed to eliminate hyperspectral noise. Based on the preprocessed spectral and OC data, OC predictive models were developed. The optimal OC prediction model was developed based on the characteristic wavelengths selected by competitive adaptive reweighted sampling from the preprocessed data by Savitzky-Golay smoothing and the first derivative method. The determination coefficient of this model was 0.9383, with a root mean squared error prediction of 1.7921 % and residual predictive deviation of 4.0271. The further validation of this model by the other samples demonstrated it's robustness and accuracy. The results reveal the potential of HSI in the rapid OC detection in C. oleifera. This will provide reference and guidance for the phenotype collection of C. oleifera.