文献类型：期刊文献

英文题名：Prediction of cyanidin 3-rutinoside content in Michelia crassipes based on near-infrared spectroscopic techniques

作者：Xiao, Yuguang[1] Zhang, Xiaoshu[2] Liu, Jun[1] Li, He[3] Jiang, Jingmin[1] Li, Yanjie[1] Diao, Shu[1]

第一作者：Xiao, Yuguang

通信作者：Diao, S[1]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou, Peoples R China;[2]Xinxiang Univ, Sch Civil Engn & Architecture, Xinxiang, Peoples R China;[3]Res Inst Landscape Plants, Guizhou Acad Forestry, Guiyang, Peoples R China

年份：2024

卷号：15

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001224730000001)】；

基金：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research was supported by Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding" (2021C02071-3).

语种：英文

外文关键词：model calibration; NIR spectroscopy; regression algorithm; cyanidin 3-rutinoside; Michelia crassipes

摘要：Currently the determination of cyanidin 3-rutinoside content in plant petals usually requires chemical assays or high performance liquid chromatography (HPLC), which are time-consuming and laborious. In this study, we aimed to develop a low-cost, high-throughput method to predict cyanidin 3-rutinoside content, and developed a cyanidin 3-rutinoside prediction model using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). We collected spectral data from Michelia crassipes (Magnoliaceae) tepals and used five different preprocessing methods and four variable selection algorithms to calibrate the PLSR model to determine the best prediction model. The results showed that (1) the PLSR model built by combining the blockScale (BS) preprocessing method and the Significance multivariate correlation (sMC) algorithm performed the best; (2) The model has a reliable prediction ability, with a coefficient of determination (R-2) of 0.72, a root mean square error (RMSE) of 1.04%, and a residual prediction deviation (RPD) of 2.06. The model can be effectively used to predict the cyanidin 3-rutinoside content of the perianth slices of M. crassipes, providing an efficient method for the rapid determination of cyanidin 3-rutinoside content.