文献类型：期刊文献

英文题名：Prediction and Comparisons of Turpentine Content in Slash Pine at Different Slope Positions Using Near-Infrared Spectroscopy

作者：Luan, Qifu[1,2] Diao, Shu[1,2] Sun, Honggang[1,2] Ding, Xianyin[1,2] Jiang, Jingmin[1,2]

第一作者：Luan, Qifu;栾启福

通信作者：Jiang, JM[1];Jiang, JM[2]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou 311400, Peoples R China;[2]Natl Forestry & Grassland Engn Technol Res Ctr Ex, Hangzhou 311400, Peoples R China

年份：2022

卷号：11

期号：7

外文期刊名：PLANTS-BASEL

收录：;Scopus(收录号：2-s2.0-85127371542);WOS:【SCI-EXPANDED(收录号:WOS:000781985700001)】；

基金：This research was supported by Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding: 2021C02070-8-3; Fundamental Research Funds of Chinese Forestry Academy: CAFYBB2017ZA001-2-1.

语种：英文

外文关键词：model calibration; turpentine; NIR spectroscopy; slope position; slash pine

摘要：Pine resin is one of the best known and most exploited non-wood products. Resin is a complex mixture of terpenes produced by specialized cells that are dedicated to tree defense. Chemical defenses are plastic properties, and concentrations of chemical defenses can be adjusted based on environmental factors, such as resource availability. The slope orientation (south/sunny or north/shady) and the altitude of the plantation site have significant effects on the soil nutrient and the plant performance, whereas little is known about how the slope affects the pine resin yield and its components. In total, 1180 slash pines in 18 plots at different slope positions were established to determine the effects on the alpha- and beta-pinene content and resin production of the slash pine. The near-infrared spectroscopy (NIR) technique was developed to rapidly and economically predict the turpentine content for each sample. The results showed that the best partial least squares regression (PLS) models for alpha- and beta-pinene content prediction were established via the combined treatment of multiplicative scatter correction-significant multivariate correlation (MSC-sMC). The prediction models based on sMC spectra for alpha- and beta-pinene content have an R-2 of 0.82 and 0.85 and an RMSE of 0.96 and 0.82, respectively, and they were successfully implemented in turpentine prediction in this research. The results also showed that a barren slope position (especially mid-slope) could improve the alpha-pinene and beta-pinene content and resin productivity of slash pine, and the beta-pinene content in the resin had more variances in this research.