Prediction and Comparisons of Turpentine Content in Slash Pine at Different Slope Positions Using Near-Infrared Spectroscopy.

Abstract

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 α- and β-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 α- and β-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 α- and β-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 α-pinene and β-pinene content and resin productivity of slash pine, and the β-pinene content in the resin had more variances in this research.

Plant phenotyping relevance

NIR分光法を用いてスラッシュマツ樹脂中のα・β-ピネン含量を迅速予測する手法を開発し、PLSモデルの性能評価と実適用を行っている。植物の化学的形質を対象とした測定・推定法が研究の中心である。

Code and data availability

The paper reports NIR-based PLS prediction of α- and β-pinene content in slash pine resin, but provides no public phenotype/spectral dataset, images, code, or models. The Data Availability Statement says 'Not applicable.' Analysis used proprietary Unscrambler software and generic R; the R-project URL is a generic tool,

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