Terpenes are phytochemicals found in multiple plant genera, especially aromatic herbs and conifers. Terpene content quantification is costly and complex, requiring the extraction of oil content and gas chromatography analyses. Near infrared (NIR) spectroscopy could provide an alternative quantitative method, especially if calibration can be developed with the spectra of dried plant material, which are easier and faster to acquire than oil-based spectra. Here, multispecies NIR spectroscopy calibrations were developed for total terpene content (mono- and sesquiterpenes) and for specific terpenes (α-pinene, β-pinene and myrcene) with five conifers species (Picea glauca, Picea rubens, Pinus resinosa, Pinus strobus and Thuja occidentalis). The terpene content of fresh shoot samples was quantified with gas chromatography. The NIR spectra were measured on freeze-dried samples (n = 137). Using a subset of the samples, modified partial least squares regressions of total terpene and the three individual terpenes content were generated as a functions of the NIR spectra. The standard errors of the internal cross-validations (values between 0.25 and 2.28) and the ratio of prediction to deviation ratios (RPD values between 2.20 and 2.38) indicate that all calibrations have similar accuracy. The independent validations, however, suggest that the calibrations for total terpene and α-pinene content are more accurate (respective coefficient of determination: r2 = 0.85 and 0.82). In contrast, calibrations for β-pinene and myrcene had a low accuracy (respectively: r2 = 0.62 and 0.08), potentially because of the low concentration of these terpenes in the species studied. The calibration model fits (i.e., r2) are comparable to previously published calibration using the spectra of dried shoot samples and demonstrate the potential of this method for terpenes in conifer samples. The calibration method used could be useful in several other domains (e.g. seedling breeding program, industrial), because of the wide distribution of terpenes and especially of pinenes.

中文翻译：

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使用近红外光谱预测干燥针叶树枝条中的萜烯含量

萜烯是在多种植物属中发现的植物化学物质，尤其是芳香草本植物和针叶树。萜烯含量定量既昂贵又复杂，需要提取油含量和进行气相色谱分析。近红外 (NIR) 光谱可以提供一种替代的定量方法，特别是如果可以使用干燥植物材料的光谱进行校准，这种光谱比油基光谱更容易、更快地获得。在这里，针对总萜烯含量（单萜和倍半萜烯）和特定萜烯（α-蒎烯、β-蒎烯和月桂烯）与五种针叶树种（Picea glauca、Picea rubens、Pinus resinosa、Pinus strobus 和崖柏）。用气相色谱法定量新鲜枝条样品的萜烯含量。NIR 光谱是在冻干样品上测量的 (n = 137)。使用样品的一个子集，生成总萜烯和三种单独萜烯含量的修正偏最小二乘回归作为 NIR 光谱的函数。内部交叉验证的标准误差（值介于 0.25 和 2.28 之间）和预测与偏差比（RPD 值介于 2.20 和 2.38 之间）表明所有校准具有相似的准确度。然而，独立验证表明总萜烯和 α-蒎烯含量的校准更准确（各自的决定系数：r2 = 0.85 和 0.82）。相比之下，β-蒎烯和月桂烯的校准精度较低（分别为：r2 = 0.62 和 0.08），这可能是因为所研究物种中这些萜烯的浓度较低。校准模型拟合（即 r2）与之前发布的使用干枝样品光谱的校准相当，并证明了这种方法对针叶树样品中萜烯的潜力。由于萜烯，尤其是松烯的广泛分布，所使用的校准方法可用于其他几个领域（例如育苗计划、工业）。