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首页> 外文期刊>Journal of near infrared spectroscopy >Predicting terpene content in dried conifer shoots using near infrared spectroscopy
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Predicting terpene content in dried conifer shoots using near infrared spectroscopy

机译:使用近红外光谱预测干果芽中的萜烯含量

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摘要

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 (alpha-pinene, beta-pinene and myrcene) with five conifers species (Picea glauca,Picea rubens,Pinus resinosa,Pinus strobusandThuja 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 alpha-pinene content are more accurate (respective coefficient of determination: r(2) = 0.85 and 0.82). In contrast, calibrations for beta-pinene and myrcene had a low accuracy (respectively: r(2) = 0.62 and 0.08), potentially because of the low concentration of these terpenes in the species studied. The calibration model fits (i.e., r(2)) 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.
机译:Terpenes是多种植物属,尤其是芳香草药和针叶树的植物化学品。萜烯含量定量成本高,复杂,需要提取油含量和气相色谱分析。近红外线(NIR)光谱可以提供替代的定量方法,特别是如果可以使用干燥植物材料的光谱开发校准,这比油基光谱更容易和更快地获得。在这里,为总萜烯含量(单次和酪萜)和具有五种针叶树种类的特定萜烯含量(单萜,β-甲烯和牛氨基)开发了多层谱光谱校准(Picea glauca,Picea rubens,Pinus Restavosa,Pinus Strobusandthuja occidentalis )。用气相色谱法量化新鲜拍摄样品的萜烯含量。在冷冻干燥的样品上测量NIR光谱(n = 137)。使用样本的子集,产生总萜烯的修改部分最小二乘回归和三个单独的Terpenes内容作为NIR光谱的功能。内部交叉验证的标准误差(0.25和2.28之间的值)和对偏差比率的预测比率(2.20和2.38之间的RPD值)表明所有校准都具有相似的准确性。然而,独立验证表明,总萜烯和α-突烯含量的校准更准确(相应的测定系数:R(2)= 0.85和0.82)。相反,β-突烯和氨基的校准具有低精度(分别:R(2)= 0.62和0.08),可能是由于所研究的物种中这些萜烯的低浓度。校准模型配合(即,R(2))与先前公布的使用干芽样品的光谱相当,并证明了针叶树样品中Terpenes的这种方法的潜力。所使用的校准方法可用于其他几个域(例如幼苗繁殖计划,工业),因为萜烯的广泛分布尤其是食肉饼干。

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