The proposed work is focused on the simultaneous quantification of 14 compounds in the medicinal plant Achillea millefolium based on Near-Infrared Spectroscopy (NIR). The regression model of single-compound models (SCMs) and multicompound model (MCM) were created by partial least-squares regression (PLSR). Also, these models were calibrated by gas chromatographic mass spectroscopy (GC-MS). The results showed that the averaged standard errors of prediction (SEP) for the SCMs and MCM were 0.49 and 0.62, respectively, and most of the 14 compounds were significantly correlated. 43 correlations were significant at the 0.01 level (47.25% of the total), and 11 correlations were significant at the 0.05 level (12.09% of the total). The first three principal components (PCs) of principal component analysis (PCA) can explain >78% of the total variance. According to the component matrix and the communality table, octadecanoic acid has the largest influence on PC 1 (extraction squared = 46.72%), whose extraction was 0.932. The communality of neophytadiene, Z,Z,Z-9,12,15-octadecatrienoic acid, and oleic acid was also found to be large, whose extractions were 0.955, 0.937, and 0.859, respectively. These results indicate that if one compound shows a linear relationship with the NIR absorbance signal (SCM) also, an MCM can be created due to the close interrelations of these compounds. In this context, the present work highlights a suitable sample preparation technique to perform NIR analysis of raw plant material to benefit from robust and precise calibrations. To sum up, this NIR spectroscopic approach offers a precise, rapid, and cost-effective high-throughput analytical technique to simultaneously and noninvasively perform quantitative analysis of raw plant materials.

中文翻译：

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GC-MS分析和近红外光谱结合多元技术同时测定A草中14种化合物的含量

拟议的工作侧重于药用植物Achillea millefolium中14种化合物的同时定量基于近红外光谱（NIR）。通过偏最小二乘回归（PLSR）创建了单化合物模型（SCM）和多化合物模型（MCM）的回归模型。此外，这些模型还通过气相色谱质谱法（GC-MS）进行了校准。结果表明，SCM和MCM的平均预测标准误差（SEP）分别为0.49和0.62，并且14种化合物中的大多数具有显着相关性。在0.01水平（占总数的47.25％）上有43个显着相关，在0.05水平（占总数的12.09％）上有11个显着相关。主成分分析（PCA）的前三个主成分（PC）可以解释> 78％的总方差。根据组成矩阵和社区表，十八酸对PC 1的影响最大（萃取平方= 46.72％），其萃取率为0.932。还发现新植二烯，Z，Z，Z-9、12、15-十八碳三烯酸和油酸的社区很大，提取率分别为0.955、0.937和0.859。这些结果表明，如果一种化合物也与NIR吸光度信号（SCM）呈线性关系，则由于这些化合物之间的紧密联系，可以产生MCM。在这种情况下，本工作着重介绍了一种合适的样品制备技术，可以对植物原料进行近红外光谱分析，从而受益于强大而精确的校准方法。总而言之，这种NIR光谱方法可提供精确，快速，