傅里叶变换近红外漫反射光谱法结合多元分析技术判别麻黄植物的方法学研究
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摘要
麻黄植物(Ephedra plants)广泛分布于世界很多地区,我国拥有丰富的麻黄植物资源,是麻黄植物及其制品的主要生产国。麻黄是一种古老的植物,对植物学研究极具意义。麻黄植物还是医药工业及其他工业不可替代的原料来源,因为其次生代谢产物包括生物碱、挥发油、鞣质、黄酮、多糖和有机酸等。在我国,麻黄具有悠久的药用历史,是重要的药用植物。此外,麻黄植物是沙地生态系统的重要组成部分,对维护和改善沙地生态系统具有重要意义。研究麻黄植物次生代谢产物的影响因素,建立准确、易用的麻黄植物判别方法,不仅有助于麻黄植物生态学的研究和麻黄资源的科学利用,还能为其他植物的研究与利用提供参考。
本论文采用傅里叶变换近红外漫反射光谱法(diffuse reflectance Fourier transform near infrared spectroscopy, diffuse reflectance FT-NIRS),结合多元分析技术(multivariate analysis),较为系统地研究了受遗传因素(hereditary factors)、生态因子(ecological factors)及代谢节律(metabolic rhythms)影响的不同种类(species)、不同产地(habitats)和不同采摘时间(picking times)的麻黄植物理化性质的差异,建立了麻黄植物的近红外漫反射光谱判别方法(discrimination methods)。
主要研究内容和结果:
1.不同种类麻黄植物的漫反射FT-NIRS判别方法学研究
采用傅里叶变换近红外漫反射光谱法,结合多元分析技术,对草麻黄、中麻黄和木贼麻黄进行的判别研究显示,受遗传因素影响的不同种类麻黄植物,其理化性质存在显著性差异;漫反射FT-NIRS分别结合判别分析(discriminant analysis, DA)、自组织映射神经网络(self-organizing map, SOM)和反向传播人工神经网络(back-propagation artificial neural network, BP-ANN)均能有效地判别不同种类的麻黄植物。
①三种麻黄植物近红外漫反射光谱(near infrared diffuse reflectance spectra, NIRDRS)的测量方法:取经形态学鉴别且水分低于8.0%的草麻黄、中麻黄和木贼麻黄的草质茎,经粉碎、过筛,在10 000~4000 cm-1的范围内,以4 cm-1的分辨率,扫描64次,获得NIRDRS。
②三种麻黄植物NIRDRS的预处理方法:首先进行平滑处理,当用DA法建模时采用五点Norris平滑,当用ANN法(包括SOM法和BP-ANN法)建模时采用七点Savitzky-Golay平滑;其次,进行一阶导数基线校正;最后,进行多元散射校正。
③三种麻黄植物NIRDRS的波长范围选择:采用全光谱(full spectrum)能更好地表征不同种类麻黄植物的特性差异,获得更好的模式识别结果。
④三种麻黄植物NIRDRS的数据预处理方法:采用主成分分析技术(principal component analysis, PCA)对数据进行降维处理。当用DA法建模时,光谱数据的维数被降为9 PCs,累计贡献率为98.7%;当用ANN法(包括SOM法和BP-ANN法)建模时,光谱数据的维数被降为10 PCs,累计贡献率为99.9%。
⑤三种麻黄植物NIRDRS的数据分析方法:分别运用DA、SOM和BP-ANN三种多元分析技术研究草麻黄、中麻黄和木贼麻黄的近红外漫反射光谱与麻黄种类之间的关系,建立并验证了不同种类麻黄植物的判别模型。其中,DA判别模型正确区分三种麻黄植物的性能指标为84.2%;SOM法和BP-ANN法所建判别模型对三种麻黄植物的预测准确性均为95.0%。DA判别模型和SOM判别模型具有良好的可视化功能(visualization functions)。
2.不同产地麻黄植物的漫反射FT-NIRS判别方法学研究
采用傅里叶变换近红外漫反射光谱法,结合多元分析技术,对山西草麻黄与内蒙古草麻黄进行的判别研究显示,受生态因子影响的不同产地麻黄植物,其理化性质存在显著性差异;漫反射FT-NIRS分别结合DA法、SOM法和BP-ANN法均能有效地判别不同产地的麻黄植物。
①两个产地草麻黄NIRDRS的测量方法:取经形态学鉴别且水分低于8.0%的山西草麻黄与内蒙古草麻黄的草质茎,经粉碎、过筛,在10 000~4000 cm-1的范围内,以4 cm-1的分辨率,扫描64次,获得NIRDRS。
②两个产地草麻黄NIRDRS的预处理方法:当用DA法、SOM法和BP-ANN法建模时,均采用相同的综合方法进行光谱的预处理。首先,进行五点Savitzky-Golay平滑;然后,进行多元散射校正。
③两个产地草麻黄NIRDRS的波长范围选择:采用全光谱能更好地表征不同产地麻黄植物的特性差异,获得更好的模式识别结果。
④两个产地草麻黄NIRDRS的数据预处理方法:采用PCA技术对数据进行降维处理。对于DA、SOM和BP-ANN三种建模方法,光谱数据的维数均被降为10 PCs,累计贡献率为99.8%。
⑤两个产地草麻黄NIRDRS的数据分析方法:分别采用DA法、SOM法和BP-ANN法研究山西草麻黄和内蒙古草麻黄的近红外漫反射光谱与草麻黄产地之间的关系,建立并验证了不同产地草麻黄的判别模型。其中,DA法所建判别模型正确区分两个产地草麻黄的性能指标为91.9%;SOM法和BP-ANN法所建判别模型对两个产地草麻黄的预测准确性均为100.0%。DA判别模型和SOM判别模型具有良好的可视化功能。
3.不同采摘时间麻黄植物的漫反射FT-NIRS判别方法学研究
采用傅里叶变换近红外漫反射光谱法,结合多元分析技术,对同一天上午10:00~11:30与下午4:30~5:00采摘的山西草麻黄进行的判别研究显示,受代谢节律影响的不同采摘时间麻黄植物,其理化性质存在显著性差异。可以认为,山西草麻黄的次生代谢受到一天内代谢节律的调控,受调控的组分可能具有挥发性。该现象的发现,为麻黄植物挥发油的利用及其他植物挥发油的研究提供了一种新的思路。
漫反射FT-NIRS分别结合DA法、SOM法和BP-ANN法均能有效地判别不同采摘时间的麻黄植物。
①两个采摘时间山西草麻黄NIRDRS的测量方法:取经形态学鉴别且水分低于8.0%的、同一天上午10:00~11:30与下午4:30~5:00采摘的山西草麻黄的草质茎,经粉碎、过筛,在10 000~4000 cm-1的范围内,以4 cm-1的分辨率,扫描64次,获得NIRDRS。
②两个采摘时间山西草麻黄NIRDRS的预处理方法:当用DA法、SOM法和BP-ANN法建模时,均采用相同的综合方法进行光谱的预处理。首先,进行五点Savitzky-Golay平滑;然后,进行多元散射校正。
③两个采摘时间山西草麻黄NIRDRS的波长范围选择:采用全光谱能更好地表征不同采摘时间麻黄植物的特性差异,获得更好的模式识别结果。
④两个采摘时间山西草麻黄NIRDRS的数据预处理方法:采用PCA技术对数据进行降维处理。对于DA、SOM和BP-ANN三种建模方法,光谱数据的维数均被降为10 PCs,累计贡献率为99.7%。
⑤两个采摘时间山西草麻黄NIRDRS的数据分析方法:分别采用DA法、SOM法和BP-ANN法研究不同采摘时间山西草麻黄的近红外漫反射光谱与山西草麻黄采摘时间之间的关系,建立并验证了不同采摘时间山西草麻黄的判别模型。其中,DA法所建判别模型正确区分两个采摘时间山西草麻黄的性能指标为88.1%;SOM法和BP-ANN法所建判别模型对两个采摘时间山西草麻黄的预测准确性均为93.3%。DA判别模型和SOM判别模型具有良好的可视化功能。
主要结论:
1.采用漫反射FT-NIRS结合多元分析技术,对不同种类、不同产地和不同采摘时间麻黄植物理化性质的差异进行了研究。实验证明,受遗传因素的影响,不同种类麻黄植物的理化性质存在明显的差异;受生态因子的影响,不同产地麻黄植物的理化性质存在明显的差异,与发表的其他方法的研究结果相吻合。实验发现,受代谢节律的影响,同一天上午10:00~11:30与下午4:30~5:00采摘的山西草麻黄,它们的理化性质存在明显的差异,可以认为山西草麻黄的挥发性次生代谢产物受到一天内代谢节律的调控。该现象的发现,将促进麻黄植物生态学的研究及麻黄资源的利用,并将为其他植物的研究与利用(尤其是挥发油的研究与利用)提供一种新的思路。
2.建立了漫反射FT-NIRS结合多元分析技术判别麻黄植物的方法。尽管随遗传因素、生态因子及代谢节律对麻黄植物次生代谢的影响强度依次减弱,不同种类、不同产地和不同采摘时间麻黄植物的差异按顺序减小,实验证明,漫反射FT-NIRS分别结合DA、SOM、BP-ANN,能够以基本稳定的优良预测性能对它们进行判别,而且DA判别模型和SOM判别模型还具有良好的可视化功能。所建漫反射FT-NIRS判别方法,判断客观、准确,容易使用,分析速度快,不必进行特殊的样品处理,也不需要使用化学试剂。该方法的建立,为麻黄植物的研究及麻黄资源的利用提供了一种可靠、便利的优良工具,为准确评价麻黄植物的品质、规范麻黄中药材市场提供了技术支持,为其他植物的判别研究提供了参考。
Ephedra plants (Ephedraceae) are widely distributed in the world. Among them, Ephedra sinica (E. sinica), Ephedra intermedia (E. intermedia) and Ephedra equisetina (E. equisetina) are dominant and economically important species in China and their main habitats are in Inner Mongolia, Xinjiang, Shanxi, Gansu and so on. The secondary metabolites contained in Ephedra plants mainly include alkaloids, essential oils, tannins, flavonoids and organic acids. They are very important for the plants’survivals. On the other hand, plant secondary metabolites are unique sources for pharmaceuticals, food additives, flavors, and other industrial materials. Hence, the identification of Ephedra plants of different species, habitats and picking times could help the research of phytoecology of Ephedra and the utilization of Ephedra plants.
In this thesis, the feasibility of the discrimination of Ephedra plants of different species, habitats and picking times is evaluated with diffuse reflectance FT-NIRS. The optimization of the spectral measurement conditions, spectra processing approaches, data pre-processing methods, and data analysis techniques is also discussed.
The main research contents and results are as follows.
1. Discrimination of Ephedra plants of different species with diffuse reflectance FT-NIRS: The samples of E. sinica, E. intermedia and E. equisetina were discriminated with reference to morphological characters. The herbaceous stems (moisture contents < 8.0%) were pulverized and sieved. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from pulverized samples put in glass vials in the near infrared (NIR) region between 10 000 and 4000cm?1, averaging 64 scans per spectrum at a resolution of 4 cm?1. The NIRDRS were processed first with smoothing techniques. The five-point Norris smoothing was used in the discriminant analysis (DA). The seven-point Savitzky–Golay smoothing was used in the self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The methods for subsequent processing were first derivative and multiplicative signal correction (MSC). The dimensions of the spectral data were reduced to 9 principle components (PCs) for the DA and to 10 PCs for the artificial neural network (SOM and BP-ANN). The cumulative contribution rates of 9 and 10 PCs were 98.7% and 99.9% in sequence. Ephedra plants of different species could be discriminated respectively with the DA, SOM and BP-ANN. The performance index of the DA model was 84.2%. The prediction accuracies of both the SOM and the BP-ANN models were 95.0%. The visualization functions were achieved with the DA and the SOM.
2. Discrimination of E. sinica from different habitats with diffuse reflectance FT-NIRS: The NIRDRS of the samples of E. sinica from Shanxi and Inner Mongolia were measured using the method used in Section 1. The NIRDRS were processed by the five-point Savitzky–Golay smoothing technique and MSC. The dimensions of the spectral data were reduced to 10 PCs and the cumulative contribution rate was 99.8% for the DA, SOM and BP-ANN. E. sinica from different habitats could be identified respectively with the three data analysis methods. The performance index of the DA model was 91.9%. The prediction accuracies of both the SOM and the BP-ANN models were 100.0%. The visualization functions were achieved with the DA and the SOM.
3. Discrimination of E. sinica from Shanxi picked at different times of day with diffuse reflectance FT-NIRS: The NIRDRS of the samples of E. sinica from Shanxi picked between 10:00 and 11:30 am and between 4:30 and 5:00 pm were measured using the method used in Section 1. The techniques of spectra processing and data pre-processing for this task were the same as those used in Section 2. Nevertheless, the cumulative contribution rate of 10 PCs was 99.7% for the three data analysis methods. E. sinica from Shanxi picked at different times of day could be distinguished respectively with the DA, SOM and BP-ANN. The performance index of the DA model was 88.1%. The prediction accuracies of both the SOM and the BP-ANN models were 93.3%. The visualization functions were achieved with the DA and the SOM.
The main conclusions are as follows.
1. The results obtained in this task suggest that there is significant difference between E. sinica picked in the morning and afternoon. This fact implies that the secondary metabolites of E. sinica are modulated by time of day.
2. The experiment has proved that diffuse reflectance FT-NIRS with multivariate analysis techniques could distinguish not only the Ephedra plants of different species and different habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The approach established is objective, easy-to-use, rapid and pollution-free. It is a useful tool for the research of phytoecology of Ephedra and the utilization of Ephedra plants.
本论文采用傅里叶变换近红外漫反射光谱法(diffuse reflectance Fourier transform near infrared spectroscopy, diffuse reflectance FT-NIRS),结合多元分析技术(multivariate analysis),较为系统地研究了受遗传因素(hereditary factors)、生态因子(ecological factors)及代谢节律(metabolic rhythms)影响的不同种类(species)、不同产地(habitats)和不同采摘时间(picking times)的麻黄植物理化性质的差异,建立了麻黄植物的近红外漫反射光谱判别方法(discrimination methods)。
主要研究内容和结果:
1.不同种类麻黄植物的漫反射FT-NIRS判别方法学研究
采用傅里叶变换近红外漫反射光谱法,结合多元分析技术,对草麻黄、中麻黄和木贼麻黄进行的判别研究显示,受遗传因素影响的不同种类麻黄植物,其理化性质存在显著性差异;漫反射FT-NIRS分别结合判别分析(discriminant analysis, DA)、自组织映射神经网络(self-organizing map, SOM)和反向传播人工神经网络(back-propagation artificial neural network, BP-ANN)均能有效地判别不同种类的麻黄植物。
①三种麻黄植物近红外漫反射光谱(near infrared diffuse reflectance spectra, NIRDRS)的测量方法:取经形态学鉴别且水分低于8.0%的草麻黄、中麻黄和木贼麻黄的草质茎,经粉碎、过筛,在10 000~4000 cm-1的范围内,以4 cm-1的分辨率,扫描64次,获得NIRDRS。
②三种麻黄植物NIRDRS的预处理方法:首先进行平滑处理,当用DA法建模时采用五点Norris平滑,当用ANN法(包括SOM法和BP-ANN法)建模时采用七点Savitzky-Golay平滑;其次,进行一阶导数基线校正;最后,进行多元散射校正。
③三种麻黄植物NIRDRS的波长范围选择:采用全光谱(full spectrum)能更好地表征不同种类麻黄植物的特性差异,获得更好的模式识别结果。
④三种麻黄植物NIRDRS的数据预处理方法:采用主成分分析技术(principal component analysis, PCA)对数据进行降维处理。当用DA法建模时,光谱数据的维数被降为9 PCs,累计贡献率为98.7%;当用ANN法(包括SOM法和BP-ANN法)建模时,光谱数据的维数被降为10 PCs,累计贡献率为99.9%。
⑤三种麻黄植物NIRDRS的数据分析方法:分别运用DA、SOM和BP-ANN三种多元分析技术研究草麻黄、中麻黄和木贼麻黄的近红外漫反射光谱与麻黄种类之间的关系,建立并验证了不同种类麻黄植物的判别模型。其中,DA判别模型正确区分三种麻黄植物的性能指标为84.2%;SOM法和BP-ANN法所建判别模型对三种麻黄植物的预测准确性均为95.0%。DA判别模型和SOM判别模型具有良好的可视化功能(visualization functions)。
2.不同产地麻黄植物的漫反射FT-NIRS判别方法学研究
采用傅里叶变换近红外漫反射光谱法,结合多元分析技术,对山西草麻黄与内蒙古草麻黄进行的判别研究显示,受生态因子影响的不同产地麻黄植物,其理化性质存在显著性差异;漫反射FT-NIRS分别结合DA法、SOM法和BP-ANN法均能有效地判别不同产地的麻黄植物。
①两个产地草麻黄NIRDRS的测量方法:取经形态学鉴别且水分低于8.0%的山西草麻黄与内蒙古草麻黄的草质茎,经粉碎、过筛,在10 000~4000 cm-1的范围内,以4 cm-1的分辨率,扫描64次,获得NIRDRS。
②两个产地草麻黄NIRDRS的预处理方法:当用DA法、SOM法和BP-ANN法建模时,均采用相同的综合方法进行光谱的预处理。首先,进行五点Savitzky-Golay平滑;然后,进行多元散射校正。
③两个产地草麻黄NIRDRS的波长范围选择:采用全光谱能更好地表征不同产地麻黄植物的特性差异,获得更好的模式识别结果。
④两个产地草麻黄NIRDRS的数据预处理方法:采用PCA技术对数据进行降维处理。对于DA、SOM和BP-ANN三种建模方法,光谱数据的维数均被降为10 PCs,累计贡献率为99.8%。
⑤两个产地草麻黄NIRDRS的数据分析方法:分别采用DA法、SOM法和BP-ANN法研究山西草麻黄和内蒙古草麻黄的近红外漫反射光谱与草麻黄产地之间的关系,建立并验证了不同产地草麻黄的判别模型。其中,DA法所建判别模型正确区分两个产地草麻黄的性能指标为91.9%;SOM法和BP-ANN法所建判别模型对两个产地草麻黄的预测准确性均为100.0%。DA判别模型和SOM判别模型具有良好的可视化功能。
3.不同采摘时间麻黄植物的漫反射FT-NIRS判别方法学研究
采用傅里叶变换近红外漫反射光谱法,结合多元分析技术,对同一天上午10:00~11:30与下午4:30~5:00采摘的山西草麻黄进行的判别研究显示,受代谢节律影响的不同采摘时间麻黄植物,其理化性质存在显著性差异。可以认为,山西草麻黄的次生代谢受到一天内代谢节律的调控,受调控的组分可能具有挥发性。该现象的发现,为麻黄植物挥发油的利用及其他植物挥发油的研究提供了一种新的思路。
漫反射FT-NIRS分别结合DA法、SOM法和BP-ANN法均能有效地判别不同采摘时间的麻黄植物。
①两个采摘时间山西草麻黄NIRDRS的测量方法:取经形态学鉴别且水分低于8.0%的、同一天上午10:00~11:30与下午4:30~5:00采摘的山西草麻黄的草质茎,经粉碎、过筛,在10 000~4000 cm-1的范围内,以4 cm-1的分辨率,扫描64次,获得NIRDRS。
②两个采摘时间山西草麻黄NIRDRS的预处理方法:当用DA法、SOM法和BP-ANN法建模时,均采用相同的综合方法进行光谱的预处理。首先,进行五点Savitzky-Golay平滑;然后,进行多元散射校正。
③两个采摘时间山西草麻黄NIRDRS的波长范围选择:采用全光谱能更好地表征不同采摘时间麻黄植物的特性差异,获得更好的模式识别结果。
④两个采摘时间山西草麻黄NIRDRS的数据预处理方法:采用PCA技术对数据进行降维处理。对于DA、SOM和BP-ANN三种建模方法,光谱数据的维数均被降为10 PCs,累计贡献率为99.7%。
⑤两个采摘时间山西草麻黄NIRDRS的数据分析方法:分别采用DA法、SOM法和BP-ANN法研究不同采摘时间山西草麻黄的近红外漫反射光谱与山西草麻黄采摘时间之间的关系,建立并验证了不同采摘时间山西草麻黄的判别模型。其中,DA法所建判别模型正确区分两个采摘时间山西草麻黄的性能指标为88.1%;SOM法和BP-ANN法所建判别模型对两个采摘时间山西草麻黄的预测准确性均为93.3%。DA判别模型和SOM判别模型具有良好的可视化功能。
主要结论:
1.采用漫反射FT-NIRS结合多元分析技术,对不同种类、不同产地和不同采摘时间麻黄植物理化性质的差异进行了研究。实验证明,受遗传因素的影响,不同种类麻黄植物的理化性质存在明显的差异;受生态因子的影响,不同产地麻黄植物的理化性质存在明显的差异,与发表的其他方法的研究结果相吻合。实验发现,受代谢节律的影响,同一天上午10:00~11:30与下午4:30~5:00采摘的山西草麻黄,它们的理化性质存在明显的差异,可以认为山西草麻黄的挥发性次生代谢产物受到一天内代谢节律的调控。该现象的发现,将促进麻黄植物生态学的研究及麻黄资源的利用,并将为其他植物的研究与利用(尤其是挥发油的研究与利用)提供一种新的思路。
2.建立了漫反射FT-NIRS结合多元分析技术判别麻黄植物的方法。尽管随遗传因素、生态因子及代谢节律对麻黄植物次生代谢的影响强度依次减弱,不同种类、不同产地和不同采摘时间麻黄植物的差异按顺序减小,实验证明,漫反射FT-NIRS分别结合DA、SOM、BP-ANN,能够以基本稳定的优良预测性能对它们进行判别,而且DA判别模型和SOM判别模型还具有良好的可视化功能。所建漫反射FT-NIRS判别方法,判断客观、准确,容易使用,分析速度快,不必进行特殊的样品处理,也不需要使用化学试剂。该方法的建立,为麻黄植物的研究及麻黄资源的利用提供了一种可靠、便利的优良工具,为准确评价麻黄植物的品质、规范麻黄中药材市场提供了技术支持,为其他植物的判别研究提供了参考。
Ephedra plants (Ephedraceae) are widely distributed in the world. Among them, Ephedra sinica (E. sinica), Ephedra intermedia (E. intermedia) and Ephedra equisetina (E. equisetina) are dominant and economically important species in China and their main habitats are in Inner Mongolia, Xinjiang, Shanxi, Gansu and so on. The secondary metabolites contained in Ephedra plants mainly include alkaloids, essential oils, tannins, flavonoids and organic acids. They are very important for the plants’survivals. On the other hand, plant secondary metabolites are unique sources for pharmaceuticals, food additives, flavors, and other industrial materials. Hence, the identification of Ephedra plants of different species, habitats and picking times could help the research of phytoecology of Ephedra and the utilization of Ephedra plants.
In this thesis, the feasibility of the discrimination of Ephedra plants of different species, habitats and picking times is evaluated with diffuse reflectance FT-NIRS. The optimization of the spectral measurement conditions, spectra processing approaches, data pre-processing methods, and data analysis techniques is also discussed.
The main research contents and results are as follows.
1. Discrimination of Ephedra plants of different species with diffuse reflectance FT-NIRS: The samples of E. sinica, E. intermedia and E. equisetina were discriminated with reference to morphological characters. The herbaceous stems (moisture contents < 8.0%) were pulverized and sieved. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from pulverized samples put in glass vials in the near infrared (NIR) region between 10 000 and 4000cm?1, averaging 64 scans per spectrum at a resolution of 4 cm?1. The NIRDRS were processed first with smoothing techniques. The five-point Norris smoothing was used in the discriminant analysis (DA). The seven-point Savitzky–Golay smoothing was used in the self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The methods for subsequent processing were first derivative and multiplicative signal correction (MSC). The dimensions of the spectral data were reduced to 9 principle components (PCs) for the DA and to 10 PCs for the artificial neural network (SOM and BP-ANN). The cumulative contribution rates of 9 and 10 PCs were 98.7% and 99.9% in sequence. Ephedra plants of different species could be discriminated respectively with the DA, SOM and BP-ANN. The performance index of the DA model was 84.2%. The prediction accuracies of both the SOM and the BP-ANN models were 95.0%. The visualization functions were achieved with the DA and the SOM.
2. Discrimination of E. sinica from different habitats with diffuse reflectance FT-NIRS: The NIRDRS of the samples of E. sinica from Shanxi and Inner Mongolia were measured using the method used in Section 1. The NIRDRS were processed by the five-point Savitzky–Golay smoothing technique and MSC. The dimensions of the spectral data were reduced to 10 PCs and the cumulative contribution rate was 99.8% for the DA, SOM and BP-ANN. E. sinica from different habitats could be identified respectively with the three data analysis methods. The performance index of the DA model was 91.9%. The prediction accuracies of both the SOM and the BP-ANN models were 100.0%. The visualization functions were achieved with the DA and the SOM.
3. Discrimination of E. sinica from Shanxi picked at different times of day with diffuse reflectance FT-NIRS: The NIRDRS of the samples of E. sinica from Shanxi picked between 10:00 and 11:30 am and between 4:30 and 5:00 pm were measured using the method used in Section 1. The techniques of spectra processing and data pre-processing for this task were the same as those used in Section 2. Nevertheless, the cumulative contribution rate of 10 PCs was 99.7% for the three data analysis methods. E. sinica from Shanxi picked at different times of day could be distinguished respectively with the DA, SOM and BP-ANN. The performance index of the DA model was 88.1%. The prediction accuracies of both the SOM and the BP-ANN models were 93.3%. The visualization functions were achieved with the DA and the SOM.
The main conclusions are as follows.
1. The results obtained in this task suggest that there is significant difference between E. sinica picked in the morning and afternoon. This fact implies that the secondary metabolites of E. sinica are modulated by time of day.
2. The experiment has proved that diffuse reflectance FT-NIRS with multivariate analysis techniques could distinguish not only the Ephedra plants of different species and different habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The approach established is objective, easy-to-use, rapid and pollution-free. It is a useful tool for the research of phytoecology of Ephedra and the utilization of Ephedra plants.
引文
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