The Spectroscopic and Electronic Properties of Dimethylpyrazole and Its Derivatives Using the Experimental and Computational Methods
In this paper the spectroscopic and the geometric properties of four ligands with pyrazole unit are studied at both experimental and computational levels. The computational results are perfectly in good agreement with the experimental results especially in terms of the IR, 1H-NMR and 13C-NMR shifts. The spectroscopic features as well as the computed properties help to establish the successful synthesis of ligands bdmpzm and bdmpza. The theoretical and the experimental IR and Raman significantly help in distinguishing the four ligands. The results show that the Raman spectral is better applicable in characterising the CH3 deformation, the C—H, CNN and CCNNout of the ligands but vibrations like N—H in dmpz and O—H, CO in bdmpza are observed to be Raman inactive. A significant variations are observed among the two available *N atoms characterising the bidentate features of bdmpzm, bdmpza and bdcpzm which indicates a possible different affinities for metal coordination. Also the result suggest that bdmpza will be the best starting material for NLO application than other while bdcpzm is predicted to have potential of been a poor coordinating ligand. The computed variations in the properties of *N atoms that are the characteristic features of their power of coordination can be of immense help since these type of ligands have a wide application in transition metal coordination.
利用近红外光谱对非均匀样品进行分析时，所得样品光谱中包含由光散射导致的干扰信息，通常需要借助多元散射校正算法(multiple scattering correction, MSC)对光谱进行预处理。由于不同波段光谱中所包含的散射信息、噪声水平、基线漂移程度等存在差异，利用MSC方法对光谱进行预处理时，基于不同波段的光谱数据会得到不同的校正结果，进而影响所得定标模型的可靠性。以60个全麦粉样品为研究对象，确定定标区间后，对包含定标区间的不同波段的原始光谱分别进行MSC处理，并利用固定区间内的光谱数据结合偏最小二乘回归(partial least square regression, PLSR)方法建立分析样品中蛋白质含量的定标模型，研究了MSC光谱预处理波段对定标模型的影响，并对MSC光谱预处理波段进行了优化，使定标模型的相关系数由0.96提高到0.98，交互验证均方根误差(root mean squares error of cross validation, RMSECV)由0.37%降低到0.32%。结果表明：利用MSC方法对样品光谱预处理时，光谱预处理波段会影响多元散射校正对光谱中非化学吸收信息的校正能力，确定合适的预处理波段是获得可靠分析结果的一个前提条件。
以四种品牌152组食用醋样品为研究对象，采用漫反射与透射两种近红外光谱采集模式分别进行光谱数据采集，并以此建立了食用醋品牌溯源模型，重点考察光谱采集模式、光谱预处理方法等对溯源模型精度的影响。结果表明，选取114组样品为训练集，原始光谱数据经过多元散射校正、二阶求导预处理后，采用偏最小二乘判别分析法(PLS1-DA)建立的食用醋NIRS品牌溯源模型，对38组测试集样品进行预测，透射光谱模型的决定系数(R2)、校准均方根误差(root-mean-square error of calibration, RMSEC)、预测均方根误差(root-mean-square error of prediction, RMSEP)分别为0.92，0.113，0.127，正确识别率为76.32%;漫反射光谱模型R2，RMSEC，RMSEP分别为0.97，0.102，0.119，正确识别率为86.84%。由此说明，近红外光谱结合PLS1-DA可以用来建立食用醋品牌溯源模型，且漫反射光谱模型预测效果更好。
基于敦煌辐射校正场，利用高、中两类定标场地，采用反射率基法对资源三号卫星多光谱传感器进行在轨场地绝对辐射定标，获取多光谱传感器的2013年绝对辐射定标系数，并与2012年定标结果进行对比分析。同时，利用2013年7月1日Landsat 8的operational land imager(OLI)影像对资源三号卫星多光谱传感器进行交叉定标，验证定标系数的可靠性。结果表明，一年来资源三号卫星多光谱传感器各波段性能存在1%～8.5%的变化;交叉定标和场地定标的结果有较好的一致性，说明定标结果具有较高的可信度。
Determination of Elements by Atomic Absorption Spectrometry in Medicinal Plants Employed to Alleviate Common Cold Symptoms
Eleven important medicinal plants generally used by the people of Turkey for the treatment of common cold have been studied for their mineral contents. Eleven minor and major elements (essential, non-essential and toxic) were identified in the Asplenium adiantum-nigrum L., Althaea officinalis L., Verbascum phlomoides L., Euphorbia chamaesyce L., Zizyphus jujube Miller, Peganum harmala L., Arum dioscoridis Sm., Sambucus nigra L., Piper longum L., Tussilago farfara L. and Elettaria cardamomum Maton by employing flame atomic absorption and emission spectrometry and electro-thermal atomic absorption spectrometry. Microwave digestion procedure for total concentration was applied under optimized conditions for dissolution of medicinal plants. Plant based biological certified reference materials (CRMs) served as standards for quantification. These elements are found to be present in varying concentrations in the studied plants. The baseline data presented in this work can be used in understanding the role of essential, non-essential and toxic elements in nutritive, preventive and therapeutic properties of medicinal plants.
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