红外、近红外光谱-簇类的独立软模式方法识别植物调和油脂

用IR,NIR光谱法结合簇类的独立软模式(SIMCA)识别方法对植物油脂进行分类识别,建立了识别二元、三元植物调和油脂的测定方法。应用NIRCal5.2软件的SIMCA技术,分别为所制备的植物调和油脂建立了IR和NIR识别模型,并讨论了光谱处理和数据处理方法来提高模型的分类识别效果。分别以各种植物调和油脂的IR和NIR光谱为变量,随机抽取2/3的样本作训练集,建立了各个调和油的主成分分析(Princi-pal component analysis,PCA)模型;1/3作验证集,对所建模型进行验证识别。用聚类分析-主成分分析(CLU-PCA)方法考察调和油的IR,NIR光谱信息与其纯油的主成分分布。结果显示,在4000～10000cm-1光谱范围内,SIMCA可以对15种二元调和油和2种三元调和油的NIR光谱分别聚类并识别;并对10种二元调和油和2种三元调和油的IR光谱分别聚类并识别。IR以4个波数1099,1119,1746与2855cm-1的吸收值作为分析基础,选择不同的主成分数及数据预处理方法。各种油脂的SIMCA分析的分类精度均为100%,调和油的验证识别准确率100%,最低识别比例为1%,且IR识别灵敏度高于NIR。     

近红外光谱结合特征变量筛选方法用于农药乳油中毒死蜱含量的测定

为提高毒死蜱农药乳油中有效成分近红外光谱定量分析模型的精度和稳定性。采用联合区间偏最小二乘法(siPLS)结合遗传算法(GA)筛选特征变量,由交互验证法确定最佳主成分因子数及筛选的变量数。结果表明,从全光谱区优选出81个变量,主成分因子数为11时,能建立性能最优的模型,模型预测集的决定系数R_p~2为0.972,预测均方根误差(RMSEP)为0.353%。研究表明,利用siPLS结合GA方法优选特征变量,能大幅度地消除农药乳油光谱变量间的冗余信息和无关信息,降低模型的复杂度,提高农药有效成分预测模型的精度及稳定性。     

丝氨酸热分解机理的研究

用热重法(TG)、差热分析(DTA)和红外光谱(IR)方法测试了丝氨酸的热分解过程,用量子化学方法在RHF/6-21G水平上全优化计算了丝氨酸及其热分解中间产物、产物分子的几何构型,得到其总能量和Mulliken集居数等数据.从理论上证明了环氧中间产物的存在.通过对实验结果和计算结果如Mulliken集居数的分析,提出了丝氨酸的热分解反应机理是先失去CO2为主要反应通道,同时伴有先失去NH3而生成环氧中间产物的副反应发生.     

基于高光谱和化学计量学方法的除草剂残留分类识别研究

本研究基于高光谱技术和化学计量学方法,对薄荷叶上的异丙甲草胺、烟嘧(莠去津、敌草胺和砜嘧(精喹4类除草剂残留进行种类判别.高光谱成像光谱范围为450nm～950nm的可见-近红外区域.为降低噪音对光谱数据的干扰、提升判别精度,采用SG平滑和多元散射校正对高光谱曲线进行处理.利用主成分分析算法(PCA)对原始数据进行降维...     

NEWS

蛋白质非特异性吸附(Nonspecific protein adsorption,NPA)是免疫分析中背景噪音的主要来源,也是制约免疫分析灵敏度和选择性的瓶颈。中科院苏州纳米所马宏伟等人(Anal.     

一种新型联萘基旋光共轭聚合物的圆二色谱和圆偏振荧光光谱

众所周知 ,聚合物的光电性质依赖于聚合物链的构象和 (或 )组成 ,通过在聚合物上引入手性单元 ,采用圆二色谱 ( CD)和圆偏振荧光光谱 ( CPL)等方法可表征聚合物结构 [1] .近年来 ,由于圆偏振光可用作光数据存储和液晶显示器背景光 [2 ] ,人们开始注重共轭聚合物圆偏振光材料的研究 .共轭聚合物的光致和电致圆偏振光的现象由一种带手性侧链的聚噻吩[3 ] 和一种带手性侧链的聚 (对苯撑乙烯 ) [4 ]产生 ,但它们的圆偏振荧光度 (用不对称因子 glum=2 ( IL-IR) / ( IL+IR)表示 ,IL 和 IR 分别指左圆偏振光强度和右圆偏振光强度 )相对较低 …     

一种基于免疫算法的新型因子分析算法

基于免疫算法的基本思想,提出了新的免疫主成分分析法(IPCA),该方法将免疫算法中抗体对抗原的消除运算应用于二维数据矩阵的正交分解,可得到矩阵的特征值和特征向量.结果表明,IPCA与传统的主成分分析法比较,对HPLC-DAD模拟信号的计算结果基本一致.对HPLC-DAD实验信号的解析结果表明,将IPCA与窗口因子分析技术结合比传统的WFA具有更强的解析能力.     

用微量热法研究亚甲基蓝与溴酸根的氧化还原反应的热动力学

用微热量计对亚甲基蓝与溴酸根在水中的氧化还原反应进行了热动力学研究.根据在微量热计上测得的数据和算得该反应的热力学参数(活化焓、活化熵及活化自由能)、数率常数和动力学参数(活化能、指前因子及反应级数),对此反应的机理也作了探讨.     

Daubechies小波主成分回归法机理及算法研究

将小波变换与主成分回归相结合,提出一种新型多元校正算法---小波基主成分回归法。理论分析和仿真实验表明,该法可更有效地去除噪声,提取有用信息。将其用于氯霉素及甲硝唑实际药物体系分析,与主成分回归法(PCR)相比,得到的回收率总平均相对误差由1.70%下降到0.90%。此外,通过将统计判据和小波多尺度分析相结合,发展了一种新的因子数判定方法。理论和实验研究表明,该法比传统因子数判定法具有更高的可靠性。     

通过红外光谱结合化学计量学方法研究奥克托今的合成机理

通过红外(IR)光谱在线监测醋酐法合成奥克托今(HMX)的反应过程, 采用渐进因子分析(EFA)结合多元曲线分辨-交替最小二乘法(MCR-ALS)以及直观推导式演进特征投影法(HELP)等化学计量学方法对反应过程的光谱数据矩阵进行解析, 获得了各组分浓度变化曲线和对应的IR光谱; 并采用密度泛函理论(DFT)的B3LYP方法, 在6-31G^*基组水平上得到该化合物的全优化结构, 在振动分析的基础上求得体系的振动频率和IR光谱. 通过对比发现, MCR-ALS和HELP法可得出相互验证的一致结论; 将通过量子化学计算的中间体的IR光谱特征吸收峰与2种方法进行比较, 结果吻合, 从而推导出合理的反应机理. 实验结果表明, 化学计量学方法结合在线红外光谱是研究反应机理的有效手段, 对反应路线的选择具有指导意义.     

一种测定中药复方制剂中活性成分的计算分析方法

提出了一种测定中药复方制剂中活性成分含量的计算分析新方法。该方法将主 成分分析和广义秩消因子分析集成用于处理HPLC／DAD数据，并改进了传统的IND因 子指示函数，适用于定量计算分析复杂混和物体系中的待测成分。将其用于测定复 方丹参制剂中三七皂苷R1的含量，计算分析结果表明，本方法明显优于广义秩消因 子法，同时证明IND函数的改进提高了计算分析准确度。     

Mutual peak matching in a series of HPLC–DAD mixture analyses

One of the largest challenges in high performance liquid chromatography (HPLC) method development is the necessity for tracking the movement of peaks as separation conditions are changed. Peak increments are then used to build a mathematical model capable of minimizing the number of experiments in an optimization circuit. Method optimization for an unknown mixture is, moreover, complicated by the absence of any a priori information on component properties and retention times when direct signal assignment is not possible. On the contrary, achievement of the maximum separation becomes an important factor for successful identification or quantitation. In this case, the optimization may be based on assigning peaks of the same component chosen from different experiments to each other. In other words, mutual peak matching between the HPLC runs is required.

A new method for mutual peak matching in a series of HPLC with diode array detector (HPLC–DAD) analyses of the same unknown mixture acquired at varying separation conditions has been developed. This approach, called mutual automated peak matching (MAP), does not require any prior knowledge of the mixture composition. Applying abstract factor analysis (AFA) and iterative key set factor analysis (IKSFA) on the augmented data matrix, the algorithm detects the number of mixture components and calculates the retention times of every individual compound in each of the input chromatograms. Every candidate component is then validated by target testing for presence in each HPLC run to provide quantitative criteria for the detection of “missing” peaks and non-analyte components as well as confirming successful matches. The matching algorithm by itself does not perform full curve resolution. However, its output may serve as a good initial estimate for further modeling. A common set of UV-Vis spectra of pure components can be obtained, as well as their corresponding concentration profiles in separate runs, by means of alternating least-square multivariate curve resolution (ALS MCR), resulting in reconstruction of overlapped peaks.

The algorithms were programmed in MATLAB^® and tested on a number of sets of simulated data. Possible ways to improve the stability of results, reduce calculation time, and minimize operator interaction are discussed. The technique can be used to optimize HPLC analysis of a complex mixture without preliminary identification of its components.     

Generalized Gaussian reference curve measurement model for high‐performance liquid chromatography with diode array detector separation and its solution by multi‐target intermittent particle swarm optimization

In order to separate a high‐performance liquid chromatography with diode array detector (HPLC‐DAD) data set to chromatogram peaks and spectra for all compounds, a separation method based on the model of generalized Gaussian reference curve measurement (GGRCM) and the algorithm of multi‐target intermittent particle swarm optimization (MIPSO) is proposed in this paper. A parameter θ is constructed to generate a reference curve r(θ) for a chromatogram peak based on its physical principle. The GGRCM model is proposed to calculate the fitness ε(θ) for every θ, which indicates the possibility for the HPLC‐DAD data set to contain a chromatogram peak similar to the r(θ). The smaller the fitness is, the higher the possibility. The algorithm of MIPSO is then introduced to calculate the optimal parameters by minimizing the fitness mentioned earlier. Finally, chromatogram peaks are constructed based on these optimal parameters, and the spectra are calculated by an estimator. Through the simulations and experiments, the following conclusions are drawn: (i) the GGRCM‐MIPSO method can extract chromatogram peaks from simulation data set without knowing the number of the compounds in advance even when a severe overlap and white noise exist and (ii) the GGRCM‐MIPSO method can be applied to the real HPLC‐DAD data set. Copyright © 2014 John Wiley & Sons, Ltd.     

Comparison of a high‐performance liquid chromatography method for quantification of carbamazepine with chemiluminescent microparticle immunoassay

Carbamazepine is an antiepileptic drug widely used for the treatment of epilepsy. In the National Institute of Neurology, monitoring has been performed using the technique chemiluminescent microparticle immunoassay (CMIA) in an automated way during the last five years. The aim of this study was to develop a simple and rapid HPLC analytical method coupled to DAD‐UV detection for the determination of plasma concentrations of carbamazepine and compare its feasibility with those used in routine analysis. The developed HPLC method was fully validated and the applicability of the proposed method was verified through the analysis of plasma samples of patients and later compared with the quantification of the same plasma samples with the CMIA method. The limit of quantification obtained was 0.5 μg/mL. The mean value for recovery was 99.05% and the coefficient of variation (CV) was 5.6%. The precision and accuracy of this method were within the acceptable limits; inter‐ and intraday CV values were <10%. The correlation between the CMIA method and the developed HPLC method was very good (r ≈ 0.999). A Bland–Altman plot showed no significant bias between the results. The HPLC‐DAD method may be an alternative to determine and monitoring the carbamazepine levels in human plasma or serum. Copyright © 2015 John Wiley & Sons, Ltd.     

一种基于光谱信息计算解析的液相色谱滤噪新方法

提出一种基于光谱信息计算解析的高效液相色谱滤噪新方法.对于HPLC/DAD所获得的二维量测数据,取半峰高以上色谱数据点所对应的各列光谱数据进行计算处理,求得在检测波长下的单位标准光谱,并根据光谱维数据与单位标准光谱的关系,采用最小二乘法重构计算求出色谱维数据,从而去除色谱噪声.仿真实验和实测谱图的滤噪结果表明,该方法能有效滤除常见噪声,与中值滤波算法相比较,滤噪性能明显优于后者,是提高色谱定量分析准确度的有力工具.     

Multivariate curve resolution-particle swarm optimization: A high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic data is evaluated using simulated gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography–diode array detection (HPLC–DAD) data. To present a comprehensive study, different number of components and various levels of noise under proper constraints of non-negativity, unimodality and spectral normalization are considered. Calculation of the extent of rotational ambiguity in MCR solutions for different chromatographic systems using MCR-BANDS method showed that MCR-PSO solutions are always in the range of feasible solutions like true solutions. In addition, the performance of MCR-PSO is compared with other popular MCR methods of multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least squares (MCR-ALS). The results showed that MCR-PSO solutions are rather similar or better (in some cases) than other MCR methods in terms of statistical parameters. Finally MCR-PSO is successfully applied in the resolution of real GC–MS data. It should be pointed out that in addition to multivariate resolution of hyphenated chromatographic signals, MCR-PSO algorithm can be straightforwardly applied to other types of separation, spectroscopic and electrochemical data.     

Comparison of two different isolation methods of benzimidazoles and their metabolites in the bovine liver by solid-phase extraction and liquid chromatography–diode array detection

A new analytical method that uses high performance liquid chromatography–diode array detector (HPLC–DAD) was developed for the analysis of 14 benzimidazoles residues, including metabolites, in bovine liver. Samples were extracted using two different extraction procedures: with phosphate buffer after enzymatic hydrolysis (method A) or using organic solvent, i.e. acetonitrile (method B). Then, samples were purified on a strong cation exchange (SCX) cartridge and analyzed in HPLC/DAD. The recovery percentages, obtained spiking the matrix (liver) at concentrations of 500 and 100 μg kg^?1 with a standard mixture of benzimidazoles, were in the range 6–101% and 80–102% for methods A and B, respectively. The repeatability of the methods was assessed in all cases by the % of correlation value (CV) that was lower than 19%. The limits of quantification (LOQs) in the matrix for methods A and B were in the range 40–60 and 20–50 μg kg^?1, respectively. The best of the two methods, method B, was used for the analysis of 10 bovine liver samples.     

Determination of rank by augmentation (DRAUG)

Determining the rank of a chemical matrix is the first step in many multivariate, chemometric studies. Rank is defined as the minimum number of linearly independent factors after deletion of factors that contribute to random, nonlinear, uncorrelated errors. Adding a matrix of rank 1 to a data matrix not only increases the rank by one unit but also perturbs the primary factor axes, having little effect on the secondary axes associated with the random errors in the measurements. The primary rank of a data matrix can be determined by comparing the residual variances obtained from principal component analysis (PCA) of the original data matrix to those obtained from an augmented matrix. The ratio of the residual variances between adjacent factor levels represents a Fisher ratio that can be used to distinguish the primary factors (chemical as well as instrumental factors) from the secondary factors (experimental errors). The results gleaned from model studies as well as those from experimental studies are used to illustrate the efficacy of the proposed methodology. The method is independent of the nature of the error distribution. Limitations and precautions are discussed. An algorithm, written in MATLAB format, is included. Copyright © 2011 John Wiley & Sons, Ltd.     

Electron transfer reactivity in matrix-assisted laser desorption/ionization (MALDI): ionization energy, electron affinity and performance of the DCTB matrix within the thermochemical framework

DCTB [(H(3)C)(3)C-p-Ph-CH=C(CH(3))-trans-CH=C(CN)(2)] has recently advanced to the most promising matrix material for matrix-assisted laser desorption/ionization (MALDI) within material sciences. However, data that would allow the evaluation of the electron-transfer reactivity within a thermochemical framework are sparse. The present study reports the first-time determination of the ionization energy (IE) of DCTB applying photoelectron (PE) spectroscopy. The experimental IE (8.54 +/- 0.05 eV) is in excellent agreement with the theoretical value of 8.47 eV, obtained by AM1 calculations. The same level of theory determines the electron affinity (EA) as 2.31 eV. Model analytes of known thermochemistry (phenanthrene [C(14)H(10)], anthracene [C(14)H(10)] and fluorofullerene [C(60)F(46/48)]) are used to bracket the electron-transfer reactivity within DCTB-MALDI. The formation of molecular ions of these analytes either is expected or is beyond the thermochemical accessibility of the DCTB matrix.