Direct and simultaneous spectrofluorometric determination of naproxen and salicylate in human serum assisted by chemometric analysis

A direct method for the simultaneous determination of naproxen and salicylate in human serum is reported, based on a combination of spectrofluorometric measurements with two multivariate calibration techniques: partial least-squares (PLS-1) and the novel net analyte preprocessing (NAP). The method is rapid, selective and sensitive, and is based on the measurement of the fluorescence spectra of NH₃ alkalinized whole human sera at the excitation wavelength of 315 nm. It can be applied within the ranges of concentrations 50-200 ng ml⁻¹ for naproxen and 100-300 ng ml⁻¹ for salicylate. The employed chemometric techniques have been compared on the basis of the statistical indicators for calibration and validation. Reproducibility and interference studies in abnormal sera have also been carried out.     

基于数据挖掘的图书馆数据预处理方法研究

通过挖掘高校图书馆大量的借阅历史数据,从中提取出切实有用的信息及有效的借阅规则,可为读者提供个性化的推荐服务,进而提高图书馆馆藏图书的流通率。但是,高校图书馆数据具有类型复杂、数据量庞大、冗余值较多等特点,很大程度上影响了挖掘效率。因此,在对图书馆数据挖掘之前,先对这些数据进行预处理,就可很好地解决挖掘效率问题。本文结合内蒙古工业大学图书馆一段时间的流通数据,借助SQL Server2012数据库管理工具,详细研究了基于数据挖掘的数据清洗、数据转换和数据规约3种图书馆数据的预处理方法。     

指纹识别预处理方法

文章结合指纹的方向特性,提出了一套可行的指纹识别预处理算法,并对前景/背景提 取部分的算法进行了改进。通过算法仿真验证,其处理结果比较理想。     

噪音相位图中强莫尔条纹密度和方位的研究

本文采用一种自动条纹分析的预处理方法,对噪音相应图中的莫尔条纹密度和方向的估计进行了分析研究。这些信息能用来构造相位-跳转-保存-过滤方法,提高二维展开相位法的性能以及可以使用坚固片断的相位数据。这种方法的特点是：对噪音反应非常灵敏,测试和参数的估算都要在在傅里叶范围内进行并可以做成模型用于估算研究。     

基于红外背景复杂程度描述的小目标检测算法

提出了关于红外“背景复杂程度”的基本概念,发现并论述了一种有效的红外背景复杂程度定量描述方法。此外,提出了一种基于红外背景复杂程度描述的自适应Butterworth高通滤波方法,实现了具备一定普适性的红外图像预处理。在小目标检测问题的研究中,构造并实现了一种更具实用性的先检测后跟踪小目标检测算法,此算法在一个完整的框架下实现了红外图像预处理过程和小目标检测过程的自适应统一。     

两种基于差分的红外点目标图像预处理技术的研究

图像去噪是图像处理中的重要组成部分.目前,针对点目标检测,人们已经发展了一系列比较成熟的滤波预处理算法.本文通过对红外点目标图像预处理方法的研究,给出了两种基于差分的背景抑制算法.最后针对仿真结果,对这两种背景抑制方法的去噪性能进行了分析比较.     

双面镜多元并扫遥感图像的几何校正方法

双面镜多元并扫成像方式在星载仪器中已得到越来越广泛的应用,但相应的遥感图像的预处理工作的重要性也日渐突出.文中从成像方式和仪器结构方面考虑,针对双面镜多元并扫成像方式提出了一套0级数据的几何方法,并对处理前后的图像质量进行了比较.结果表明,该校正方法可有效地改善图像的几何质量,校正效果良好.     

用Daubechies小波软阈值法对近红外光谱数据进行预处理

提出了一种基于Daubechies小波软阈值的近红外光谱预处理方法,并用该方法处理了两类不同苹果的近红外光谱数据。该方法选用归一化相关系数(Normalized Correlation,NC)和峰值信噪比(Peak Signal to Noise Ratio,PSNR)作为定量分析指标.与常用的矢量归一化法(Vector Normalization,VN)和标准正态变量校正法(Standard normal Variate,SNV)相比,该方法优势明显,既能有效去除噪声,又能很好地保留光谱的特征细节信息,提高了后续光谱分析过程中建模的稳健性和模型预测的精确度.     

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.     

Chemometric quality control of chromatographic purity

It is common practice in chromatographic purity analysis of pharmaceutical manufacturing processes to assess the quality of peak integration combined by visual investigation of the chromatogram. This traditional method of visual chromatographic comparison is simple, but is very subjective, laborious and seldom very quantitative. For high-purity drugs it would be particularly difficult to detect the occurrence of an unknown impurity co-eluting with the target compound, which is present in excess compared to any impurity. We hypothesize that this can be achieved through Multivariate Statistical Process Control (MSPC) based on principal component analysis (PCA) modeling. In order to obtain the lowest detection limit, different chromatographic data preprocessing methods such as time alignment, baseline correction and scaling are applied. Historical high performance liquid chromatography (HPLC) chromatograms from a biopharmaceutical in-process analysis are used to build a normal operation condition (NOC) PCA model. Chromatograms added simulated 0.1% impurities with varied resolutions are exposed to the NOC model and monitored with MSPC charts. This study demonstrates that MSPC based on PCA applied on chromatographic purity analysis is a powerful tool for monitoring subtle changes in the chromatographic pattern, providing clear diagnostics of subtly deviating chromatograms. The procedure described in this study can be implemented and operated as the HPLC analysis runs according to the process analytical technology (PAT) concept aiming for real-time release.