Near-infrared (NIR) spectroscopy for the non-destructive and fast determination of geographical origin of Angelicae gigantis Radix

Near-infrared (NIR) spectroscopy has been utilized to discriminate the Korean and Chinese Angelicae gigantis Radix. Decursin, one of the major ingredients in Korean Angelicae gigantis Radix, was preliminarily identified using TLC and HPLC. Decursin was then used as a unique marker component for successful discrimination between two geographical origins. Second derivative spectra were used to reduce baseline variations observed in original diffuse reflectance spectra as well as to enhance spectral features. The unique 1625 nm band of decursin in Korean samples provided clear spectral differences over Chinese samples. To develop a calibration model, soft independent modeling of class analogy (SIMCA) was used. With the use of the SIMCA model, independent sample datasets collected at two different periods were predicted. The Korean and Chinese samples were clearly identified with 100% accuracy.     

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies

Near-infrared spectroscopy (NIRS) is a fast and non-destructive analytical method. Associated with chemometrics, it becomes a powerful tool for the pharmaceutical industry. Indeed, NIRS is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Moreover, NIRS can be implemented during pharmaceutical development, in production for process monitoring or in quality control laboratories.This review focuses on chemometric techniques and pharmaceutical NIRS applications. The following topics are covered: qualitative analyses, quantitative methods and on-line applications. Theoretical and practical aspects are described with pharmaceutical examples of NIRS applications.     

Integration of SIMCA and near-infrared spectroscopy for rapid and precise identification of herbal medicines

The recognition, control, and monitoring of herbal medicinal materials is a crucial work and challenge in the pharmaceutical industry. Consequently, the development of a rapid and accurate inspection method and model is an important goal and job. The raw materials of a variety of herbal medicines were measured using nondestructive near-infrared spectroscopy with soft independent modeling of class analogy to build up the classification model. The adulterated samples could be eliminated by the analysis of the model, and identification rates were demonstrated in the range of 98–100%. The method could be applied not only to the pharmaceutical industry but also to the food industry. Food materials can be measured with the inspection model for effective identification and determination of adulteration.     

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor‐like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject‐multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High‐Resolution Magic‐Angle‐Spinning Nuclear Magnetic Resonance (HR‐MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂‐filtered, diffusion‐filtered and conventional water‐presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.     

红外光谱和聚类分析法在药用菊花产域分类鉴别中的应用

目的：对不同产地的213个药用菊花样本进行产域模式识别方法学研究。方法：运用模式识别技术的SIMCA法，凭借红外光谱具有的指纹特性，构建药用菊花样品的红外谱图库，作为模式识别提取的特征数据，以随机分取的150个样品为训练集，剩余为实验集。结果：该方法的每类产区的正确识别率均达到了60％以上，取得了较满意的分类效果。结论：红外光谱法结合聚类分析法可作为中药材产域分类鉴别的一种新的现代化方法。     

Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review

The emergence of chemical imaging (CI) has gifted spectroscopy an additional dimension. Chemical imaging systems complement chemical identification by acquiring spatially located spectra that enable visualization of chemical compound distributions. Such techniques are highly relevant to pharmaceutics in that the distribution of excipients and active pharmaceutical ingredient informs not only a product's behavior during manufacture but also its physical attributes (dissolution properties, stability, etc.). The rapid image acquisition made possible by the emergence of focal plane array detectors, combined with publication of the Food and Drug Administration guidelines for process analytical technology in 2001, has heightened interest in the pharmaceutical applications of CI, notably as a tool for enhancing drug quality and understanding process. Papers on the pharmaceutical applications of CI have been appearing in steadily increasing numbers since 2000. The aim of the present paper is to give an overview of infrared, near-infrared and Raman imaging in pharmaceutics. Sections 2 and 3 deal with the theory, device set-ups, mode of acquisition and processing techniques used to extract information of interest. Section 4 addresses the pharmaceutical applications.     

注射用益气复脉(冻干)味的测定

目的 建立一种用于测定注射用益气复脉（冻干）滋味的方法,以保证不同生产批次间的质量稳定。方法 采用Alpha MOS公司电子舌（Astree）测定注射用益气复脉（冻干）6批样品,以0.01 mol/L盐酸为校准溶液,以蒸馏水为稀释溶剂,样品采集时间为60 s,每次分析时间120 s,每个样品重复测9次,利用Alpha Soft V14.2软件进行数据分析。结果 注射用益气复脉（冻干）的酸、甜、苦3个滋味表现较明显,且批间差异不大;利用主成分分析能很好地区分不同批次样品在味道上的差异;不同样品间味道的相对强度存在差异;利用独立建模分析对其他批次样品进行鉴定,识别率为100%。结论 所建立的方法具有样品处理方法简单、操作简单、快速、检测成本低、污染小等特点,可用于注射用益气复脉（冻干）滋味的测定。     

Analysis of the procedures used to evaluate suicide crime scenes in Brazil: A statistical approach to interpret reports

This study uses statistical techniques to evaluate reports on suicide scenes; it utilizes 80 reports from different locations in Brazil, randomly collected from both federal and state jurisdictions. We aimed to assess a heterogeneous group of cases in order to obtain an overall perspective of the problem. We evaluated variables regarding the characteristics of the crime scene, such as the detected traces (blood, instruments and clothes) that were found and we addressed the methodology employed by the experts. A qualitative approach using basic statistics revealed a wide distribution as to how the issue was addressed in the documents. We examined a quantitative approach involving an empirical equation and we used multivariate procedures to validate the quantitative methodology proposed for this empirical equation. The methodology successfully identified the main differences in the information presented in the reports, showing that there is no standardized method of analyzing evidences.     

红外光谱法结合系统聚类和SIMCA模式识别法快速鉴别羌活种子

目的采用傅里叶变换红外光谱法鉴别羌活种子。方法在4 000～400 cm-1内测定羌活种子光谱吸收峰,应用系统聚类和SIMCA模式识别法鉴别羌活和宽叶羌活的种子。结果运用系统聚类法,当聚类距离为15时,两种羌活可明显分为2类;运用SIMCA模式识别法,所建模型对羌活和宽叶羌活的种子识别率分别达96%和97%,拒绝率均为100%,两者盲样检测准确率也在90%以上,所建模型可用于样品检测。结论应用红外光谱法结合系统聚类和SIMCA模式识别法,可以简单、快速、准确地鉴别羌活种子,该方法为鉴别羌活种子的来源及真伪提供了一种新的技术手段。     

红外光谱结合化学计量学评价不同产地何首乌

目的建立快速评价不同产地何首乌的方法。方法运用化学计量预处理方法处理4个产地何首乌样品的红外光谱,分析各产地样本的平均光谱,建立主成分分析鉴别和SIMCA预测模型。结果 ①何首乌含有蒽醌类、二苯乙烯类、磷脂类、多糖和苷类等化学成分。②主成分分析模型能基本鉴别不同产地何首乌。③不同产地何首乌的差异主要体现在芳香族化合物、草酸钙、磷脂、糖蛋白、多糖和苷类共6种化学成分上。④SIMCA模型预测未知样品的准确率达到100%。结论 红外光谱结合化学计量学是一种快速、无损地评价何首乌的有效方法。