裂解气相色谱法分析蓝色中性笔字迹的书写墨水种类及墨迹随书写时间的变化规律

中性笔是当前比较流行的一种书写工具,其墨水多采用颜料作为色料成分,很难用水和有机溶剂进行提取,故无法利用分析圆珠笔油墨和水性笔墨水所采用的气相色谱或高效液相色谱等方法进行检验。目前在法庭科学领域内还没有一种有效的方法用于分析中性笔字迹的书写墨水。采用裂解气相色谱法分析了蓝色中性笔字迹的书写墨水,根据色谱峰的个数和保留时间将收集的65个样品分成3类,其中大部分样品属于具有铜酞菁颜料主要色谱峰的第二大类。色谱峰的保留时间和峰面积比值的分析结果表明所建立的分析方法重现性好且稳定。在此基础上,对蓝色中性笔墨迹随书写时间的变化规律进行了初步研究,得出了老化曲线。     

运用色谱指纹图谱与化学计量学方法对灵芝进行分类

采用95%乙醇为提取溶剂,运用高效液相色谱(HPLC)指纹图谱技术与化学计量学方法,对11个不同灵芝菌株子实体进行分类。通过相似度分析分别获得提取样品指纹图谱的13个共有峰及每个样品之间的相似度;以相对共有峰面积为分析参数,运用化学计量学方法包括聚类分析(HCA)、主成分分析(PCA)及判别分析(DA)对其进行分类,结果分为紫芝、赤芝和美国大灵芝3类。实验结果表明,用化学计量学的方法对灵芝样品的指纹图谱数据进行分析,是一种可用于其分类的科学方法。     

相似系统理论定量评价中药材色谱指纹图谱的相似度

研究相似系统理论定量评价中药材提取物液相色谱指纹图谱的相似度。以相似系统理论对栀子药材提取物高效液相色谱指纹图谱的相似度评价,发现相似系统理论对数据的差异比较敏感,而且相似度的计算结果能够反映样品的相对差异。相似系统理论可以作为一种评价中药色谱指纹图谱相似度的新方法。     

高血压病肝阳上亢证血清代谢指纹图谱研究

根据临床高血压病肝阳上亢证的诊断标准,选择典型病例,以高效液相色谱-质谱联用技术分析高血压病肝阳上亢证患者和健康志愿者的血清成分,采用"中药色谱指纹图谱相似度评价系统"对齐、匹配色谱峰,采用PCA方法进行模式识别,以MS检测标记物分子量。两组高效液相色谱数据可以在得分图实现分类,与健康志愿者作比较,患者机体相关代谢发生显著变化。高效液相色谱质谱联用结合PCA模式识别的代谢组学方法可用于研究复杂条件下机体病理的生理变化,为高血压病肝阳上亢证病证诊断提供科学依据。     

邓老凉茶颗粒的超高效液相色谱质谱联用指纹图谱研究

建立了适用于邓老凉茶颗粒质量控制的超高效液相色谱质谱联用指纹图谱分析方法。样品采用甲醇索氏萃取60 min,萃取液采用超高效液相色谱质谱法进行指纹图谱分析。色谱柱采用Waters ACQUITY HSST3 C18(150 mm×3.0 mm,1.8μm),以0.5%甲酸-乙腈为流动相进行梯度洗脱,流速为0.8 mL/min,柱温35℃。质谱采用负离子ESI模式,选择基峰离子流质量色谱图进行指纹图谱研究。32个共有峰在15 min内得到良好分离,其中15个共有峰通过对照品进行了确证。通过《中药色谱指纹图谱相似度评价系统2004A版》对邓老凉茶颗粒样品进行相似度分析,15个批次样品的相似度均达到0.960以上,表明邓老凉茶颗粒的产品质量稳定性很好。以32个共有峰的相对峰面积进行主成分分析,邓老凉茶颗粒样品之间的细微质量差异得到明显区分。该方法快速、高效、可靠,可有效地用于邓老凉茶颗粒的质量控制。     

布渣叶药材中黄酮的高效液相色谱指纹图谱分析

建立了布渣叶药材中黄酮类化合物的高效液相色谱指纹图谱。在高效液相色谱分析中采用Kromasil C18色谱柱(250 mm×4.6 mm,5μm)作固定相及以同比例混合的甲醇和稀磷酸溶液(0.1+99.9)的混合溶液作流动相进行梯度淋洗实现各化合物的分离,加入牡荆苷作内标,在276 nm波长处作紫外检测。应用所提出的方法分析了取自全国9个不同产地的上述药材的样品。在所得的高效液相色谱指纹图谱上显示了9个样品的黄酮类化合物的18个色谱峰。此18个色谱峰的相对保留时间和相对峰面积的数据间具有很好的相似度,结果表明:所建立的指纹图谱可用于此类药材的质量控制。     

基于水溶性浸出物及HPLC指纹图谱评价不同贮存年限半夏的品质

研究不同贮存年限半夏药材的浸出物,建立浸出物的HPLC特征指纹图谱,为半夏药材品质评控提供参考。浸出物测定方法采用药典法;HPLC指纹图谱的色谱条件:采用C_(18)色谱柱(150 mm×4.6 mm,5μm),以水–甲醇为流动相,梯度洗脱,流量为0.8 m L/min,检测波长为260 nm,柱温为25℃,进样体积为50μL。采用相似度评价及聚类分析技术揭示14批样品的相似性及差异性。14批半夏浸出物有12批合格,2批不合格。建立14批半夏浸出物样品的高效液相指纹图谱,确定了3个共有峰,共有峰保留时间的相对标准偏差小于2%,峰面积的相对标准偏差差异较大。1~#~7~#半夏样品有12个共有峰,共有峰保留时间的相对标准偏差小于1.5%,峰面积的相对标准偏差差异较大。各批次药材化学成分组成及含量均存在一定差异。以半夏浸出物数据与其高效液相色谱指纹图谱数据为基础,将指纹图谱相似度评价与聚类分析结合起来,用浸出物含量及评价软件测评结果对半夏品质进行综合评估,可以更精确地对半夏药材进行质量控制。     

连花清瘟颗粒有效成分提取及高效液相色谱指纹图谱分析北大核心CSCD

建立连花清瘟颗粒的高效液相色谱(HPLC)指纹图谱,确认其化学成分并结合化学模式识别技术对其进行系统、科学的质量评价。使用化学对照品比对分析和超高效液相色谱-飞行时间质谱(UPLC-Q-TOF-MS)进行定性鉴定;Swell Chromplus TM-C18色谱柱(150 mm×4.6 mm,5μm);以0.1%磷酸-乙腈流动相,梯度洗脱;检测波长278 nm,进行高效液相色谱(HPLC)。采用中药色谱指纹图谱相似度评价系统及聚类分析、主成分分析分析软件进行化学模式识别方法分析。结果表明,通过指纹图谱相似度计算发现,10批样品的HPLC指纹图谱中发现16个共有峰,相似度达到0.967。确认了9个化学成分,包括新绿原酸、绿原酸、隐绿原酸、异绿原酸A、苦杏仁苷、连翘苷、连翘酯苷A、大黄素、大黄酚、大黄酸。该方法简单、稳定、重复性好,可用于该药物的质量评价。     

王老吉广东凉茶颗粒的超高效液相色谱指纹图谱研究

建立了适用于王老吉广东凉茶颗粒质量控制的超高效液相色谱指纹图谱分析方法。样品采用甲醇超声萃取30 min,萃取液用超高效液相色谱法进行指纹图谱分析。色谱柱采用Waters ACQUITY HSS T3 C18(3.0mm×150 mm,1.8μm),以0.5%甲酸-乙腈为流动相进行梯度洗脱,流速为0.8 mL/min,柱温35℃。28个共有峰在18 min内得到良好分离,通过对照品对其中4个峰进行了确证。对王老吉广东凉茶颗粒样品进行了相似度分析,23个批次样品的相似度均达到0.95以上;以28个共有峰的相对峰面积进行主成分分析,23批次样品均落在同一区域。相似度评价和主成分分析结果均表明,王老吉广东凉茶颗粒的产品质量稳定性好。该方法快速、高效、稳定,可用于王老吉广东凉茶颗粒的质量控制。     

t检验法用于评价中药色谱指纹图谱的相似度

本文采用t检验法评价中药色谱指纹图谱的相似度。利用来自六个不同厂家的三黄片样品的高效液相色谱指纹图谱数据来验证t检验法的适用性,并用主成分分析(PCA)进一步验证方法的可行性。在PCA的聚类图上,t检验无显著性差异的样品聚为一类,t检验有显著性差异的样品各为一类。结果表明:用t检验法能客观地反映中药样品间的相似性和差异性。     

Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis

Two clinical data sets were applied for pattern recognition in order to discover the correlation between urinary nucleoside profiles and tumours. One data set contains 168 clinical urinary samples, of which 84 specimens are from female thyroid cancer patients (malignant tumour group), and the other samples were collected from healthy women (normal group). However, 168 clinical urinary samples comprised the second data set, too. In all the specimens, each number of the samples for both uterine cervical cancer patients (malignant tumour group) and healthy females (normal group) is 60, and the other 48 samples were collected from uterine myoma patients (benign tumour group). For the two data sets, the separation and quantitative determination of the clinical urinary nucleosides were performed by capillary electrophoresis (CE). The pattern recognition was achieved applying multiple layer perceptron artificial neural networks (MLP ANN) based on conjugate gradient descent training algorithm. Moreover, applying the proposed principal component analysis (PCA) input selection scheme to MLP ANN, the accuracy rate of the pattern recognition was improved to some extent (or without any deterioration) even by much simpler structure of MLP ANN. The study showed that MLP ANN based on PCA input selection was a promising tool for pattern recognition.     

A simple taste analyzing system design for visual and quantitative analysis of different tastes using multi-array chemical sensors and pattern recognition techniques

In this paper, we describe design of a simple taste analyzing system using sensory system based on a multi-array chemical sensor (MACS) and personal digital assistant (PDA) for visual and quantitative analysis of different tastes using pattern recognition techniques. The sensory system is communicated with PDA, which has several interesting benefits for data analysis and display, via wireless using the Bluetooth. A various pattern recognition techniques are adapted including spider map, principal component analysis (PCA) and fuzzy C-means (FCM) algorithm to classify visually data patterns detected by the sensory system. The proposed techniques can be determined the cluster centers and membership grade of patterns through the unsupervised way. The membership grade of an unknown pattern, which does not shown previously, can be visually and analytically determined. Throughout the experimental trails, the taste analyzing system is demonstrated robust performance through data acquisition via wireless communication and visual and quantitative analysis of different tastes for the liquids. The system, which is implemented as a simple hand-held taste analyzing instrument, can be applicable to on-site taste monitoring.     

Supervised pattern recognition in food analysis

Data analysis has become a fundamental task in analytical chemistry due to the great quantity of analytical information provided by modern analytical instruments. Supervised pattern recognition aims to establish a classification model based on experimental data in order to assign unknown samples to a previously defined sample class based on its pattern of measured features. The basis of the supervised pattern recognition techniques mostly used in food analysis are reviewed, making special emphasis on the practical requirements of the measured data and discussing common misconceptions and errors that might arise. Applications of supervised pattern recognition in the field of food chemistry appearing in bibliography in the last two years are also reviewed.     

Interpretation of analytical chemical information by pattern recognition methods-a survey

Since the late sixties, pattern recognition techniques have been used by analytical chemists to facilitate the interpretation of multivariate analytical information. Most research within the field has focused on adapting pattern recognition methods to chemical data. This has been necessary since chemical data are often complicated by the fact that distributions are unknown. Through the first decade of chemical pattern recognition, promising results have been obtained even though the data sets studied have frequently been rather small for statistical analysis. The past few years have shown that an increasing number of analytical chemists are interested in the sheer utility of pattern recognition. This can be taken as a valid sign of a useful approach. The present communication surveys this development. Those methods which have proved most useful for analytical chemical data are described in some detail, and applications within the various fields of analytical chemistry are reviewed.     

Identification of Africanized honeybees

Gas chromatography and pattern recognition methods were used to develop a potential method for differentiating European honeybees from Africanized honeybees. The test data consisted of 237 gas chromatograms of hydrocarbon extracts obtained from the wax glands, cuticle, and exocrine glands of European and Africanized honeybees. Each gas chromatogram contained 65 peaks corresponding to a set of standardized retention time windows. A genetic algorithm (GA) for pattern recognition was used to identify features in the gas chromatograms characteristic of the genotype. The pattern recognition GA searched for features in the chromatograms that optimized the separation of the European and Africanized honeybees in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the peaks identified by the pattern recognition GA primarily contained information about differences between gas chromatograms of European and Africanized honeybees. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acted as an information filter, significantly reducing the size of the search space since it restricted the search to feature sets whose principal component plots showed clustering on the basis of the bees' genotype. In addition, the algorithm focused on those classes and/or samples that were difficult to classify as it trained using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a neural network. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a "smart" one-pass procedure for feature selection and classification.     

Molecular speciation of microparticles: application of pattern recognition techniques to laser microprobe mass spectrometric data

Attempts were made to enhance the ability of laser microprobe mass spectrometry (LAMMS) to identify molecular species in individual microparticles by applying pattern recognition methods. Principal component analysis (PCA) and canonical discriminant analysis were applied to LAMMS data for nickel-containing environmental particles. Detailed comparison of the two statistical methods demonstrated the utility of PCA. The successful application was highly dependent on the use of appropriate spectral normalization and feature extraction techniques prior to PCA. Although the test system involved only a small number of standard compounds, the LAMMS data were complicated by the effects of intra-particle heterogeneity common to environmental samples and by instrumental limitations. Pattern recognition techniques provided more accurate quantitative assignments of molecular species than were available by qualitative inspection of characteristic cluster ions or by simple spectral subtraction to compare particle data with a library of standard compounds. Results were substantiated by comparison with bulk analysis studies using wet chemical techniques.     

Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domain

A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3 s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods.     

Computer assisted clinical chemical diagnosis,pattern cognition and recognition by cluster analysis

Cluster analysis is a good tool for finding and defining groupings of patients with similar patterns of clinical chemical data. The same algorithm (k-means) used for pattern cognition can be employed for pattern recognition.     

目标识别因子分析解析完全未知混合体系的研究

本文以目标识别因子分析为基础, 将因子分析与聚类分析相结合, 给出了从完全未知混合体系中提取纯物种光谱的新方法。所得纯物种光谱经光谱检索作定性判别, 然后利用外标法对混合物中的各组份进行标定。该方法用于计算机模拟体系及三组份实际混合体系的红外光谱解析, 结果令人满意。     

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.