Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data

Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from the tip of the stem in each of four strains, was analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Twenty-four extracts from mulberry leaf showed independent spectra by 1H NMR. The separation of leaf extraction data due to the difference at six locations was achieved in the PCA score plot as correlation PC1 (86.1%) and PC3 (4.6%) and showed two loading plots, suggesting classification by leaf position as an independent variable in the loading plot. Moreover, the difference among six locations clarified the seven highest discrimination powers by the SIMCA method. Meanwhile, the PCA score plot obtained classification by the variety of mulberry strains with three loading plots, but the SIMCA method did not give a peak by classification.     

Multivariate analysis for 1H-NMR spectra of two hundred kinds of tea in the world.

NMR measurements coupled with pattern-recognition analysis offer a powerful mixture-analysis tool for latent-feature extraction and sample classification. As fundamental applications of this analysis for mixtures, the 1H spectra of 176 kinds of green, black, oolong and other tea infusions were acquired by a 500 MHz NMR spectrometer. Each spectrum pattern was analyzed by a multivariate statistical pattern-recognition method where Principal Component Analysis (PCA) was used in combination with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA effectively selected variables that contribute to tea categorization. The final PCA resulted in clear classification reflecting the fermentation and processing of each tea, and revealed marker variables that include catechin and theanine peaks.     

核磁共振技术结合模式识别对中药马兜铃酸亚急性生化效应的研究

利用核磁共振技术结合模式识别方法,研究了马兜铃酸(AristolochicAcid,AA)的亚急性生化效应.大鼠连续5日腹腔注射马兜铃酸后,不同时间段尿液¹HNMR谱显示与肾小管及肾乳头受损相关的标记物(NMRmarker)浓度有显著变化;铬酸钠、氯化汞、二溴乙胺、盐酸肼和异硫氰酸-α-萘酯,并利用主成分分析法对造成的肝肾损伤模型组、AA组和对照组的大鼠尿液¹HNMR谱解析和分类.¹HNMR谱中各种代谢物的谱峰强度变化及主成分分析结果均显示,马兜铃酸引起的肾损伤与肾小管及肾乳头损伤模型类似,且随给药量的积累,肾损伤范围扩大程度加深,引起肾脏不可逆损伤.该方法可用于中药的毒理学研究.     

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.     

Differentiation of opium and poppy straw using capillary electrophoresis and pattern recognition techniques

Opium samples from four different locations and poppy straw from different plant varieties have been assayed using micellar capillary electrophoresis incorporating a sweeping technique. Individual alkaloids (morphine, codeine, papaverine, noscapine, thebaine, oripavine, reticuline and narceine) were quantitatively determined in the different samples by a validated capillary electrophoresis method. Unsupervised pattern recognition of the opium samples and the poppy straw samples using hierarchical cluster analysis (HCA) and principal component analysis (PCA), showed distinct clusters. Supervised pattern recognition using soft independent modelling of class analogy (SIMCA) was performed to show individual groupings and allow unknown samples to be classified according to the models built using the CZE assay results.     

核磁共振技术及模式识别对硝酸镥急性毒性的研究

通过分析Wistar大鼠灌胃0.2,2,10和100mg/kg体重硝酸镥[Lu(NO₃)3]48h内尿液的¹HNMR谱,研究了重稀土化合物Lu(NO₃)3在大鼠体内的急性生物效应.以氯化汞、铬酸钠、四氯化碳、盐酸肼和异硫氰酸-α-萘酯为模型药物,利用模式识别技术解析大鼠尿液的¹HNMR谱,对不同剂量Lu(NO₃)3的生物效应进行了分类.结果表明,应用核磁共振和模式识别相结合的方法,可清楚地认识稀土化合物生物效应,低剂量Lu(NO₃)3(0.2,2mg/kg体重)的毒性与肾毒化合物铬酸钠类似,而高剂量Lu(NO₃)3(10,100mg/kg体重)归入肝毒化合物四氯化碳组.该方法也可用于其它金属化合物及中药等药物的毒理学研究.     

傅里叶变换红外光谱结合模式识别法快速鉴别食用油的真伪

采用傅里叶变换红外光谱(FTIR)结合簇类独立软模式识别技术(SIMCA)建立了真伪食用油的快速鉴别方法. 该方法依据FTIR 的指纹特性, 收集并分析了53 个合格食用油和13 个伪造食用油的FTIR 谱图; 通过对谱图取二阶导数和标准化处理, 主成分分析(PCA)提取特征变量; 采用SIMCA 方法分别随机选取43 个合格食用油和9 个伪食用油样品的FTIR 谱图组成训练集, 构建得到真伪食用油的SIMCA 分类模型. 该模型经过剩余10 个合格食用油和4 个伪食用油的验证, 正确识别率达到了100%. 说明FTIR 结合SIMCA 可能成为快速鉴别食用油真伪的一种新方法.     

Application of multivariate methods to scalp hair metal data to distinguish between drug-free subjects and drug abusers

Human scalp hair samples of drug-free subjects and drug abusers (heroin and cocaine-heroin abusers) were analysed for trace metals by flame atomic absorption spectrometry (FAAS), flame atomic emission spectrometry (FAES) and electrothermal atomic absorption spectrometry (ETAAS). The classification of drug-free subjects and drug abuses groups with four multivariate methods using the metal contents in hair samples as discriminant variables has been discussed. Principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) allow distinguishing the two groups correctly. However, predictions by SIMCA are less satisfactory. Thirteen elements (Ag, Al, Ca, Cd, Cr, Cu, K, Mg, Mn, Na, Ni, Pb, and Zn) were determined by FAAS/FAES/ETAAS in 53 hair samples (16 samples of drug-free people and 37 samples of drug abusers). Human hair samples were prepared as aqueous slurries as sample pre-treatment and they were analysed using the slurry sampling technique. The half-range central value transformation was novelty used as data pre-treatment to homogenise the data. Grouping in the samples (drug-free people and drug abusers) were observed by using PCA and CA (squared Euclidean distance between objects and Ward method as clustering procedure). The application of LDA gave a correct recognition assignation percentage of 91.7 and 100.0% for the drug-free people and drug abusers, respectively, at a significance of 5%, while SIMCA offered recognition percentages of 83.3 and 91.3% for drug-free people and drug abusers, respectively, also at 5%. Finally, some studies were developed to classify heroin abusers and polidrug abusers (cocaine-heroin abusers) by the cited multivariate statistical methods. Recognition percentages of 90.9 and 100.0% were reached for heroin abusers and polidrug abusers groups, respectively, after LDA, while these percentages decreased to percentages lower than 90.0% when SIMCA was applied.     

茶叶化学成分指纹识别中样品制备方法的优化

根据指纹识别实验对样品的要求, 以¹H NMR和主成分分析(PCA)为测试与评估手段, 对茶叶中化学成分提取条件的优化方法进行了研究. 针对生物指纹识别实验中样本体系复杂及化学组分含量多等特点, 利用主成分分析对检测样品质控的多因素进行综合分析, 最终确立了茶叶中化学成分提取制备的实验条件. 利用所建立的样品制备程序对4种茶叶进行预处理和测试, 结果表明, 所建立的方法稳定、可靠, 可以满足茶叶代谢物¹H NMR指纹识别研究的要求. 本文所提出的优化方法简单可靠, 可用于复杂样本体系标准样品预处理方法的建立.     

Urinary metabonomic study using a CUMS rat model of depression

Chronic unpredictable mild stress (CUMS) is a well‐validated model of depression. In this study, a urinary metabonomics method based on the NMR spectrometry was used to study the metabolic perturbation in CUMS‐induced rat depression model. With pattern recognition analysis, a clear separation of CUMS rats and healthy controls was achieved, and nine endogenous metabolites contributing to the separation were identified. CUMS‐treated rats were characterized by the increase of glycine, pyruvate, glutamine, and asparagines, as well as the decrease of 2‐oxoglutarate, dimethylglycine, citrate, succinate, and acetate. The urinary biochemical changes related to the metabolic disturbance in CUMS induced depression, and the possible correlations with live qi stagnation in traditional Chinese medicine are discussed. The work shows that CUMS is a reliable model for studying depression, and the noninvasive urinary metabolomic method is a valuable tool to investigate the biochemical pertubations in depression as an early diagnostic means. Copyright © 2012 John Wiley & Sons, Ltd.     

基于近红外光谱分析技术的食品包装塑料的定性分析

该文基于近红外漫反射光谱分析技术对食品包装材料聚乙烯、聚丙烯进行定性判别试验研究,选取不同波段范围、采用不同光谱预处理方法,使用主成分分析法(Principal component analysis,PCA)结合SIMCA、贝叶斯判别、K-近邻3种模式识别方法建立定性预测模型,并根据正确识别率比较了各模型预测性能。结果表明：使用SIMCA方法、贝叶斯判别、K-近邻3种方法建立的定性校正模型均在1 050~1 550 nm波长范围内效果较好;采用矢量归一化、标准正态变量变换、中心化、滑动均值滤波、多项式平滑滤波、一阶微分6种光谱预处理方法和上述3种模式识别方法对塑料样品近红外光谱进行了数据处理,其中在1 050~1 550 nm范围内,主成分因子数为3,采用原始光谱建立的K-近邻定性校正模型较优,对样品校正集和预测集的正确识别率均为100%。可为食品包装材料聚乙烯、聚丙烯的快速鉴别研究提供参考。     

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and (1)H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.     

纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.     

黄酮类化合物^13C核磁共振谱的模式识别分类研究

黄酮类化合物广泛存在于植物的各个部位,根据其结构,主要分为黄酮、黄酮醇、双氢黄酮、异黄酮等。对于大多数植物化学工作者来说,解析一个未知化合物都必须经历先确定骨架类型,后确定基团位置这一过程。而前者则需要对该类型的数据规律有充分的认识,否则,就可能导致错误结果。     

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical,Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.     

汽油样品类型的模式识别研究与应用

研究了应用化学计量学方法解决汽油单体烃的气相色谱分析中单体烃定性库的自动选择问题。通过提取汽油单体烃谱图中的29个组分及其含量信息作为特征值,利用主成分分析法对不同工艺得到的催化裂化汽油、焦化汽油、直馏汽油、重整汽油和烷基化汽油进行分类,结合相似分析方法(即SIMCA方法)建立了各类汽油样本的类模型,借助这些类模型可以实现对未知样本的类型判别。所提出的识别方法可方便快速地判别待分析样品所属的汽油类别,并据此推荐适合该样品的定性模型库,从而实现汽油单体烃的快速、自动分析。     

核磁共振技术测试不同性别大鼠尿液代谢物的研究

采用核磁共振波谱技术测试不同性别大鼠尿液代谢物,分析性别因素对大鼠尿液代谢成分的影响.大鼠尿液核磁共振氢谱（1HNMR谱）结果采用主成分分析（principal component analysis,PCA）和正交偏最小二乘判别分析（orthogonal to partial least squares discriminant analysis,OPLS-DA）方法分析,得到不同性别大鼠尿液中的差异性代谢物.PCA分析结果显示2组尿液代谢成分有明显的差异,进一步进行OPLS-DA分析可以判别出2组尿液中具有差异性的代谢物.结果显示,雌性大鼠尿液中的丙氨酸、缬氨酸、鸟氨酸等氨基酸类以及乙酸、硫胺、氨基马尿酸、苯乙胺、氧氨嘧啶等代谢物含量高于雄性大鼠,差异有统计学意义（p〈0.05）.雄性大鼠尿液中的甲胺、二甲胺、三甲胺、肌酸酐、尿囊素、延胡索酸、甲酸等代谢物则明显高于雌性大鼠,差异有统计学意义（p〈0.05）.性别因素对大鼠尿液中的代谢成分有一定的影响.     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

基于质子转移反应-飞行时间质谱快速鉴别不同产地闽北水仙茶

采用质子转移反应-飞行时间质谱仪(PTR-TOF-MS), 构建了3个产地(武夷山、建阳、建瓯)113个闽北水仙茶样品香气的化学指纹图谱, 对所得的闽北水仙茶香气指纹图谱进行主成分分析(PCA), 获得了不同产地闽北水仙茶样品的质谱信息特征, 然后采用软独立建模分类法(SIMCA)、K最邻近结点算法(KNN)、偏最小二乘判别分析法(PLS-DA)对闽北水仙茶的质谱信息进行了模式识别.结果表明, PTR-TOF-MS结合分类识别模式能有效区分不同产地的闽北水仙茶.PCA 提取了3个主成分, 累计贡献率为84.66%;3个识别模型的校正集判别正确率分别为89.38%、100.00%和100.00%, 预测集的判别正确率分别为83.18%、 96.46%和95.57%.基于此成功建立了不同产地的闽北水仙茶识别模型.本方法无需样品预处理、分析速度快、灵敏度高、对茶叶无损伤, 为茶叶产地溯源提供了新方法.