神经网络法在使用裂解气相色谱鉴别中草药中的应用

将以误差反向传播为训练算法的前馈式人工神经网络（ＢＰ－ＡＮＮ）首次艇于中草药的裂解气相色谱谱图解析。重点考察了如何表征和提取复杂的裂解色谱图中有价值信息,用于主成分分析方法处理后输入到有数经优化的神经网络中。实验证明,该广阔示仅可以正确识别样品所属种类,耐用对一示同实验时间、数据残缺等原因造成的噪音具有优异的抗干扰能力。     

改进的人工神经网络在传感器阵列信号处理中的应用

本文提出传感器阵列信号处理的人工神经网络模型，以Cu^ 2/Ca^ 2系统为研究对象．尝试了神经网络方法的效果。其最大相对误差不超过5．％，最大相对预测误差不超过2．4％，结果表明，该方法性能良好，在各种传感器阵列的信号处理方面有广泛的应用前景。     

主成分—人工神经网络在近红外光谱定量分析中的应用

近红外光谱的主成分由非线性迭代偏最小二乘法（ＮＩＰＡＬＳ）求出,主成分作标准化处理后,作为Ｂ－Ｐ神经网络的输入结点进行非线性迭代。该法的优点是,充分利用了全光谱的数据,得到消除噪声后的最佳主成分,能建立非线性模型,Ｂ－Ｐ神经网络迭代时间显著缩短,用该法对大麦中的淀粉含量进行了定量分析研究。结果为：校准和预测的相关系数分别为０．９８１和０．９５３,校准和预测的相对标准偏差分别为１．７０％和２．４８％     

HELP法在中草药分析中的应用研究

基于直以推导式演进特征投影（ＨＥＬＰ）法,对冬虫夏草子座和虫体分别进行了多组分同时定性定量测定,结果表明,ＨＥＬＰ法能减少样本提取分离的步骤,降低色谱分离条件的要求,提高检测准确度,联用色谱检测与化学计量学解析法相结合将为复杂中草药分析提供一种全新手段。     

紫外可见吸收光谱结合主成分-反向传播人工神经网络鉴别真假蜂蜜

研究紫外-可见吸收光谱技术结合化学计量学方法鉴别真假蜂蜜.根据蜂蜜中果糖和葡萄糖的典型质量比1.2:1.0,配制与真蜂蜜相近的掺假溶液,并以5%～20%的比例掺入真蜂蜜中.获取纯正蜂蜜和掺假蜂蜜的紫外-可见吸收光谱,选择最佳敏感波段250～400 mn的吸光度值进行主成分分析(PCA),优选主成分作为反向传播人工神经网...     

人工神经网络在环境监测中的应用近况

综述了近年来国内人工神经网络在环境监测中的应用进展,内容包括在化学、分光光度、X射线荧光、色谱以及其他方面的应用,引用文献29篇。     

氨基酸结构描述子矢量VHSE及其在肽QSAR中的应用

从20种天然氨基酸的50个物化性质出发，按照疏水、立体和电性特征将其分类后分别进行主成分分析，并将产生的得分矢量即VHSE(principal component score vector of hydrophilicity,steric，and electronic properties)作为氨基酸结构描述子用于肽的定量构效关系研究。与已有方法相比，VHSE描述子具有物化意义明确、结果更易解释等特点。应用该描述子并结合逐步回归变量筛选和偏最小二乘建模方法，在对苦味二肽和血管舒缓激肽促进剂等体系的定量构效关系研究中，均取得了优于已有文献的结果。     

人工神经网络研究进展及其在光谱分析中的应用

介绍最常用的人工神经网络——BP神经网络的原理、结构及其研究进展,评述了人工神经网络在光谱分析中的应用。     

主成分-线性判别法对大气易挥发性有机化合物的预警

应用遥感傅里叶变换红外光谱,采用主成分提取-线性判别分析(PCA-LDA)技术,对丙酮、二氯甲烷、甲苯、苯、氯仿和甲醇等六组分的任意混合体系进行定性鉴别。被选用的这6种大气有毒有机化合物的红外光谱图相互间存在着严重的混叠,并和反向传播人工神经网络(BP-ANN)的预测结果进行了比较。PCA-LDA的鉴别判对率达92.2%,识别率94.4%,误判率7.8%;BP-ANN分别为91.1%、95.6%和8.9%。结果表明PCA处理克服了LDA对多变量数据预测的局限性,预测性能和BP-ANN相当。鉴于BP-ANN计算耗时和繁琐,PCA-LDA模型被确定为建立VOCs预警模型最适当的方法。     

电子鼻结合PCA-BP神经网络在烟用香料质量控制中的应用

本文通过电子鼻对于不同批次的烟用料液进行分析,应用主成分分析(PCA)和误差反向传播(BP)人工神经网络分析烟用料液,并建立了基于PCA-BP神经网络的烟用香精的质量控制模型。结果表明,PCA-BP神经网络模型经过学习后,能够准确的实现烟用香精香料的质量控制。     

Chemometric analysis for optimizing derivatization in gas chromatography‐based procedures

This paper focuses on the application of principal component analysis (PCA) to facilitate the optimization of the derivatization of oestrogenic steroids—estrone, 17β‐estradiol, estriol, 17α‐ethinylestradiol and diethylstilbestrol—in order to achieve (1) the complete derivatization of all the hydroxyl groups contained in the structure of the compounds and (2) the greatest effectiveness of this reaction. Six different derivatization reagents were used in this study, whereas 2‐methyl‐anthracene was applied as the internal standard to evaluate the effectiveness of the reactions. The experimental data were subjected to PCA. With PCA, the dimensionality of the original multivariable data set could be reduced and the selection of optimum conditions for derivatization facilitated. The mixture of 99% N,O‐bis(trimethylsilyl)trifluoroacetamide + 1% trimethylchlorosilane and pyridine (1:1, v/v) at 60 °C for 30 min has been established as the most convenient and efficient means of derivatizing the aforementioned oestrogenic steroids and diethylstilbestrol; the N‐methyl‐N‐(trimethylsilyl)trifluoroacetamide + pyridine (1:1, v/v) mixture seems to be a promising alternative. The application of PCA for optimizing the derivatization procedure, proposed for the first time in this study, is particularly useful in the development of multicomponent methods across several chemical classes of compounds. Copyright © 2011 John Wiley & Sons, Ltd.     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

基于广义回归神经网络的微生物降解效应对助燃剂检测的影响

为探究火场土壤载体中微生物降解效应对助燃剂鉴定的影响，在普通土和培养土两种土样上注射助燃剂，以密封存放时间为变量，通过静态顶空的样品预处理方式对样品内的助燃剂残留物进行气相色谱-质谱法（GC-MS）鉴定。研究发现，微生物降解效应会改变样品内助燃剂组分，不同土样内降解结果有所不同，普通土样的降解效应较培养土样明显，C9~C12直链烷烃和单取代芳香烃更易被降解，多取代芳烃的降解难度随取代基含量的增多而增加。按土样种类采用主成分分析（PCA）的方式进行数据降维后，采用广义回归神经网络（GRNN）对不同土样结果区分，准确率达100%。     

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin

Abstract

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.     

Fisher判别在气相色谱-质谱分析助燃剂燃烧残留物中的应用

为寻找一种用于火场助燃剂燃烧残留物鉴定的更为准确、有效的模式识别方法,对7种常见助燃剂在不同载体上的燃烧残留物样品及未知送检样品进行气相色谱-质谱（GC-MS）分析测试,通过特征组分分析鉴定出未知样品中含有汽油成分。同时运用Fisher判别及PCA（主成分分析）/Fisher判别联用两种判别方法对样本数据进行了分析处理,PCA/Fisher判别联用的结果表明送检样本中含有硝基油漆稀料成分,而仅使用Fisher判别的结果表明送检样本中含有93^#汽油。通过将两种分析方法所得结果与GC-MS特征组分分析的结果进行比对发现,Fisher判别能够对7种助燃剂燃烧残留物的样本实现更有效的分类,对未知样本的判别更为有效。该研究结果为火场助燃剂鉴定提供了新的数据分析手段。     

Thermal decomposition of methylxanthines

The thermal decomposition of theophylline, theobromine, caffeine, diprophylline and aminophylline were evaluated by calorimetrical, thermoanalytical and computational methods. Calorimetrical studies have been performed with aid of a heat flux Mettler Toledo DSC system. 10 mg samples were encapsulated in a 40 μL flat-bottomed aluminium pans. Measurements in the temperature range form 20 to 400°C were carried out at a heating rate of 10 and 20°C min⁻¹ under an air stream. It has been established that the values of melting points, heat of transitions and enthalpy for methylxanthines under study varied with the increasing of heating rate. Thermoanalytical studies have been followed by using of a derivatograph. 50, 100 and 200 mg samples of the studied compounds were heated in a static air atmosphere at a heating rate of 3, 5, 10 and 15°C min⁻¹ up to the final temperature of 800°C. By DTA, TG and DTG methods the influence of heating rate and sample size on thermal destruction of the studied methylxanthines has been determined. For chemometric evaluation of thermoanalytical results the principal component analysis (PCA) was applied. This method revealed that first of all the heating rate influences on the results of thermal decomposition. The most advantageous results can be obtained taking into account sample masses and heating rates located in the central part of the two-dimensional PCA graph. As a result, similar data could be obtained for 100 mg samples heated at 10°C·min⁻¹ and for 200 mg samples heated at 5°C min⁻¹.     

高效液相色谱指纹图谱法分析南方红豆杉药材的氯仿提取物

建立了南方红豆杉药材氯仿提取物的高效液相色谱(HPLC)指纹图谱分析方法。采用Eurospher 100 C18色谱柱(250 mm×4 mm, 5 μm),以甲醇和水为流动相进行梯度洗脱,流速为1 mL/min,检测波长为232 nm,柱温为30 ℃。以10-脱乙酰巴卡亭III(10-DABIII)为参照物,在相同的色谱条件下测定了10批不同产地的南方红豆杉药材氯仿提取物的指纹图谱,获得了11个共有指纹峰,并利用主成分分析法(PCA)对指纹图谱进行统计分析。结果表明南方红豆杉药材的质量与种植区域有关。该方法稳定、可靠,可用于南方红豆杉药材的质量控制。     

稻米代谢组学分析方法的建立及在产地溯源中的应用

采用气相色谱-串联四极杆飞行时间质谱(GC/Q-TOF MS)建立了稻米的代谢组学分析方法,并将其应用于产地溯源。利用D-最优设计对提取溶剂进行优化,通过偏最小二乘分析法考察了不同溶剂对代谢物提取效率的影响。考察了衍生试剂种类、衍生温度及时间对代谢物检测的影响,最终确定分析条件为:甲醇-水(1∶1)为提取溶剂,N-甲基-N-三甲基硅基三氟乙酰胺-三甲基氯硅烷(MSTFA-TMCS,99∶1)于60℃条件下衍生1 h。选取10种代谢物考察仪器精密度、方法重现性和衍生产物稳定性,相对标准偏差(RSD)均小于9%,证明了方法的可靠性。结合Mass Profiler Professional(MPP)软件,以主成分分析(PCA)法区分黑龙江省4个地区的29个绥粳4号稻米样本,分类效果显著。对显著变化(P0.05,倍率变化≥2)的差异性代谢物进行鉴定,确定了11种特征标记物。结果表明,利用GC/Q-TOF MS建立代谢组学分析方法,结合主成分分析鉴别稻米产地具有可行性,为大米市场的规范和资源开发利用提供了依据。     

Determination of the Mo surface environment of Mo/TiO2 catalysts by EXAFS,XANES and PCA

The local structure of oxidic Mo/TiO₂ catalysts (0.5 to 13.5 wt.% Mo) has been studied using EXAFS and XANES. Both EXAFS and XANES results suggest that the Mo surface phase is octahedrally coordinated for all Mo loadings. The EXAFS results were also examined using principal component analysis (PCA) to determine the number of Mo species present on the Mo/TiO₂ catalysts. Results from PCA of the Mo EXAFS spectra suggested the presence of three Mo species: two surface species and bulk MoO₃.