单硝基甲苯和多硝基甲苯偶极矩的理论计算和实验测定

用 MINDO/3和 CNDO/2法对单硝基甲苯和多硝基甲苯的平衡几何构型进行了全优化和 SCF 计算,求得了o,m,p-MNT,2,3-,2,4-,2,5-,2,6-,3,4-,3,5-DNT 和2,3,4-,2,3,5-,2,3,6-,2,4,5-,2,4,6-以及3,4,5-TNT 的“气相”偶极矩。用折射法实测了 MNT 和 DNT 共9种化合物在 CCl_4溶液中的偶极矩。计算值和实测值之间存在良好的线性关系。以甲苯和硝基苯的计算值通过“向量加和”近似地估算了 MNT,DNT 和 TNT的偶极矩,估算值与上述实验值和量子化学计算值之间也线性相关。     

胃肠中硝基甲苯类化合物气相色谱测定法

水中的硝基甲苯类化合物TNT,MNT,DNT的气相色谱测定方法已有介绍,但对胄肠样品的前处理和色谱分析尚未见报道。本文提出了胄肠中硝基甲苯类化合物的样品前处理和色谱分析方法。 实验部分 1.仪器:GC-7A.(日本岛津公司);800型离心沉淀器。 2.试剂:2,4,6-三硝基甲苯(TNT),军工品;2,4-二硝基甲苯(DNT)和硝基甲苯(MNT)均为CP级;石油醚(PE),沸点60～90℃;碳酸氢钠缓冲液(pH=10);二苯胺(DBA)内标液(4mg/ml)。     

高效液相色谱法测定火药中NG，α-TNT，2，6-DNT，2，4-DNT及C2

硝化甘油(NG)、二硝基甲苯(2,6-DNT和2,4-DNT)、三硝基甲苯(α-TNT)及二甲基二苯脲(C)是火药中最常见的组分。这些组分可用气相色谱法(GC)进行分离测定,但因NG的热稳定性较差,结果的精确度较差。对上述组分较有效的分析方法是高效液相色谱法(HPLC),因此法一般是在室温下进行,可避免NG等爆炸性组分的热分解。 HPLC分析火药组分在国内外早有报道。但国内做的工作不多。近年来我院开始应用HPLC分析火炸药组分。     

分解反应自催化性质快速鉴别的实验方法

许多物质的分解都具有自催化特性,常用的自催化鉴别方法是利用差示扫描量热仪(DSC)、微量量热仪(C80)等进行等温实验判定(简称"等温法").但等温法的温度选择较为困难,因此很有必要从实验角度找到一种简便有效的自催化鉴定方法.本文基于Roduit理论模拟的结果,从实验角度提出了分解反应自催化特性的判定方法(简称"中断回扫法"),并利用该法以及等温法对4种样品(硝酸异辛酯(EHN)、2,4-二硝基甲苯(2,4-DNT)、过氧化二异丙苯(DCP)以及过氧化氢异丙苯(CHP))的分解特性进行判定.结果表明:EHN以及DCP的分解符合n级分解规律,而2,4-DNT以及CHP的分解符合自催化分解规律;中断回扫法可以快速、有效地用于鉴别物质分解是否具有自催化特性.     

基于最小二乘支持向量机的色谱指纹图谱预测银杏叶总抗氧化活性

在色谱图基线校正和色谱峰匹配基础上,提出以40个银杏叶提取物HPLC指纹图谱的色谱图轮廓作为输入,相应的提取物总抗氧化活性作为输出,建立最小二乘支持向量机回归模型,并对包含10个样本的测试集进行了预测.最小二乘支持向量机的测试集预测误差均方根(RMSEP)为0.0230,预测结果优于目前普遍使用的误差反向传播神经网络和偏最小二乘回归.与采用色谱峰面积为分析变量的模型预测结果比较表明:采用消除干扰后的色谱图全谱轮廓保留了样本的全部信息,预测结果更好     

近红外光谱法同时测定通天口服液中天麻素和芍药苷的含量

本文应用近红外光谱结合偏最小二乘法建立了同时测定通天口服液中天麻素与芍药苷含量的方法。以高效液相色谱(HPLC)法测定通天口服液样品中天麻素和芍药苷的化学参考值,随机抽取60个样本作校正集,20个样本作预测集。用偏最小二乘法(PLS)将校正集样本的近红外光谱与相应样本的天麻素和芍药苷含量分别相关联建立模型。结果表明,天麻素和芍药苷校正模型的决定系数分别为96.28%、94.55%,模型的交叉验证均方差分别为0.0336、0.00908,预测集的决定系数分别为94.23%、92.86%,预测集均方差分别为0.0453、0.00839。同时还做了模型的精密度实验,该方法能用于大批量样品的快速分析。     

紫外光谱结合偏最小二乘法用于水样中化学需氧量的测定

结合紫外光谱法和化学计量学方法建立了水样中化学需氧量(COD)的测定方法。采集西宁市湟水河西钢桥至小峡段共42个样品,在190～400 nm波长范围内测定各样品的紫外光谱。用Monte Carlo采样法抽取70%的样本构成训练集,其余样本为独立测试集。分别考察了3种光谱预处理方法和4种特征筛选方法,最终确定COD分析方法为:采用SG-smoothing对光谱平滑后用无信息变量剔除法(UVE)筛选特征波长变量,最后用偏最小二乘法建立多元校正模型。结果在独立测试集上COD的预测误差均方根(RMSEP)为0. 8084,相关系数为0. 9924。紫外光谱法结合化学计量学方法可作为水样中COD测定的一种简洁、有用、快速的方法。     

分解反应自催化性质快速鉴别的实验方法

许多物质的分解都具有自催化特性,常用的自催化鉴别方法是利用差示扫描量热仪（DSC）、微量量热仪（C80）等进行等温实验判定（简称“等温法”）. 但等温法的温度选择较为困难,因此很有必要从实验角度找到一种简便有效的自催化鉴定方法. 本文基于Roduit理论模拟的结果,从实验角度提出了分解反应自催化特性的判定方法（简称“中断回扫法”）,并利用该法以及等温法对4种样品（硝酸异辛酯（EHN）、2,4-二硝基甲苯（2,4-DNT）、过氧化二异丙苯（DCP）以及过氧化氢异丙苯（CHP））的分解特性进行判定. 结果表明：EHN以及DCP的分解符合n级分解规律,而2,4-DNT以及CHP的分解符合自催化分解规律;中断回扫法可以快速、有效地用于鉴别物质分解是否具有自催化特性.     

四氧化三铁磁性纳米颗粒人工模拟酶在快速检测二硝基甲苯中的研究及应用

通过共沉淀法制备了具有类过氧化物酶催化活性的四氧化三铁磁性纳米颗粒(Fe3O4 MNPs).研究表明,Fe3O4 MNPs可以催化H2O2氧化2,2'-连氮基-双-(3-乙基苯并二氢噻唑啉-6-磺酸)(ABTS)二铵盐,生成有色的氧化态ABTS,同时也可催化H2O2氧化二硝基甲苯(DNT)的反应,消耗反应物H2O2.基于上述原理,构建了Fe3O4 MNPs-ABTS-H2O2-DNT反应体系,用于检测DNT.结果表明,氧化态ABTS在417 nm处的吸收值随着DNT含量的增加而降低,与DNT浓度在5×10-7 ～2.0×10-5 mol/L范围内呈良好的线性关系,检测限为1.5 ×10-7 mol/L(S/N=3).采用紫外-可见分光光度法对Fe3O4 MNPs-ABTS-H2O2-DNT体系的反应机理、反应条件等进行了深入探讨.所建立的硝基苯类化合物含量的比色分析方法简便、快速、灵敏、可靠,对环境水样中硝基苯类化合物的在线监测具有潜在的应用价值.     

分散液-液微萃取与气相色谱-质谱联用分析环境水样中痕量的2,4-二硝基甲苯和磷酸三(2-氯乙基)酯

建立了分散液-液微萃取与气相色谱-质谱联用同时测定环境水样中痕量2,4-二硝基甲苯和磷酸三(2-氯乙基)酯的新方法。对影响萃取效率的因素进行了详细的考察和优化,确定采用的最佳萃取条件为: 将0.8 mL乙醇和60 μL氯仿的混合溶液快速注入5.0 mL的样品溶液中,振动混匀120 s后,离心分离,吸取沉积在试管底部的氯仿相直接进样分析。该方法对磷酸三(2-氯乙基)酯和2,4-二硝基甲苯的检出限(信噪比为3)分别为0.01和0.04 μg/L,富集倍数分别为96.6和127.5;两种物质的线性范围达3到4个数量级;日内和日间测定的相对标准偏差(RSDs, n=6)分别为8.6%～11.5%和8.9%～12.0%。将该方法用于环境水样中2,4-二硝基甲苯和磷酸三(2-氯乙基)酯的分析,其加标回收率为102.1%～110.9%。方法具有操作简单、方便快速、灵敏度高、无交叉污染和环境友好等优点。     

近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Simultaneous Spectrofluorimetric Determination of Paracetamol and Caffeine in Pharmaceutical Preparations in Solid‐Phase Using Partial Least Squares Multivariate Calibration

Abstract

Partial least‐squares algorithm (PLS)‐1 was used for the solid‐phase spectrofluorimetric determination of paracetamol (PA) and caffeine (CF) in pharmaceutical formulations. In despite of the closely overlapping spectral bands, the method allows the simultaneous quantification and sample preparation prior to analysis is not required. The calibration set consisted of 96 samples with 100–400 mg/g^?1 PA plus 10–65 mg/g^?1 CF; another set of 25 samples was used for external validation. Agreement between predicted and experimental concentrations was fair (r=0.993 and 0.964 for PA and CF models). Prediction performance was evaluated in terms of the coefficient of variability (CV), relative predictive determination (RPD), and ratio error range (RER). The PLS‐1 model was used for the determination of PA and CF in pharmaceutical formulations.     

Hydroxyl and acid number prediction in polyester resins by near infrared spectroscopy and artificial neural networks

Back-propagation artificial neural networks (BP-ANN) are applied for modeling hydroxyl number and acid value of a set of 62 samples of polyester resins from their near infrared (NIR) spectra. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR) and partial least squares (PLS). The set of available samples is split into: (i) a training set, for models calculation; (ii) a test set, for setting the correct number of latent variables in PCR and PLS and for selecting the end point of the training phase of BP-ANN; (iii) a “production set” of samples, which are predicted to evaluate the models predictive ability. This approach guarantees that the predictive ability of the models is evaluated by genuine predictions. BP-ANN resulted always better than the classical PCR and PLS, from the point of view of the predictive ability. The study of the breakdown number of experiments to include in the training set showed instead that this factor does influence PCR and PLS at a lesser degree than what happens for BP-ANN. The latter approach requires a larger number of experiments for obtaining good results. The choice of optimal training sets is efficiently performed by Kohonen self-organizing maps (SOMs). It can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for monitoring the polyesterification of dicarboxylic acids with diols by predicting the acid and hydroxyl numbers directly along the process line.     

Determination of nitroaromatic, nitramine, and nitrate ester explosives in soil by gas chromatography and an electron capture detector

Hazardous waste site characterization, forensic investigations, and land mine detection are scenarios where soils may be collected and analyzed for traces of nitroaromatic, nitramine, and nitrate ester explosives. These thermally labile analytes are traditionally determined by high-performance liquid chromatography (HPLC); however, commercially available deactivated injection port liners and wide-bore capillary columns have made routine analysis by gas chromatography (GC) possible. The electron-withdrawing nitro group common to each of these explosives makes the electron capture detector (ECD) suitable for determination of low concentrations of explosives in soil, water, and air. GC-ECD and HPLC-UV concentration estimates of explosives residues in field-contaminated soils from hazardous waste sites were compared, and correlation (r>0.97) was excellent between the two methods of analysis for each of the compounds most frequently detected: 2,4,6-trinitrotoluene (TNT), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), 2,4-dinitrotoluene (2,4-DNT), 1,3-dinitrobenzene (1,3-DNB), 1,3,5-trinitrobenzene (TNB), and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX). The analytes were extracted from soils with acetonitrile by 18 h of sonication in a cooled ultrasonic bath. Two soil-to-solvent ratios were evaluated: 2.00 g:10.00 ml and 25.0 g:50.0 ml. GC-ECD method detection limits were similar for the two soil-to-solvent ratios and were about 1 mug kg(-1) for the di- and trinitroaromatics, about 10 mug kg(-1) for the mono-nitroaromatics, 3 mug kg(-1) for RDX, 25 mug kg(-1) for HMX, and between 10 and 40 mug kg(-1) for the nitrate esters (nitroglycerine [NG] and pentaerythritol tetranitrate [PETN]). Spike recovery studies revealed artifacts introduced by the spiking procedure. Recoveries were low in some soils if the amount of soil spiked was large (25.0 g) compared to the volume of spike solution added (1.00 ml). Recoveries were close to 100% when 2.00-g soil samples were spiked with 1.00 ml of solution. Analytes most frequently found in soils collected near buried land mines were the microbial transformation products of TNT (2-amino-4,6-dinitrotoluene [2-Am-DNT] and 4-amino-2,6-dinitrotoluene [4-Am-DNT]), manufacturing impurities of TNT (2,4-DNT, 2,6-DNT, and 1,3-DNB), and TNT. The microbial reduction products of the isomers of DNT and of 1,3-DNB were also detected, but the ECD response to these compounds is poor.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

Partial least squares X-ray fluorescence determination of trace elements in sediments from the estuary of Nerbioi-Ibaizabal River

The feasibility of partial least squares (PLS) regression modeling of X-ray fluorescence (XRF) spectra of estuarine sediments has been evaluated as a tool for rapid trace element content monitoring. Multivariate PLS calibration models were developed to predict the concentration of Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn in sediments collected from different locations across the estuary of the Nerbioi-Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country). The study was carried out on a set of 116 sediment samples, previously lyophilized and sieved with a particle size lower than 63 μm. Sample reference data were obtained by inductively coupled plasma mass spectrometry. 34 samples were selected for building PLS models through a hierarchical cluster analysis. The remaining 82 samples were used as a test set to validate the models. Results obtained in the present study involved relative root mean square errors of prediction varying from 21%, for the determination of Pb at hundreds μg g⁻¹ level, up to 87%, for Ni determination at little tens μg g⁻¹ level. An average prediction error of ±37% for the 14 elements under study was obtained, being in all cases mean differences between predicted and reference results of the same order than the standard deviation of three replicates from a same sample. Residual predictive deviation values obtained ranged from 1.1 to 3.9.     

Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.     

Array of potentiometric sensors for simultaneous analysis of urea and potassium

Urea biosensors based on urease immobilized by crosslinking with BSA and glutharaldehyde coupled to ammonium ion-selective electrodes were included in arrays together with potassium, sodium and ammonium PVC membrane ion-selective electrodes. Multivariate calibration models based on PCR and PLS2 were built and tested for the simultaneous determination of urea and potassium. The results show that it is possible to obtain PCR and PLS2 calibration models for simultaneous determination of these two species, based on a very small set of calibration samples (nine samples). Coupling of biosensors with ion-selective electrodes in arrays of sensors raises a few problems related to the limited stability of response and unidirectional cross-talk of the biosensors, and this matter was also subjected to investigation in this work. Up to three identical urea biosensors were included in the arrays, and the data analysis procedure allowed the assessment of the relative performance of the sensors. The results show that at least two urea biosensors should be included in the array to improve urea determination. The prediction errors of the concentration of urea and potassium in the blood serum samples analyzed with this array and a PLS2 calibration model, based on nine calibration samples, were lower than 10 and 5%, respectively.     

Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.