基于拉曼光谱法所建的多元校正模型预测烟草中绿原酸和芸香苷的含量北大核心CSCD

为了满足现场批量检测的需求,基于拉曼光谱建立了多元校正模型,实现了烟草中绿原酸和芸香苷含量的预测。120个烟草样品(包含90个校正集样品和30个验证集样品)用50%(体积分数)甲醇溶液萃取后注入拉曼光谱液体池中,在325 nm激发波长下采集800~2000 cm^(-1)内的拉曼光谱,采用Savitzky-Golay卷积平滑法预处理所得原始拉曼光谱,用Monte-Carlo交互检验法选择隐变量数目,并在1555.8~1652.9 cm^(-1)波段内建立偏最小二乘法(PLS)多元校正模型,以避免绿原酸和芸香苷拉曼光谱在1600 cm^(-1)附近的光谱重叠干扰。结果显示,所建绿原酸和芸香苷模型的预测均方根误差(RMSEP)分别为0.88和0.67,预测集决定系数(R_(p)^(2))分别为0.948和0.970,说明基于拉曼光谱和PLS所建模型,可以对烟草中多酚类化合物绿原酸和芸香苷含量实现准确可靠的预测。     

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。     

基于拉曼光谱法建立的模型快速预测布洛芬缓释胶囊中布洛芬的含量北大核心CSCD

为快速测定布洛芬缓释胶囊中布洛芬的含量,进行了题示项目研究。以高效液相色谱法(HPLC)为参比,采用拉曼光谱仪对5个校正集样本和布洛芬对照品进行扫描,将原始拉曼光谱图导入计算平台,选择波段250-1800 cm^(-1),通过荧光褪色差分法校正基线,二阶滤波求导进行降噪处理,用向量夹角转换算法计算各样本与布洛芬对照品拉曼光谱之间夹角余弦的方差值,以布洛芬的质量分数对该值进行拟合,得到模型的关联方程。采集待测样品的拉曼光谱,利用建立的模型预测其中布洛芬的含量。结果显示:布洛芬的质量分数在55%~90%内与其对应的夹角余弦的方差值呈线性关系;验证集样本的预测值与HPLC所得参考值的相对偏差为3.0%~3.8%。模型用于实际样品分析,预测值和参考值的相对偏差为1.2%~6.8%,预测值的相对标准偏差(n=5)为0.99%~3.1%。     

近红外光谱测定溶液中甲醇的含量

测量了含微量甲醇(体积分数为0.04%～0.24%)的系列乙醇水溶液的近红外光谱,利用近红外光谱分析建立了预测甲醇含量的定量分析模型。比较了用外部检验法(Test Set-Validation)和交叉检验法(Cross-Validaton)建立的数学模型,研究了使用外部检验法时,校正集和检验集样品数的改变对模型预测结果的影响。结果发现,当校正集样品数为15检验集样品数为6(总样品数为21)时,使用外部检验法建立的数学模型预测结果较好,其校正集的均方根误差和检验集的预测均方根误差(分别为RMSEE和RMSEP)均较小(分别为0.0115和0.0105),而且很接近。结果表明,近红外光谱方法简单,准确而且实用。     

化学计量学方法应用于近红外光谱法测定混合炸药中TNT含量

为构建样品中的被测组分(TNT)的含量与其红外光谱之间的数学模型,从生产线上采集以及按相同方法制备了共计155个样品并采集了它们的红外光谱,根据计算所得光谱残留F值判别并剔除异常光谱。随机选取63个样品的光谱作为校正集,其余92个样品的光谱作为验证集。另外采用常规的溶剂提取-红外光谱法测定了这些样品中TNT的含量作为建模参考值。在最优模型波段(cm-1)为:9 114.1～8 331.2,7 671.6～7 189.5,6 514.5～5 666,5 102.8～4 929.3,4 744.14～4 728.71的条件下,根据校正集的光谱数据,用偏最小二乘法建立数字模型。通过交叉检验均方根误差,RMSECV-维数曲线的理想程度以及光谱主成分分析结果选取了最优模型。采用χ2检验法,以及根据预测标准差和Bias值,结合验证集样品的光谱和数据,评估了方法的精确度和准确度。从TNT含量在36.68%～46.95%内的8个样品的测定结果得出其预测值的Bias值为0.078%,SEP%为0.514%。说明方法的准确度和精密度良好,且无需使用有机试剂。     

近红外光谱法快速测定溶剂型木器涂料中甲苯、乙苯和二甲苯

建立了使用近红外光谱法(NIR)快速测定溶剂型木器涂料中甲苯、乙苯和二甲苯的方法。收集涂料样品,使用气相色谱法(GC)测定苯系物含量。采用聚乙烯密实袋封装聚氨酯类、硝基类或醇酸类涂料,应用积分球透漫射采样方式采集清漆和漫射采样方式采集色漆的近红外光谱。采用偏最小二乘法,分别建立清漆和色漆的近红外光谱与苯系物线性关系模型。校正集均方差在0.43%~1.32%之间、相关系数R在0.9046~0.9766之间。验证集均方差在0.591%~1.73%之间。对未知样品预测,清漆样品预测值相对偏差<15%;色漆样品预测值相对偏差<20%。两个定量模型预测效果良好。该2个NIR定量方法适用于对溶剂型木器涂料中甲苯、乙苯和二甲苯含量进行快速测定。     

基于近红外光谱法的定量分析模型快速预测蕨菜中总黄酮的含量

采用近红外光谱法结合偏最小二乘法构建蕨菜中总黄酮含量的快速无损测定方法。取蕨菜样品140份,采用傅里叶变换近红外光谱仪采集4 000～11 500 cm^-1波段内近红外光谱,以一阶导数预处理原始光谱,设置主因子数为10,在6 100～7 500 cm^-1和5 400～6 000 cm^-1波段内建模。结果表明：校正集定量分析模型的校正均方根误差(RMSEC)为0.078,交叉验证决定系数(R²)为0.991 9;验证集定量分析模型的预测均方根误差(RMSEP)为0.125,R²为0.984 1,说明所建模型性能较优。分别以定量分析模型和紫外-可见(UV-Vis)分光光度法分析完全外部验证集样品,预测回收率(预测值和测定值比值的百分数)接近100%,说明所建模型的预测准确度较高,可用于蕨菜中总黄酮的快速、准确测定。     

校正集样品特性对鼠药中毒鼠强近红外光谱分析的影响

根据市售鼠药样品成分各异且相对复杂,建立6种不同成分体系和9个不同样本容量的校正集,运用小波变换压缩鼠药的近红外透射光谱数据,结合BP反向神经网络算法对压缩的数据进行建模,考察校正集样品特性对模型预测能力的影响。试验结果表明:采用BP神经网络算法建立定量模型时,只要校正集样品中包含了与预测样品性质相似的样本,就能准确地对复杂样品进行近红外定量分析。当校正集容量分别为72和84时,模型预测结果趋于平稳。当校正集数量为96时,模型的最大相关系数为0.959 8,预测最小标准差和平均相对误差分别为1.893%和1.92%。     

近红外光谱快速测定复方磺胺甲噁唑药效成分

基于近红外漫反射光谱技术,利用偏最小二乘多元校正方法建立了复方磺胺甲噁唑片中的两个有效成分磺胺甲噁唑(SMZ)和甲氧苄啶(TMP)含量的快速同时测定方法。对于SMZ和TMP定量分析模型,相关系数分别为99.969%与99.938%,校正集残差分别为0.217与0.159,而预测根均方差分别为0.310和0.418。该方法具有简单、快捷、两组分同时准确测定以及样品不经任何预处理等特点。     

温度对测定乙醇含量近红外模型的影响

以不同浓度的乙醇溶液为实验材料,研究了温度对近红外光谱法定量分析结果的影响.对体积百分比在5%～70%范围内的乙醇溶液,在15,18,20,25℃和28℃等5个温度点做了研究.对光谱经过不同预处理后,使用不同浓度的20个样品建立了测定乙醇含量的眦校正模型,根据模型评价参数选择了最佳模型.分析结果显示,温度在20℃时采用一阶导数(3点平滑)光谱预处理所建立的模型最佳.其相关系数为0.9986,预测均方根误差(RMSEP)和预测标准误差(SEP)分别为0.0079和0.0081.通过对模型进行t-检验,在显著性水平大于0.05的条件下,其测定结果与GC的测定结果对比,两者无显著性差异.应用于测定酒样中乙醇的含量,结果令人满意.     

Prediction of diesel fuel properties by vibrational spectroscopy using multivariate analysis

Partial least squares regression (PLS) calibration models based on Fourier transform infrared (FTIR-ATR) and Raman spectra (FT-Raman) were applied to the rapid and accurate simultaneous determination of the main properties of diesel fuels. Training sets were composed of over ninety commercial diesel fuel samples. The methods use baseline-uncorrected, raw FTIR-ATR and FT-Raman spectra. Two spectral regions were studied: full spectral region and “fingerprint” region. The models were validated using the cross-validation process. Based on the correlation coefficient and root mean square error of cross validation (RMSECV) values the both developed calibration models, PLS/FTIR-ATR and PLS/FT-Raman, were very accurate and comparable with standard testing methods. The following diesel fuel properties may be confidently estimated: cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, as well as the contents of total aromatics and polycyclic aromatic hydrocarbons. As compared to the “fingerprint” spectral region, the PLS/FTIR-ATR model using full spectral region displayed slightly better performances with the most of the correlation coefficient values above 0.98.     

蒸馏分离–电感耦合等离子体质谱法测定铜铅锌矿石中微量锗

建立蒸馏分离-电感耦合等离子体质谱法测定铜铅锌矿石中微量锗的方法。采用硝酸-磷酸混合酸消解铜铅锌矿石样品,在盐酸介质中蒸馏分离微量锗,在氦气碰撞池模式下,以103Rh为在线内标进行质谱法测定。锗的质量浓度在0~50 μg/L 范围内线性良好,相关系数为0.9995,方法检出限为0.019 μg/g。用所建方法对3个铜铅锌矿石成分国家一级标准物质进行测定,测定结果的相对标准偏差为4.58%~5.55%(n=7),样品加标回收率为93.0%~102.0%。该方法操作简便,灵敏度高,适用铜铅锌矿石中微量锗含量的测定。     

Methyl methacrylate in poly(methyl methacrylate)--validation of direct injection gas chromatography

Gas chromatography (GC) was investigated for the determination of residual methyl methacrylate (MMA) in heat-processed poly(methyl methacrylate) (PMMA) denture base material emphasizing recovery and validation. Standard solutions of MMA and emulsion-polymerized PMMA in dichloromethane were analysed, before and after distillation by a room-temperature air stream into a liquid nitrogen trap, and in the presence of PMMA by direct injection. Quantitative NMR analysis using dimethyl sulphoxide as internal calibration standard in deuterated chloroform solutions provided validation. Good concordance was observed between results under all conditions; no problems arose from direct injection of PMMA solution for GC. Good straight line responses in log-log plots were generally observed. For GC and MMA: log-log calibration curve (slope: 0.9552 +/- 0.0051, r2: 0.9992, n = 32) indicated some non-linearity (t = 8.875, p approximately 4 x 10(-10)). Distillation gave slope: 0.9751 +/- 0.0213 (NS versus unity; t = 1.172, p > 0.25). For PMMA solutions, distillation (r2: 0.9301) gave greater scatter than direct injection (r2: 0.9704). For NMR: log-log plot of calculated versus actual MMA (slope: 0.9363 +/- 0.0157, r2: 0.9969, n = 13) again indicated non-linearity (t = 4.0682, p = 0.0019). PMMA solutions gave slope: 0.9477 +/- 0.0328, r2 = 0.9858 (NS versus unity; t = 1.5941, p = 0.13). Determination of MMA in PMMA by GC is recommended.     

Determination of Basic Nitrogen in Residues of Crude Oil Distillation Using ATR-FTIR and Chemometric Methods

A method for basic nitrogen determination in residues of crude oil distillation using infrared spectroscopy and chemometrics algorithms was developed. Interval partial least squares, synergy interval partial least squares, and backward interval partial least squares were evaluated for calibration model construction. The samples were divided into a calibration and prediction set containing 40 and 15 samples, respectively. The first derivative with a Savitzky-Golay filter and the mean centered data showed the best results and were used in all calibration models. The backward interval partial least squares algorithm with spectra divided in 60 intervals and combinations of 4 intervals (1407 to 1372; 1117 to 1082; 971 to 936; 914 to 879 cm^?1) showed the best root mean square error of prediction of 0.016 wt%. This calibration model displayed a suitable correlation coefficient between reference and predicted values.     

Screening direct analysis of PAHS in atmospheric particulate matter with SPME

A direct headspace SPME method with PDMS fiber was developed for the determination of polynuclear aromatic hydrocarbons (PAHs) in atmospheric particulate matter collected in HiVol filter. The recovery obtained for PAHs lower than four congeners with the proposed method falls in the range 50-125% and DL was around 5-20 pg. The results obtained with standard reference materials (SRM 1649 and SRM 1650) for determination of PAHs showed acceptable agreement with the declared or certificated values.     

偏最小二乘紫外分光光度法同时测定丁烯二酸的顺反异构体

将偏最小二乘法用于紫外分光光度分析,在pH=1.4的磷酸溶液中,同时测定了丁烯二酸的顺、反异构体。确定了测定的最佳波长范围为190～268nm;测得23个混合标样的吸光度值用于建立模型,顺、反丁烯二酸的浓度范围为3.0～14.0mg/L和1.0～13.0mg/L。所建立的测定二者模型的相关系数分别为0.9951和0.9983;平均回收率分别为100.8%和100.7%;均方根误差(RMSE)分别为0.3667和0.2233;预测相对误差(REP)分别为5.05%和3.49%。对3个批次反丁烯二酸样品的测定结果与高效液相色谱法的测定结果进行比较,经成对t检验表明,两种方法的测定结果无显著性差异。     

Cellulose I crystallinity determination using FT–Raman spectroscopy: univariate and multivariate methods

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band intensity ratio of the 380 and 1,096 cm^?1 bands. For calibration purposes, 80.5% crystalline and 120-min milled (0% crystalline) Whatman CC31 and six cellulose mixtures produced with crystallinities in the range 10.9–64% were used. When intensity ratios were plotted against crystallinities of the calibration set samples, the plot showed a linear correlation (coefficient of determination R ² = 0.992). Average standard error calculated from replicate Raman acquisitions indicated that the cellulose Raman crystallinity model was reliable. Crystallinities of the cellulose mixtures samples were also calculated from X-ray diffractograms using the amorphous contribution subtraction (Segal) method and it was found that the Raman model was better. Additionally, using both Raman and X-ray techniques, sample crystallinities were determined from partially crystalline cellulose samples that were generated by grinding Whatman CC31 in a vibratory mill. The two techniques showed significant differences. In the second approach, successful Raman PLS regression models for crystallinity, covering the 0–80.5% range, were generated from the ten calibration set Raman spectra. Both univariate-Raman and WAXS determined crystallinities were used as references. The calibration models had strong relationships between determined and predicted crystallinity values (R ² = 0.998 and 0.984, for univariate-Raman and WAXS referenced models, respectively). Compared to WAXS, univariate-Raman referenced model was found to be better (root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values of 6.1 and 7.9% vs. 1.8 and 3.3%, respectively). It was concluded that either of the two Raman methods could be used for cellulose I crystallinity determination in cellulose samples.     

Urinary excretion study of coenzyme Q10 in rats by ultra-performance liquid chromatography-mass spectrometry

A method based on ultra-performance liquid chromatography mass spectrometry (UPLC-MS) applying atmospheric pressure chemical ionization in the positive ion mode is developed for the determination of coenzyme Q10 (CoQ10) in rat urine. The assay involves the extraction of crude urine, fast liquid chromatography on a Waters Acquity UPLC BEH C18 column (1.7 microm, 1.0 x 50 mm), and selected ion monitoring detection using mass transition. The calibration range is found to be 0.05-25 microg/mL, with the lower limit of quantitation of 0.05 microg/mL. Intra- and inter-day precision (relative standard deviation) for CoQ10 in rat urine range from 0.7% to 15%, and accuracy expressed in recovery rates in urine is between 83% and 118%. The recovery of this method is found to be between 80% and 95% at three concentrations. The total cumulative recovery of CoQ10 is 1.16 +/- 1.05% (percentage of dose intake, n = 4) from rat urine collected over 30 h after oral administration of the drug. The UPLC-MS method described allows the quick determination of CoQ10 in rat urine with good precision and accuracy. It is suitable for further excretion studies of CoQ10 in animals.     

FT-MIR and Raman spectroscopy coupled to multivariate analysis for the detection of clenbuterol in murine model

A fast and simple screening method for the determination of clenbuterol at the ppb level in a murine model was demonstrated by Mid Infrared (MIR) and Raman spectroscopy in conjunction with multivariate analysis. In order to build the calibration models to quantify clenbuterol in rat meat, mixtures of rat meat and clenbuterol were prepared in a range of 5-10,000 ppb. Partial Least Square (PLS) analysis was used to build the calibration model. The results shown that Mid Infrared and Raman spectroscopy were efficient, but Mid Infrared (R(2) = 0.966 and SEC = 0.27) were superior to Raman (R(2) = 0.914 and SEC = 1.167). The SIMCA model developed showed 100% classification rate of rat meat samples with or without clenbuterol. The results were confirmed with contaminated meat samples from animals treated with clenbuterol. Chemometric models represent an attractive option for meat quality screening without sample pretreatments which can identify veterinary medicinal products at the ppb level.     

蒸馏-催化极谱法测定复杂物料中微量砷

提出了用催化极谱法测定复杂物料中微量砷的含量。选择测定的溶液介质中含碲(Ⅳ)硫酸溶液5mL和150g.L-1碘化钾溶液5mL。仪器扫描速率为250mV.s-1,并采用二阶导数测定。试样(0.01～1.0g)用氯酸钾0.5g、氢氟酸5滴、硝酸5～10mL溶解,用蒸馏法分离其中的砷。砷的质量浓度在0.4mg·L-1以内与相应的峰电流呈线性关系。按此方法测定了12个矿样中砷含量,其测定值与已知值相符。方法的回收率在97%～100%之间,相对标准偏差(n=6)在2.3%～7.2%之间。