小波包压缩-RBF网络同时测定润滑油中铁、铜、锌

将小波包压缩-RBF网络方法用于润滑油中铁铜锌三组分分光光度同时测定该方法采用小波包函数对光谱数据进行压缩处理,用较大的小波色系数构成新的校正集和预测集代替原始的校正集和预测集,然后用RBF网络进行数据解析。研究表明,用bior2．4小波包处理原始测定数据,最佳小波基用logenerge熵标准,选择适当阈值将变量数由46个压缩成25个（压缩比为0．54）,整体预测效果最好。将此方法用于合成样预测．预测结果与实际浓度的相对误差绝对值在2．50％～9．40％之间;用于实际润滑油样品中铁、铜和锌的同时测定,解析值与原子吸收法（AAS）的测定值的相对误差绝对值作3．55％～7．86％之间。应用结果令人满意。     

小波包分析-减法聚类-RBF网络同时测定痕量铁、锰

提出一种解析分光光度吲时测定数据的小波色分析-减法聚类-RBF刚络新方法。该方法用小波包分析处理吸收光潜数据,滤除信号中的噪声;采用基于山峰函数的减法聚类算法,按照自适应聚类的结果构成预测各未知样的校正集,实现校准模型的优化;由此,使RBFM络在对未知样预测时能更有效地提取光潜数据中的特征信息,提高预测结果的准确性把该算法用于模拟汽油中铁、锰合成样预测,计算表明,该方法可以显著降低测定结果的相对误差,预测结果令人满意。     

三线性直接分解法分析高维灰色体系

对于由多个两维测量数据组成的三维阵，本文提出一种新三线性直接分解方法。采用高维ＰＣＡ分解，从三维阵中直接提取抽象光谱和抽象浓度，再结合ＱＺ算法，唯一地确定混合物中各组分光谱的浓度。该方法可以排除其它未知组分的干扰，适用于高维灰色体系定性定量分析和多点校准。用模拟数据讨论了光谱分离度对该方法的影响，应用于混合维生素Ｂ１、Ｂ２和Ｂ６的荧光分析，求得的光谱和浓度与实验值吻合很好。     

偏最小二乘—分光光度法同时测定邻硝基苯酚,间硝基苯酚和对硝基苯酚

１引言同分异构体由于具有相似的结构，在多数情况下，其紫外－可见光谱严重重叠，影响其同时测定。偏最小二乘法（ＰＬＳ）通过对已知标准混合物测量，利用数学模型，借助于矩阵的正交分解途径，将浓度矩阵与量测矩阵分解为一列正交阵与行正交阵之积，具有较强的数据解析功能，可避免矩阵示逆困难，消除组分间的相互作用。本文将ＰＬＳ法用于吸收峰完全重叠的邻硝基苯酚，间硝基苯酚和对硝基苯酚三组分体系分析，结果表明，本法准确可靠，操作简便。２实验部分２．１仪器和试剂ＵＶ－２５０型紫外可见分光光计（日本岛津）。ＡＳＴ－４８６…     

荧光光度法同时测定邻苯二酚、间苯二酚与对苯二酚

将一种直接信号校正(DOSC)-小波包变换(WPT)-偏最小二乘法(PLS)(DOSC-WPT-PLS)新方法用于解析荧光光谱严重重叠的邻苯二酚?间苯二酚和对苯二酚混合物,并对其进行测定。该法将DOSC、WPT及PLS 3种方法结合从而提高了获取特征信息的能力和回归质量。DOSC方法用于除去与浓度无关的结构噪音。利用WPT的时域和频域局部化的特点改进了除噪质量和数据压缩及信息提取能力。PLS方法用于多变量校准和噪音消除。处理该3种组分的荧光光谱数据,并实现了3种化合物的同时测定。设计了PDOSCWPTPLS程序执行相关计算,并对以上3种化学计量学方法进行了比较,其总体相对预测标准偏差分别为4.3%、7.7%、11.5%,结果表明DOSC-WPT-PLS法优于WPT-PLS法和PLS法。将该法用于测定自来水中邻苯二酚?间苯二酚和对苯二酚的含量,其回收率分别为99%~110%?95%~108%和98%~104%,结果满意。     

线性规划—导数分光光度法同时测定钴,镍,铜,锌,铁的研究

1 引言 近年来,随着计算机的普及和发展,用化学计量学方法来解析光谱数据,实现毋需分离的多组分同时测定的报道迅速增加,但一般仅限于用来解析基本光谱数据。本文提出用线性规划算法解析一阶导数光谱数据的新方法,对以PAR为显色剂的Co、Ni、Cu、Zn、Fe五组分混合体系的同时测定进行了研究,结果表明,该法既保持了导数分光光度法的高灵敏度,又利用了线性规划算法解决了普通导数光谱难以解析的5组分同时测定问题,对标准合成样品进行了测定,结果令人满意。     

完全重叠色波二维谱的解析新方法

提出一种新的完全重叠色波二维谱解析方法。对于两组分色谱－光谱数据阵，只要在重叠区中存在单组分区，就能解得各组分的纯色谱和纯光谱，这种方法将求解纯谱问题转化为优化问题，再用数值遗传算法寻找最优解．本法用于２，４－二硝基苯酚和３，５－二硝基苯甲酸的合成实验以及四环素的杂质分析都得到满意的结果．     

化学计量学方法在动力学过程分析中的应用

提出了一种公因子分析的新方法。通过这一方法，对一个动力学体系，我们能够从两个不同的波长范围或反应时间范围的数据阵中获得共同组分的数目和它们的光谱或浓度曲线。将这一方法结合正交投影（OPR）技术，成功地应用于聚苯胺掺杂质子酸反应体系数据阵的解析，获得了这一过程中聚苯胺本征态及掺杂后两个新生结构的光谱和浓度变化曲线。基于解析结果可以推断：聚苯胺质子酸掺杂可能生成PANHn^n （α）、PANHn^a (β）两个不同结构的产物，他们间可相互转化。     

PLS-ANN算法-NIR光谱非破坏性Norvasc药物有效成分的定量分析

采用偏最小二乘(PLS)结合人工神经网络(ANN)算法解析Norvasc(络活喜)药片的近红外(NIR)漫反射光谱, 实现了对其中有效成分苯磺酸氨氯地平的非破坏定量测定. 设计了最佳的PLS-ANN模型, 分别讨论了最佳波长范围、 导数光谱及输入层和隐含层节点数对预测结果的影响. 以HPLC法的测定结果作标准, 苯磺酸氨氯地平浓度预测值的相对误差RE＜3.5%, 该方法可用于Norvasc药品实际生产中的质量控制.     

利用目标试验因子分析法确定化学反应的级数及速率常数

利用目标试验因子分析(TTFA)结合数值遗传算法(NGA).解析反应过程中在线测得的动力学谱-光谱数据矩阵,可在未知各组分纯光谱及动力学模型情况下同时求解出各组分的纯光谱、反应级数及速率常数。提出用近似计算法计算各组分的动力学谱,使该方法能适用于任意反应级数的体系。针对两步连续反应模型,对反应物、中间体和最终产物均有吸收及某一种组分没有吸收的体系的模拟实验数据矩阵进行了处理,表明该方法均能适用。利用该方法对邻苯二甲酸二甲酯在碱性介质中的水解反应及日落黄水溶液的电解降解反应过程中测得的数据矩阵进行解析,均获得了可靠结果。     

近红外光谱测定人参与西洋参的主要皂甙总量

采用近红外光谱测定人参与西洋参的主要皂甙总量.采集人参与西洋参的漫反射光谱,分别对光谱进行正交信号校正(OSC)与常规预处理,建立了对应的偏最小二乘(PLS)回归模型.与常规最优预处理方法相比,OSC能很好地消除人参与西洋参的品种差异,显著提高了光谱与皂甙含量的相关系数,同时降低了PLS建模因子数,提高了模型的稳健性与...     

Simultaneous spectrophotometric determination of trace amounts of uranium, thorium, and zirconium using the partial least squares method after their preconcentration by α-benzoin oxime modified Amberlite XAD-2000 resin

A new solid phase extraction method for separation and preconcentration of trace amounts of uranium, thorium, and zirconium in water samples is proposed. The procedure is based on the adsorption of U(VI), Th(IV) and Zr(IV) ions on a column of Amberlite XAD-2000 resin loaded with α-benzoin oxime prior to their simultaneous spectrophotometric determination with Arsenazo III using orthogonal signal correction partial least squares method. The enrichment factor for preconcentration of uranium, thorium, and zirconium was found to be 100. The detection limits for U(VI), Th(IV) and Zr(IV) were 0.50, 0.54, and 0.48 μg L⁻¹, respectively. The precision of the method, evaluated as the relative standard deviation obtained by analyzing a series of 10 replicates, was below 4% for all elements. The practical applicability of the developed sorbent was examined using synthetic seawater, natural waters and ceramic samples.     

On-line gel permeation chromatography-attenuated total reflectance-Fourier transform infrared determination of lecithin and soybean oil in dietary supplements

Gel permeation chromatography (GPC) with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectrometry detection has been proposed for the simultaneous determination of lecithin and soybean oil in dietary supplements. The method involves the extraction of analytes with dichloromethane in an ultrasound water bath and the injection of 2 ml of centrifuged and filtered extracts into the system integrated by two Envirogel GPC columns (19 mm x150 mm, 19 mm x 300 mm) coupled on-line. Dichloromethane was used as mobile phase. A method has been developed to select the most appropriated wavenumber to be used for the determination of each considered compound from the calculation of a factor which maximizes the analyte signal minimizing the interferent contributions, being selected the detection wavenumbers of 1034 and 1138 cm(-1) for lecithin and soybean oil, respectively in the first order derivative ATR-FTIR spectra. The method provides limits of detection of 2 and 4 mg ml(-1) for lecithin and soybean oil and repeatability values, measured as relative standard deviation, of 2.5% and 3.4% being extended the linear range till 100 mg ml(-1) for lecithin and up to 50 mg ml(-1) for soybean oil. Accurate results were found for 10 synthetic samples and 7 commercial dietary supplement preparations.     

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.     

Application of orthogonal space regression to calibration transfer without standards

To transfer a calibration model in cases where the standardization samples are rare or unstable, a method based on orthogonal space regression (OSR) is proposed. It uses virtual standardization spectra to account for response changes between instruments or batches. A comparative study of the proposed OSR, piecewise direct standardization, finite impulse response, orthogonal signal correction, and model updating (MU) was conducted on both pharmaceutical tablet data and chlorogenic acid data. The results of these studies suggest that both the OSR and the MU are superior to the other transfer techniques in terms of root‐mean‐squared error of prediction and ratio of performance to interquartile distance. Moreover, OSR requires no identical standard samples, and it avoids re‐optimizing the transfer models. In conclusion, both the differences among spectra measured on different spectrometers and the differences between different batches can be corrected successfully using the OSR method. Copyright © 2013 John Wiley & Sons, Ltd.     

Simultaneous determination of five synthetic antioxidants in edible vegetable oil by GC–MS

A simple, quick and nontoxic analytical method for the simultaneous determination of five synthetic antioxidants [t-butyl-4-hydroxyanisole (BHA), 2,6-di-t-butyl-hydroxytoluene (BHT), t-butyl hydroquinone (TBHQ), ethoxyquin (EQ) and 2,6-di-tert-butyl-4-hydroxymethyl-phenol (Ionox 100)] in edible vegetable oil has been developed. The analytes were extracted by ethanol, then separated and detected by GC–MS. Extraction conditions such as volume of ethanol required, mixing time and number of extractions were investigated and optimized by an orthogonal array experimental design. The five compounds behaved linearly in the 0.100∼20.0 mg/L concentration range, and the limits of detection (LOD) for BHA, BHT, TBHQ, EQ and Ionox-100 were 1.00, 0.92, 11.5, 0.83 and 1.39 μg/L, respectively. The recoveries at the tested concentrations of 1.00, 20.0 and 100 mg/kg were 75.6∼123%, with coefficients of variation <10.0%. The proposed procedure was successfully applied to the simultaneous analysis of the five antioxidants in soybean oil, tea oil, edible blended oil, rap oil, peanut oil, peanut blended oil and sesame oil samples purchased from local supermarkets.     

Gas-chromatographic fatty-acid fingerprints and partial least squares modeling as a basis for the simultaneous determination of edible oil mixtures

Partial least squares modeling and gas-chromatographic fatty-acid fingerprints are reported as a method for the simultaneous determination of cottonseed, olive, soybean and sunflower edible oil mixtures. In this work, two sets of three- and four-component combinations of oils were prepared, hydrolyzed and the obtained free fatty acids analyzed by gas chromatography (GC) without any further derivatization. The normalized percentages of the myristic (14:0), palmitic (16:0), palmitoleic (16:1), stearic (18:0), oleic (18:1), linoleic (18:2) and linolenic (18:3) acids were chromatographically measured in samples and used for constructing calibration matrix. The cross-validation method was used to select the number of factors and the proposed methods were validated by using two sets of synthetic oil mixture samples. The relative standard error for each oil in mixture samples was less than 10%. This approach allows determining possible adulteration in each of the four edible oils.     

An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS

Two orthogonal signal correction methods (OSC and DOSC) were applied on a set of 83 roasted coffee NIR spectra from varied origins and varieties in order to remove information unrelated to a specific chemical response (caffeine), which was selected due to its high discriminant ability to differentiate between arabica and robusta coffee varieties. These corrected NIR spectra, as well as raw NIR spectra and three chemical quantities (caffeine, chlorogenic acids and total acidity), were used to develop separate classification models accordingly using the potential functions method as a class-modelling technique in order to evaluate their respective capacities to discriminate between coffee varieties and the influence of these pre-processing methods on the classification of the coffee samples into their corresponding variety class. The transformation of roasted coffee NIR spectra by means of an orthogonal signal correction method, taking into account in this correction a chemical response closely related to the sample origin, prompted a notable improvement in the specificity of the constructed classification models.     

An emphatic orthogonal signal correction-support vector machine method for the classification of tissue sections of endometrial carcinoma by near infrared spectroscopy

A new application of emphatic orthogonal signal correction (EOSC) for baseline correction of near infrared spectra from reflectance measurements of tissue sections is introduced. EOSC was evaluated and compared with principal component orthogonal signal correction (PC-OSC) by using support vector machine (SVM) classifiers. In addition, some exemplary synthetic data sets were created to characterize EOSC coupled to SVM for classification. Orthogonal experimental design coupled with analysis of variance (ANOVA) was used to determine the significant parameters for optimization, which were the OSC method and number of components for the model. EOSC combined with the SVM gave better predictions with respect to a larger number of components and was not as susceptible to overfitting the data as the classifier built with PC-OSC data. These results were supported by simulations using synthetic data sets. EOSC is a softer signal correction approach that retains more signal variance which was exploited by the SVM. Classification rates of 93 ± 1% were obtained without orthogonal signal correction with the SVM. PC-OSC and EOSC data gave similar peak prediction accuracies of 94 ± 1%. The key advantages demonstrated by EOSC were its resistance to overfitting, fine-tuning capability or softness, and the retention of spectral features after signal correction.     

Influence of the procedure used to prepare the calibration sample set on the performance of near infrared spectroscopy in quantitative pharmaceutical analyses

Calibrating near infrared diffuse reflectance spectroscopy (NIRS) methods usually involves preparing a set of samples with a view to expanding the analyte concentration range spanned by production samples. In this work, the performances of the two procedures most frequently used for this purpose in near infrared pharmaceutical analysis, viz., synthetic samples obtained by weighing of the pure constituents of the pharmaceutical and doped samples made by under- or overdosing previously powdered production samples, were compared. Both procedures were found to provide similar results in the quantification of the active compound in the pharmaceutical, which was determined with a relative standard error of prediction (RSEP) of < 1.6%. However, the two types of sample preparation provide different spectra, which precludes the accurate quantification of synthetic samples from calibrations obtained with doped samples and vice versa. None of the mathematical pre-treatments tested with a view to reducing this different scattering (viz., second derivative, standard normal variate and orthogonal signal correction) could effectively solve this problem. This hinders accurate validation of the linearity of the procedure and makes it advisable to use doped samples which are markedly less different to production samples.