比值光谱导数法同时测定铝合金中铁,铜,锌

用比值光谱－导数分光光度法，在ｐＨ５．５缓冲溶液中，利用金属－２－（５－溴－２－吡啶偶氮）－５－二乙氨基酚（５－Ｂｒ－ＰＡＤＡＰ）－ＯＰ三元络合物显色体系，对混合物中铁，铜，锌三组分进行了同时测定。合成试样５次测定回收率在９７．３％－１０４．４％之间。应用于铝合金中铁，铜，锌的测定，各６次测定的ＲＳＤ分别为３．６６％，１．３８％，２．０３％。     

5—（5—硝基—2—吡啶偶氮）—2,4—二氨基甲苯与钌的显色反应及其应用

报道以５－（５－硝基－２－吡啶偶氮）－２，４－二氨基甲苯（５－ＮＯ２－ＰＡＤＡＴ＿作为测定钌的分光光度法。在４０％乙醇存在下ＰＨ４．０－６．５乙酸－乙酸钠缓冲溶液中５－ＮＯ２－ＰＡＤＡＴ与Ｒｕ（Ⅱ）形成稳定的红色络合物。该络合物的无机酸作用下，可转变为另一型人有较高吸收特性的络合物，适宜酸度范围分别为０．１２－２．０ｍｏｌ／ＬＨＣｌ，０．１２－１．２ｍｏｌ／ＬＨＣｌＯ４，０．１２－１．０ｍｏｌ／Ｌ     

5—Br—PADAP快速光度法测定钢中铌

研究了用２－（５－溴－吡啶偶氮）－５－二乙氨苯基酚（５－Ｂｒ－ＰＡＤＡＰ）测定钢中铌显色条件，所拟方法简捷实用，可测钢中０．０１０％～６．００％的铌。     

偏最小二乘催化极谱法同时测定铂,钯,铑

应用Ｍ２７３Ａ电化学系统中的线性扫描技术，确定了０．７５ｍｏｌ／ＬＨ２ＳＯ４－１．５％ＮＨ４Ｃｌ－２．８ｍｍｏｌ／Ｌ（ＣＨ２）６Ｎ４－０．００２５％Ｎ２Ｈ４．Ｈ２ＳＯ４为偏最小二乘极谱法同时测定Ｐｔ、Ｐｄ、Ｒｈ的最佳极谱体系。Ｐｔ、Ｐｄ、Ｒｈ的线性范围为３．２ｍｇ／Ｌ、０－１５．０ｍｇ／Ｌ和０－１．０ｍｇ／Ｌ。模拟样品及实际样品的回收率在９０．３－１０７．７％之间。     

偏最小二乘光度法同时测定铜和铁的研究及应用

７－（１－苯偶氮）－８－羟基喹啉－５－磺酸钠在ＰＨ＝４．７５ＨＡｃ－ＮａＡｃ缓冲溶液中能与Ｃｕ（Ⅱ）和ＦＥ（Ⅲ）形成稳定的络合物，本文研究了Ｃｕ（Ⅱ）－ＰＡＨＱＳ、Ｆｅ（Ⅲ）－ＰＡＨＱＳ体系的显色条件，以偏最小二乘法处理两者重叠吸收峰，建立了光度法同时测定铜和铁的方法。     

等吸收点—多波长线性回归—导数分光光度法同时测定重油中的铜,镍,锌

本文提出了一种同时测定多组分的新方法，即等吸收点－多波长线性回归－导数分光光度法。利用金属离子－５－Ｂｒ－ＰＡＤＡＰ－ＣＰＢ三元络合显色体素，同时测定了重油中的铜、镍、锌，相对标准偏差小于２．６％，分析结果与ＩＣＰ－ＡＥＳ及ＧＦ－ＡＡＳ法吻合。与常规分光光度法比较，灵敏度提高１０倍左右。     

双波长分光光度法测定微量铜

报道了以５－（５－硝基－２－吡啶偶氮）－２－４－二氨基甲苯（５－ＮＯ２－ＰＡ－ＤＡＴ）为显色剂,应用双峰双波长光度法测定铜（Ｉ）的新方法。实验结果表明,在ｐＨ４．０￣ｐＨ６．０的乙酸－乙酸钠缓冲溶液中和盐酸羟胺和乙醇存在下,铜（Ｉ）可与试剂形成稳定的１：２红色配合物。     

meso—四（4—氨基苯基）卟啉二阶导数光度法测定痕量锌

１引言用卟啉类显色剂测定痕量金属含量灵敏度很高，因而备受重视。但用导数光度法研究非水溶性卟啉显色剂的报道很少。非水溶性ｍｅｓｏ－四（４－氨基苯基）卟啉［Ｔ（４－ＡＰ）Ｐ］作为锌的显色剂研究尚未见报道。本文用二阶导数光度法建立了该卟琳测定痕量锌的新方法。该方法灵敏度高，选择性好，简便。用于测定水样中痕量锌，结果满意。２实验部分２．１主要仪器和试剂Ｂｅｃｋｍａｎ　ＤＵ－７Ｕ型分光光度计。锦标准溶液（用前稀释成１．００ｍｇ／Ｌ）；５．０×１０－４　ｍｏｌ／ＬＴ（４－ＡＰ）Ｐ的ＤＭＦ溶液。２．２实验方法…     

5-Br-PADAB分光光度法测定钯

研究了４－（５－溴－２－吡啶偶氮）－１,３－二氨基苯（简称５－Ｂｒ－ＰＡＤＡＢ）分光光度法测定微量钯的反应,在乙酸介质中Ｐｄ（Ⅱ）与５－Ｂｒ－ＰＡＤＡＢ生成紫红色络合物,确定了分光光度法测定钯的新体系。在实验条件下试剂的最大吸收峰位于４３８ｎｍ处,络合物的最大吸收峰位于５６４ｎｍ处,表观摩尔吸光系数ε＝５．２０×１０－４Ｌ／ｍｏｌ．ｃｍ,用等摩尔连续变化法、摩尔比法测得络合比ｎ（Ｐｄ（Ⅱ））∶ｎ（５－Ｂｒ－ＰＡＤＡＢ）＝１∶１,钯离子的量浓度在４．２７×１０－８～１．５４×１０－５ｍｏｌ／Ｌ时,服从Ｂｅｅｒ定律。试验了３０种离子在一定量下不干扰测定。     

二极管阵列检测—偏最小二乘吸光光度法同时测定铜锌

研究了以２－（５－溴－２－噻唑偶氮）５－二乙氨基苯酚（５－Ｂｒ－ＰＡＤＡＰ）作显色剂,ＣＣＤ（Ｃｈａｒｇｅ Ｃｏｕｐｌｅｄ Ｄｅｖｉｃｅ,电荷耦器件）二极管阵列检测分光光度装置采及收光谱,用偏最小二乘法解析,同时测定了粮食中铜和锌。方法的线性范围锌为０．０５～０．４０μｇ．ｍｌ＾－１,铜为０．０２～０．６０μｇ．ｍｌ＾－１;检出限铜为０．０２μｇ．ｍｌ＾－１,平均加标回收率锌为９３．３％～９５．７     

人工神经网络-伏安分析法同时测定邻、间、对二硝基苯

将反向传播算法的前馈神经网络用于导数脉冲伏安分析法同时测定邻、间、对二硝基苯。实验在盐酸－氯化钾－乙醇介质中进行，悬汞电极作为工作电极。通过对网络结构和参数的优化，加快了训练速度，提高了预测的准确度。用该法对邻、间、对二硝基苯混合物进行定量分析，预测的相对标准误差（ＳＥＰ）分别为４２６％，４９９％和４８６％。对人工神经网络（ＡＮＮ）和偏最小二乘法（ＰＬＳ）的结果进行的比较表明，ＡＮＮ法优于ＰＬＳ法。     

Simultaneous determination of cobalt and palladium in micellar media using H-point standard addition method and partial least square regression

A simple, sensitive and selective spectrophotometric method for the simultaneous determination of Co(II) and Pd(II) using partial least square (PLS) calibration and H-point standard addition method is described. The method is based on the complex formation of Co(II) and Pd(II) with 4-(2-pyridylazo) resorcinol (PAR) in acidic media and in the presence of sodium dodecyl sulfate (SDS) as a micellizing agent. Acidic media and the presence of a micellar system improve selectivity and sensitivity, respectively. By applying PLS calibration, Co(II) and Pd(II) can be determined in the range of 0.20-2.0 and 0.40-4.0 microg ml(-1), respectively. The relative errors of prediction for the determination of Co(II) and Pd(II) in the 10 prediction samples were 1.69 and 1.72%, respectively. The results of applying H-point standard addition method show that Co(II) and Pd(II) can be determined simultaneously with concentration ratio of Co(II) to Pd(II) varying between 7:1 and 1:8 in the mixed samples. Both proposed methods (PLS and HPSAM) were applied to the determination of Co(II) and Pd(II) in several alloy solutions with satisfactory results.     

Application of Multivariate Calibration Techniques to Simultaneous Spectrophotometric Determination of Copper and Iron Using 1‐(2‐Pyridylazo)‐2‐naphthol in AOT Micellar Solution

Multivariate calibration models (PCR and PLS) were developed for simultaneous determination of Fe(III) and Cu(II) with 1‐(2‐pyridylazo)‐2‐naphthol and AOT as chromogenic reagent and micellizing agent, respectively. In the presence of AOT the spectrum of Fe(III)‐PAN complex was shifted to higher wavelength and the overlapping with Cu‐PAN spectrum decreased. It seems that this anionic surfactant enters the structure of the Fe‐PAN complex to cause a shift in the absorption spectrum of it. The parameters controlling behavior of the systems were investigated and optimum conditions were selected. Sixteen ternary mixtures were selected as the calibration set. To select the number of factors in PCR and PLS algorithms, a cross validation method, leaving out one sample at a time, was employed. The calibration models were validated with 8 synthetic mixtures containing the metal ions in different proportions that were randomly designed. The best calibration model was obtained by using PLS regression. The method was successfully applied to simultaneous determination of copper and iron in biological samples.     

Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network

A novel method named a wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for the simultaneous UV–visible spectrometric determination of Cu(II), Cd(II) and Zn(II). This method combined wavelet packet denoising with an Elman recurrent neural network. A wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. An Elman recurrent network was applied for nonlinear multivariate calibration. In this case, using trials, the kind of wavelet function, the decomposition level, and the number of hidden nodes for the WPTERNN method were selected as Daubechies 14, 3, and 8, respectively. A program (PWPTERNN) was designed that could perform the simultaneous determination of Cu(II), Cd(II) and Zn(II). The relative standard errors of prediction (RSEP) obtained for all components using WPTERNN, a Elman recurrent neural network (ERNN), partial least squares (PLS), principal component regression (PCR), Fourier transform based PCR (FTPCR), and multivariate linear regression (MLR) were compared. Experimental results demonstrated that the WPTERRN method was successful even where there was severe overlap of spectra. The results obtained from an additional test case also demonstrated that the WPTERNN method performed very well. Figure The part of WP coefficients obtained by wavelet packet transforms     

Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection

This paper proposes a methodology for the classification and determination of total protein in milk powder using near infrared reflectance spectrometry (NIRS) and variable selection. Two brands of milk powder were acquired from three Brazilian cities (Natal-RN, Salvador-BA and Rio de Janeiro-RJ). The protein content of 38 samples was determined by the Kjeldahl method and NIRS analysis. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations were used to predict the total protein. Soft independent modeling of class analogy (SIMCA) was also used for full-spectrum classification, resulting in almost 100% classification accuracy, regardless of the significance level adopted for the F-test. Using this strategy, it was feasible to classify powder milk rapidly and nondestructively without the need for various analytical determinations. Concerning the multivariate calibration models, the results show that PCR, PLS and MLR-SPA models are good for predicting total protein in powder milk; the respective root mean square errors of prediction (RMSEP) were 0.28 (PCR), 0.25 (PLS), 0.11 wt% (MLR-SPA) with an average sample protein content of 8.1 wt%. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for the determination of total protein in milk powder.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Quantitative analysis of less soluble form IV in commercial carbamazepine (form III) by diffuse reflectance fourier transform spectroscopy (DRIFTS) and lazy learning algorithm

Carbamazepine is a poorly soluble drug, with known bioavailability problems related to its polymorphism, and a form (C-monoclinic or form IV) less soluble than the pharmaceutically acceptable (P-monoclinic or form III) can be formed under various conditions, possible to occur during drug formulation. Therefore, quantitative analysis of form IV in form III is important to the drug formulators. In the present study, a fast and simple non-destructive method was developed for quantification of form IV in form III, by using DRIFTS spectral data subjected to the standard normal variate transformation (row centering and scaling) and to the lazy learning algorithm. Fast principal component (fast PCR) and partial least squares (PLS) regression methods of multivariate calibration were also used, which were compared with lazy learning. The lazy learning algorithm was performing better than the fast PCR and PLS methods (root mean squared error of cross-validation 1.318% versus 3.337 and 3.058%, respectively). Even with a small number of calibration samples it gave satisfactory predictive performance (root mean squared error of prediction <2.0% versus >3.3% of fast PCR and >2.6% of PLS), in the concentration range below 30% (w/w) of form IV. This is attributed to the capability of handling non-linearity in the relation of reflectance and concentration as well as to local modeling using a pre-selected number of nearest neighbor concentrations.     

A comparative study between PCR and PLS in simultaneous spectrophotometric determination of diphenylamine, aniline, and phenol: Effect of wavelength selection

Partial least squares (PLS) and principal component regression (PCR) have received considerable attention in the chemometrics for multicomponent analysis where superiority of one over another is a challenging problem yet. Considering the effect of wavelength selection, a comparison was made between PCR and PLS methods by application those to simultaneous spectrophotometric determination of diphenylamine (DPA), a compound from the third European Union list of priority pollutants, and its environmentally related products aniline and phenol. The UV absorbance spectra of the methanolic solutions of the analytes were measured in the concentration ranges of 1.0-10.0 microg mL(-1) and then subjected to PCR and PLS. The models refinement procedure and validation was performed by cross-validation. A modified changeable size moving windows strategy, where optimized the intervals between the sensors in a selected windows, was also proposed to select the more informative spectral regions for each of the analytes. It was found that wavelength selection improved the quality of predictions for both regression methods whereas more reliable results were obtained by removing of the highly collinear neighboring wavelengths. The resultant data explained that PLS produced more or less better results when whole spectral data were used but in the case of selected wavelength regions both methods produced similar results and no comments could be given about the superiority of one against another. The major difference was obtaining the higher number of factors for PCR, which is not a significant problem.     

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.