Multivariate control charts based on net analyte signal (NAS) for characterization of the polymorphic composition of Piroxicam using near infrared spectroscopy

Near infrared spectroscopic and multivariate statistical control charts based on the net analyte signal (NAS) were applied to the polymorphic characterization of Piroxicam samples. Three different polymorphic forms (I, II and III) were studied, using X-ray powder diffraction (XRPD) and scanning electron microscopy as reference techniques. Samples containing form I were considered inside the quality specifications and forms II and III were impurities. Three control charts were developed: the NAS chart that corresponds to the analyte of interest (polymorphic form I), the interference chart that corresponds to the contribution of other compounds in the sample and the residual chart that corresponds to nonsystematic variation. From the limits estimated for each chart using samples inside the quality specifications, it was possible to identify samples that did not present polymorphic form I. The use of multivariate control charts provides a rapid evaluation of purity and the polymorphic composition of pharmaceutical formulations based on Piroxicam.     

Multivariate control charts based on NAS and mid‐infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

In this work, mid‐infrared spectroscopy and multivariate control charts based on net analyte signal were applied for quality control of B5 blends of biodiesel/diesel (5% biodiesel/95% diesel). Control charts were constructed using instrumental signal decomposition, generating three charts: the net analyte signal chart for monitoring the analyte of interest (methyl soybean biodiesel); the interference chart, which corresponds to the contribution of all other compounds in the diesel sample (diesel); and the residual chart, which corresponds to non‐systematic variations. Statistical limits were established for each developed chart, using samples inside quality specifications (normal operation conditions). To validate multivariate control charts, new samples were analyzed. The new samples represented samples in‐control and samples out‐of‐control in relation to the content of biodiesel, adulterated biodiesel with severe vegetable oils and adulterated diesel with residual automotive lubricant oil, kerosene, and gasoline. The results obtained show an excellent distinction between the samples inside and out of the quality specifications, with 91% and 100% correctly classified, respectively, which demonstrates that the methodology developed is a viable alternative for quality monitoring of this type of fuel. Copyright © 2015 John Wiley & Sons, Ltd.     

Simultaneous Quantification of Three Polymorphic Forms of Carbamazepine using Raman Spectroscopy and Multivariate Calibration

Carbamazepine is a pharmaceutical product used to treat epilepsy and bipolar disorder. Some active pharmaceutical ingredients, such as carbamazepine, present polymorphism that may alter the bioavailability. Consequently, the determination of different polymorphic forms has become important for the pharmaceutical industry. In this work, polymorphic forms were synthesized and characterized by differential scanning calorimetry and X-ray diffraction. Raman spectroscopy was used to quantify mixtures of the three common polymorphic forms of carbamazepine. A ternary mixture design was used to create the calibration set of ten samples and six levels of concentration for each polymorph. Partial least squares was performed to build the prediction models. Ten spectra were obtained to obtain representative Raman spectra of the mixtures. The calibration models were built using the average spectra, and an external set of samples was used to evaluate the models. The partial least squares model gave a root mean square error of prediction of 6.2% for carbamazepine I, 6.8% for carbamazepine III, and 11.6% for carbamazepine dihydrate. The results showed that good results were obtained for the solid state characterization of the mixtures of polymorphs using a fast strategy for simultaneous analysis.     

金测定的方法验证及质量控制图的建立

对DZ/T 0279.19–2016 《区域地球化学样品分析方法第19部分:金量测定泡沫塑料富集–石墨炉原子吸收光谱法》进行了实验室方法验证,并建立了相应的质量控制图。结果表明,实验室具备采用泡沫塑料富集–石墨炉原子吸收光谱法测定金的资源和技术能力,建立的质量控制图能够对分析系统进行有效核查。     

Sensitivity of fast-atom bombardment mass spectrometry to various polymorphic forms of cortisone acetate.

The present paper discusses non-traditional possibilities of the applications of mass spectrometry to the detection and study of differences in crystal polymorphic forms. The parameter of fast-atom bombardment which is sensitive to different polymorphs, namely the rate of the formation of cluster ions of an analyte with the glycerol matrix, was chosen for study. Using as an example the analysis of four different samples of cortisone acetate, varying in their polymorphic forms, treatment procedures, and dispersion of the crystalline powder, it is shown that the relative intensity of the cluster ion [MGH]+ (where M represents the analyte and G a molecule of glycerol) differs for two of the polymorphic forms and is the same for one of these forms, whether it is obtained by recrystallization from chloroform or by cryogrinding. Analysis of the time dependences of the [MGH]+ ion intensity allowed us to detect finer effects in the samples, associated with cryogrinding, namely differences in solubility and presumably, mechanoactivation.     

Robustness testing of control charts employed by analytical laboratories

 The robustness of Shewhart control charts for subgroup means and subgroup ranges was tested by using the Monte Carlo method using training data sets comprising various numbers of points, with two repetitions in each subgroup (as in routine laboratory practice). The following control chart designs were tested: conventional based on the arithmetic mean and standard deviation, robust based on the median and/or the trimmed mean and Winsorized standard deviation, and a two-step design. The methods were applied to the system in the state of statistical control (outliers excluded) and to the system without statistical control (outliers included). Satisfactory results for both cases were only obtained when using the two-stage control charts. The conventional charts led to underestimation of the effect of outliers in the system without statistical control, whereas the robust control charts led to overestimation of the effect of outliers (false alarm) in the system under statistical control. The tests also gave evidence that the training set should include 20 points as a minimum. Received: 13 January 1997 Accepted: 12 February 1997     

Multivariate standard addition method solved by net analyte signal calculation and rank annihilation factor analysis

This article describes the use of the net analyte signal (NAS) concept and rank annihilation factor analysis (RAFA) for building two different multivariate standard addition models called “SANAS” and “SARAF.” In the former, by the definition of a new subspace, the NAS vector of the analyte of interest in an unknown sample as well as the NAS vectors of samples spiked with various amounts of the standard solutions are calculated and then their Euclidean norms are plotted against the concentration of added standard. In this way, a simple linear standard addition graph similar to that in univariate calibration is obtained, from which the concentration of the analyte in the unknown sample and the analytical figures of merit are readily calculated. In the SARAF method, the concentration of the analyte in the unknown sample is varied iteratively until the contribution of the analyte in the response data matrix is completely annihilated. The proposed methods were evaluated by analyzing simulated absorbance data as well as by the analysis of two indicators in synthetic matrices as experimental data. The resultant predicted concentrations of unknown samples showed that the SANAS and SARAF methods both produced accurate results with relative errors of prediction lower than 5% in most cases.     

Interrelationships between generalized Tikhonov regularization,generalized net analyte signal,and generalized least squares for desensitizing a multivariate calibration to interferences

Orthogonal pre‐processing (orthogonal projection) of spectral data is a common approach to generate analyte‐specific information for use in multivariate calibration. The goal of this pre‐processing is to remove from each spectrum the respective sample interferent contributions (spectral interferences from overlap, scatter, noise, etc.). Two approaches to accomplish orthogonal pre‐processing are net analyte signal (NAS) and generalized least squares (GLS). Developed in this paper is the mathematical relationship between NAS and GLS. It is also realized that orthogonal NAS pre‐processing can remove too much analyte signal and that the degree of interferent correction can be regulated. Similar to GLS, the degree of correction is accomplished by using a regularization (tuning) parameter to form generalized NAS (GNAS). Also developed in this paper is an alternative to GNAS and GLS based on generalized Tikhonov regularization (GTR). The mathematical relationships between GTR, GNAS, and GLS are derived. A result is the ability to express the model vector as the sum of two contributions: the orthogonal NAS contribution and a non‐NAS contribution from the interferent components. Thus, rather than the usual situation of sequentially pre‐processing data by either GNAS or GLS followed by model building with the pre‐processed data, the methods of GTR, GNAS, and GLS are expressed as direct computations of model vectors allowing concurrent pre‐processing and model building to occur. Simultaneous pre‐processing and model forming are shown to be natural to the GTR process. Two near‐infrared spectroscopic data sets are studied to compare the theoretical relationships between GTR, GNAS, and GLS. One data set covers basic calibration, and the other data set is for calibration maintenance. Filter factor representation is key to developing the interprocess relationships. Copyright © 2013 John Wiley & Sons, Ltd.     

Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pretreatments and estimation of multivariate detection limits

Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600 ppm in an industrial ester. The detection limit obtained, round 100 ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes.     

Evaluation of statistical control charts for on-line radiation monitoring

Statistical control charts are presented for the evaluation of time series radiation counter data from flow cells used for monitoring of low levels of ⁹⁹TcO₄⁻ in environmental solutions. Control chart methods consisted of the 3-sigma (3σ) chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving average (EWMA) chart. Each method involves a control limit based on the detector background which constitutes the detection limit. Both the CUSUM and EWMA charts are suitable to detect and estimate sample concentration requiring less solution volume than when using a 3σ control chart. Data presented here indicate that the overall accuracy and precision of the CUSUM method is the best.     

三元醇丙烯酰胺键合色谱固定相的制备及评价

利用巯基与乙烯基的"点击化学"反应合成了一种新型含多羟基的硅烷偶联剂,再将其与硅胶反应制得含多羟基的亲水固定相。经过元素分析表征证明多羟基官能团已成功键合到硅胶表面。采用一系列不同性质的标准物质考察了亲水色谱模式下固定相的溶质保留机理。由于固定相结构中既具有极性多羟基官能团,也有短的疏水碳链,因此固定相兼具疏水性与亲水性。将此固定相成功应用于亲水与反相色谱两种模式,并对比了两种模式下流速对柱效的影响。最后将固定相应用于烷基苯、水溶性维生素以及核苷的分离中,取得了较好的分离效果,证明了固定相良好的应用前景。     

Monitoring analytical measurements in presence of two component measurement error

Control charts are increasingly adopted by laboratories for effective monitoring of analytical processes. Analytical methods are mostly subject to two types of measurement errors, i—additive and ii—multiplicative, or proportional, error. These errors have been combined in a single model, namely the two component error model (TCME) proposed by [1]. In this study we present a comparison among the performance of three widely used location control charts, i.e. Shewhart, CUSUM and EWMA charts in presence of TCME model. This study will help quality practitioners to choose an efficient chart for the monitoring of analytical measurements.     

Application of Quartz Crystal Nanobalance for Simultaneous Determination of Vanillylmandelic and Homovanillic Acids by a Net Analyte Signal-Based Method

Homovanillic acid (HVA) and vanillylmandelic acid (VMA) were selectively determined by quartz crystal nanobalance sensor in conjunction with net analyte signal (NAS)-based method called HLA/GO. An orthogonal design was applied for the formation of calibration and prediction sets including HVA, VMA, and some common and structurally similar urine compounds. The selection of the optimal time range involved the calculation of the NAS regression plot in any considered time window for each test sample. The searching of a region with maximum linearity of NAS regression plot (minimum error indicator) and minimum of predicted error sum of squares value was carried out by applying a moving window strategy. Based on the obtained results, the differences on the adsorption profiles in the time range between 1 and 300 s were used to determine mixtures of compounds by HLA/GO method. Several figures of merit like selectivity, sensitivity, analytical sensitivity, and limit of detection were calculated for both compounds. The results showed that the method was successfully applied for the determination of VMA and HVA.     

Biointeraction analysis of carbamazepine binding to α1‐acid glycoprotein by high‐performance affinity chromatography

Interactions of the drug carbamazepine with the serum protein α₁‐acid glycoprotein (AGP) were examined by high‐performance affinity chromatography. Frontal analysis studies with an immobilized AGP column and control column indicated carbamazepine had both low‐affinity interactions with the support and high‐affinity interactions with AGP. When a correction was made for binding to the support, the association equilibrium constant measured at pH 7.4 and 37°C for carbamazepine with AGP was 1.0 (±0.1)×10⁵ M^?1, with values that ranged from 5.1 to 0.58×10⁵ M^?1 in going from 5 to 45°C. It was found in competition studies that these interactions were occurring at the same site that binds propranolol on AGP. Temperature studies indicated that the change in enthalpy was the main driving force for the binding of carbamazepine to AGP. These results provide a more complete picture of how carbamazepine binds to AGP in serum. This report also illustrates how high‐performance affinity chromatography can be used to examine biological interactions and drug–protein binding in situations in which significant interactions for an analyte are present with both the chromatographic support and an immobilized ligand.     

Chemometric resolution of fully overlapped CE peaks: Quantitation of carbamazepine in human serum in the presence of several interferences

Drug monitoring in serum samples was performed using second‐order data generated by CE‐DAD, processed with a suitable chemometric strategy. Carbamazepine could be accurately quantitated in the presence of its main metabolite (carbamazepine epoxide), other therapeutic drugs (lamotrigine, phenobarbital, phenytoin, phenylephrine, ibuprofen, acetaminophen, theophylline, caffeine, acetyl salicylic acid), and additional serum endogenous components. The analytical strategy consisted of the following steps: (i) serum sample clean‐up to remove matrix interferences, (ii) data pre‐processing, in order to reduce the background and to correct for electrophoretic time shifts, and (iii) resolution of fully overlapped CE peaks (corresponding to carbamazepine, its metabolite, lamotrigine and unexpected serum components) by the well‐known multivariate curve resolution‐alternating least squares algorithm, which extracts quantitative information that can be uniquely ascribed to the analyte of interest. The analyte concentration in serum samples ranged from 2.00 to 8.00 mg/L. Mean recoveries were 102.6% (s=7.7) for binary samples, and 94.8% (s=13.5) for spiked serum samples, while CV (%)=4.0 was computed for five replicate, indicative of the acceptable accuracy and precision of the proposed method.     

Multivariate control charts: Control charts for calibration curves

The proposed multivariate control charts for p-dimensional vectors are an extension of the conventional control charts for one variable. The controlling quantity is the Mahalanobis distance of vector x from the central value vector x..: D=(x-x..)^T-1.(x-x..), where is the covariance matrix estimate. The quantity D has Hotelling's T² distribution. A PC program was set up for the automatic graphical construction of such charts. The program draws the sequential chart of the quantity D as well as the position of the vectors x in the p dimensional control ellipsoid in the axes of the principal components. In this way a control chart was developed for the calibration curve in the photometric determination of Fe³⁺ with sulfosalicylic acid. Vector x was formed by absorbance values for the calibration curve points (p=5). The chart can assist in detection of even small disturbances of the calibration curve.     

Quality control samples for routine use in water laboratories

Summary Quality management systems in water laboratories have steadily improved during the past years, and the use of quality control samples is becoming routine. A selection of quality control samples has been designed and prepared by the Danish reference laboratory for water analysis. The samples cover 38 parameters at up to three concentration levels that are typical for environmental water. The samples have been produced in quantities that enable frequent use. Some samples are distributed together with preconstructed control charts for laboratory staff with limited knowledge of control chart statistics. Other users apply a Quality 4.0 computer programme for administration and statistical handling of quality control data with construction of control charts.     

An accurate quantitative analysis of polymorphic content by chemometric X-ray powder diffraction.

Data from X-ray powder diffraction (XRD) were subjected to a partial least-squares regression analysis (PLS) to build a calibration model for predicting the polymorphic content of carbamazepine (CBZ). The effectiveness of the PLS method in the construction of calibration models was analyzed by a scientific approach based on a regulation vector. CBZ forms I and III were characterized by differential scanning calorimetry (DSC) and XRD. Powder mixtures of forms I and III at various ratios (0-100% w/w; form III) were subjected to XRD. Five diffraction peaks were used for the peak-area method to compare with PLS. The results obtained by PLS had a better predictive accuracy compared to those of the peak-area method. The XRD-PLS method was established as a non-destructive, non-contact way to avoid the particle orientation effect based on statistical theory.     

Simultaneous spectrophotometric determination of phenanthridine, phenanthridinone and phenanthridine N-oxide using multivariate calibration methods

The multivariate calibration methods, partial least squares (PLS) and principle component regression (PCR) have been used to determine phenanthridine, phenanthridinone and phenanthridine N-oxide in spiked human plasma samples. Resolution of binary and ternary mixtures of analytes with minimum sample pre-treatment and without analyte separation has been successfully achieved analyzing the UV spectral data. The net analyte signal (NAS) concept was also used to calculate multivariate analytical figures of merit such as limit of detection, selectivity and sensitivity. The simultaneous determination of three analytes was possible by PLS and PCR processing of sample absorbance in the 210–355 nm region. Good recoveries were obtained for both synthetic mixtures and spiked human plasma samples.     

Determination of carbendazim, thiabendazole and fuberidazole using a net analyte signal-based method

The net analyte signal (NAS)-based method HLA/GO, modification of the original hybrid linear analysis (HLA) method, has been used to determine carbendazim, fuberidazole and thiabendazole in water samples. This approach was used after a solid-phase extraction (SPE) step, using the native fluorescence emission spectra of real samples, previously standardized by piecewise direct standardization (PDS). The results obtained show that the modification of HLA performs in a similar way that partial least-squares method (PLS-1). The NAS concept was also used to calculate multivariate analytical figures of merit such as limit of detection, selectivity, sensitivity and analytical sensitivity (γ⁻¹). With this purpose, blanks of methanol and ternary mixtures, with the target analyte at low concentration and the other two ranging according to the calibration matrix, were used, with different results. Detection limits calculated in the last way are more realistic and show the influence of the other components in the sample. Selectivity for carbendazim is higher than the corresponding values for fuberidazole and thiabendazole, whereas sensitivity, as well as the values obtained for their detection limits, are lower for carbendazim, followed by thiabendazole and fuberidazole. Results obtained by modification of HLA vary in the same way that the ones obtained by PLS-1.