Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis

The secondary metabolites of different Ephedra plants are various. Therefore, the discrimination of different Ephedra plants is significant. An objective, easy-to-use, rapid and pollution-free approach is proposed for discriminating Ephedra plants of different species, habitats and picking times on the basis of diffuse reflectance Fourier transform near infrared spectroscopy (FT-NIRS) measurements and multivariate analysis. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from 37 pulverized samples of Ephedra plants put in glass vials in the near infrared (NIR) region between 10 000 and 4000 cm⁻¹, averaging 64 scans per spectrum at a resolution of 4 cm⁻¹. After spectra processing and data pre-processing, spectral data were analyzed respectively with three multivariate analysis techniques: discriminant analysis (DA), self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The proposed method could distinguish not only the Ephedra plants of three species and two habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The performance indexes of the DA model were 84.2-91.9% and the prediction accuracies of both the SOM and the BP-ANN models reached 93.3-100.0%.     

Kinetics independent spectrometric analysis using non-linear calibration modelling and exploitation of concentration gradients generated by a flow-batch system for albumin and total protein determination in blood serum

An automatic method for kinetics independent spectrometric analysis is proposed in this study. It uses a non-linear calibration model that explores concentration gradients generated by a flow-batch analyser (FBA) for the samples, dye, and the single standard solution. The procedure for obtaining the gradients of the dye and standard solution is performed once at the beginning of analysis. The same procedure is applied thereafter for each sample. For illustration, the proposed automatic methodology was applied to determine total protein and albumin in blood serum by using the Biuret and Bromocresol Green (BCG) methods. The measurements were made by using a laboratory-made photometer based on a red and green bicolour LED (Light-Emitting Diode) and a phototransistor, coupled to a “Z” form flow cell. The sample throughput was about 50 h⁻¹ for albumin and 60 h⁻¹ for total protein, consuming about 7 μL of sample, 2.6 mL of BCG and 1.2 mL of biuret reagents for each determination. Applying the paired t-test for results from the proposed analyser and the reference method, no statistic differences at 95% confidence level were found. The absolute standard deviation was usually smaller than 0.2 g dL⁻¹. The proposed method is valuable for the determination of total protein and albumin; and can also be used in other determinations where kinetic effects may or may not exist.     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples

Determination of particle size is one of the critical parameters in nanotechnology. The relationship between particle size and diffuse reflectance (DR) spectra in near-infrared region has been applied to introduce a method for estimation of particle size. Back-propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near-infrared diffuse reflectance spectra. Thirty five different nano TiO₂ samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP-ANN. The network was trained by 30 samples and was evaluated by the remaining 5 samples. In order to establish whether the new method is applicable for estimation of particle size of nano structured samples, the optimized model was applied to analyze 44 nano TiO₂ samples. It was observed that ANN using the back-propagation algorithm is capable of generalization and could correctly predict the average particle size of nano-sized particles.     

Semi-automated in vivo solid-phase microextraction sampling and the diffusion-based interface calibration model to determine the pharmacokinetics of methoxyfenoterol and fenoterol in rats

In vivo solid-phase microextraction (SPME) can be used to sample the circulating blood of animals without the need to withdraw a representative blood sample. In this study, in vivo SPME in combination with liquid–chromatography tandem mass spectrometry (LC–MS/MS) was used to determine the pharmacokinetics of two drug analytes, R,R-fenoterol and R,R-methoxyfenoterol, administered as 5 mg kg⁻¹i.v. bolus doses to groups of 5 rats. This research illustrates, for the first time, the feasibility of the diffusion-based calibration interface model for in vivo SPME studies. To provide a constant sampling rate as required for the diffusion-based interface model, partial automation of the SPME sampling of the analytes from the circulating blood was accomplished using an automated blood sampling system. The use of the blood sampling system allowed automation of all SPME sampling steps in vivo, except for the insertion and removal of the SPME probe from the sampling interface. The results from in vivo SPME were compared to the conventional method based on blood withdrawal and sample clean up by plasma protein precipitation. Both whole blood and plasma concentrations were determined by the conventional method. The concentrations of methoxyfenoterol and fenoterol obtained by SPME generally concur with the whole blood concentrations determined by the conventional method indicating the utility of the proposed method. The proposed diffusion-based interface model has several advantages over other kinetic calibration models for in vivo SPME sampling including (i) it does not require the addition of a standard into the sample matrix during in vivo studies, (ii) it is simple and rapid and eliminates the need to pre-load appropriate standard onto the SPME extraction phase and (iii) the calibration constant for SPME can be calculated based on the diffusion coefficient, extraction time, fiber length and radius, and size of the boundary layer. In the current study, the experimental calibration constants of 338.9 ± 30 mm⁻³ and 298.5 ± 25 mm⁻³ are in excellent agreement with the theoretical calibration constants of 307.9 mm⁻³ and 316.0 mm⁻³ for fenoterol and methoxyfenoterol respectively.     

Sorptive thin film microextraction followed by direct solid state spectrofluorimetry: A simple,rapid and sensitive method for determination of carvedilol in human plasma

A poly acrylate-ethylene glycol (PA-EG) thin film is introduced for the first time as a novel polar sorbent for sorptive extraction method coupled directly to solid-state spectrofluorimetry without the necessity of a desorption step. The structure, polarity, fluorescence property and extraction performance of the developed thin film were investigated systematically. Carvedilol was used as the model analyte to evaluate the proposed method. The entire procedure involved one-step extraction of carvedilol from plasma using PA-EG thin film sorptive phase without protein precipitation. Extraction variables were studied in order to establish the best experimental conditions. Optimum extraction conditions were the followings: stirring speed of 1000 rpm, pH of 6.8, extraction temperature of 60 °C, and extraction time of 60 min. Under optimal conditions, extraction of carvedilol was carried out in spiked human plasma; and the linear range of calibration curve was 15–300 ng mL⁻¹ with regression coefficient of 0.998. Limit of detection (LOD) for the method was 4.5 ng mL⁻¹. The intra- and inter-day accuracy and precision of the proposed method were evaluated in plasma sample spiked with three concentration levels of carvedilol; yielding a recovery of 91–112% and relative standard deviation of less than 8%, respectively. The established procedure was successfully applied for quantification of carvedilol in plasma sample of a volunteer patient. The developed PA-EG thin film sorptive phase followed by solid-state spectrofluorimetric method provides a simple, rapid and sensitive approach for the analysis of carvedilol in human plasma.     

Application of chemometric methods to the simultaneous kinetic spectrophotometric determination of iodate and periodate based on consecutive reactions

A kinetic spectrophotometric method for the simultaneous determination of iodate and periodate in mixtures was proposed. The method is established on the different kinetic behaviours of the analytes which react with starch–iodide in the presence of sodium chloride in sulfuric acid medium. The kinetic data were collected from 260 to 900 nm every 10 nm, within a time range of 0–180 s at 1 s interval, and the absorbance collected at 291, 354 and 585 nm, respectively, increased linearly with the concentration between 0.1–1.2 mg L^− 1 for both iodate and periodate. The mechanism investigation revealed that the iodate/periodate–iodide–starch system is a consecutive reaction. Subsequently, the mathematical model for the quantitative kinetic determination based on the consecutive reactions by utilizing chemometric methods was deduced, and the simultaneous determination of synthetic mixtures of iodate and periodate was then applied. Kinetic data collected at 291, 354 and 585 nm, were processed by chemometric methods, such as classical least square (CLS), principal component regression (PCR), partial least square (PLS), back-propagation artificial neural network (BP-ANN), radial basis function–artificial neural network (RBF-ANN) and principle component–radial basis function–artificial neural network (PC-RBF-ANN). The results showed that calibration model with the data collected at 354 nm had some advantages for the prediction of the analytes as compared with the ones of other two wavelengths, and the PLS and PC-RBF-ANN gave the lower prediction errors than other chemometric methods. The proposed method was applied to the simultaneous determination of iodate and periodate in several real samples; and the standard addition method yielded satisfactory recoveries in all instances.     

LC–MS3 Strategy for Quantification of Carbamazepine in Human Plasma and Its Application in Therapeutic Drug Monitoring

This study developed a detection method based on the strategy of HPLC/MS³ and verified its suitability by quantifying carbamazepine in human plasma. The high-performance liquid chromatography–tandem mass spectrometry (HPLC/MS³) system was performed using a Shimadzu UFLC XR liquid chromatography and a SCIEX QTRAP^® 5500 linear ion trap triple quadrupole mass spectrometer. The specific operation was as follows: the sample protein was firstly precipitated using methanol, then carbamazepine and carbamazepine-D₂N¹⁵ were separated on an ACQUITY UPLC HSS T3 column using the gradient elution with solvent A (0.1% formic acid) and solvent B (0.1% formic acid in acetonitrile) at a flow rate of 0.25 mL/min. Each sample was run for 7 min. This method was validated for various parameters including accuracy, precision, selectivity, linearity, LLOQ, etc. Only 5 μL of sample plasma could obtain the result of LLOD 0.5 µg/mL. The intra-day and inter-day precision was <8.23%, and accuracy was between −1.74% and 2.92%. This method was successfully used for monitoring the blood concentration of epilepsy patients after carbamazepine treatment.     

Quantitation of low concentrations of polysorbates in high protein concentration formulations by solid phase extraction and cobalt-thiocyanate derivatization

A spectrophotometric method was developed to quantify low polysorbate (PS) levels in biopharmaceutical formulations containing high protein concentrations. In the method, Oasis HLB solid phase extraction (SPE) cartridge was used to extract PS from high protein concentration formulations. After loading a sample, the cartridge was washed with 4 M guanidine HCl and 10% (v/v) methanol, and the retained PS was eluted by acetonitrile. Following the evaporation of acetonitrile, aqueous cobalt-thiocyanate reagent was added to react with the polyoxyethylene oxide chain of polysorbates to form a blue colored PS–cobaltothiocyante complex. This colored complex was then extracted into methylene chloride and measured spectrophotometrically at 620 nm. The method performance was evaluated on three products containing 30–40 mg L⁻¹ PS-20 and PS-80 in ≤70 g L⁻¹ protein formulations. The method was specific (no matrix interference identified in three types of protein formulations), sensitive (quantitation limit of 10 mg L⁻¹ PS) and robust with good precision (relative standard deviation ≤6.4%) and accuracy (spike recoveries from 95% to 101%). The linear range of the method for both PS-20 and PS-80 was 10 to 80 mg L⁻¹ PS. By diluting samples with 6 M guanidine HCl and/or using different methylene chloride volumes to extract the colored complexes of standards and samples, the method could accurately and precisely quantify 40 mg L⁻¹ PS in up to 300 g L⁻¹ protein 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 orthophosphates using a macro segmented flow analyzer (MSFA) based on colorimetric detection

Fabrication of a macro segmented flow analysis (MSFA) system based on reconfiguration of the manifold by adjustment of the sample/reagent ratio, has been found to produce a sensitive method for orthophosphate analysis based on colorimetric detection at 880 nm. Optimization of sample tube length, reaction temperature and molybdate concentration in the carrier solutions has been carried out. The larger sample tube internal diameter led to the combined advantages of better sensitivities, wider working range and higher sample throughput over most existing methods. Using the optimized conditions of 50.0 cm sample tube length (1.6 mm i.d.), 37.0 °C reaction temperature and 0.0113 M molybdate concentration in the carrier solution, the calibration model for orthophosphate standard solutions was found to be linear (y = 0.04895x + 0.003561; correlation coefficient, r²=0.9970) over the working range 0.01-2.00 mg l⁻¹ orthophosphate. The volume of the sample injected was 1.396 ml at a flow rate of 6.0 ml min⁻¹. The sample throughput of this MSFA method was 40 samples per an hour, with a detection limit of 4.0 μg l⁻¹, and %R.S.D.’s below 5%. The MSFA method was successfully applied to analysis of water and wastewater samples.     

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca,K, P,Fe, Mn,Na, Zn), protein and moisture in alfalfa

In the present work we study the use of near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of major (Ca, K, P) and minor (Fe, Mn, Na, Zn) elements, protein and moisture in alfalfa. The method allows immediate analysis of the alfalfa without prior sample treatment or destruction through direct application of the fibre-optic probe on ground samples in the case of the mineral composition and on-ground and compacted (baled) samples in the case of protein and humidity. The regression method employed was modified partial least-squares (MPLS). The calibration results obtained using samples of alfalfa allowed the determination of Ca, K, P, Fe, Mn, Na and Zn, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) expressed in mg/kg of alfalfa of 1.37 × 10³ and 0.878 for Ca, 1.10 × 10³ and 0.899 for K, 227 and 0.909 for P, 103 and 0.948 for Fe, 5.1 and 0.843 for Mn, 86.2 and 0.979 for Na, and of 1.9 and 0.853 for Zn, respectively. The SEP(C) and RSQ values (in %) for protein and moisture in ground samples were 0.548 and 0.871 and 0.150 and 0.981, respectively; while in the compacted samples they were 0.564 and 0.826 and 0.262 and 0.935, respectively. The prediction capacity of the model and the robustness of the method were checked in the external validation in alfalfa samples of unknown composition, and the results confirmed the suitability of the method.     

Aptamer-functionalized solid phase microextraction–liquid chromatography/tandem mass spectrometry for selective enrichment and determination of thrombin

In this publication, a novel solid phase microextraction (SPME) coating functionalized with a DNA aptamer for selective enrichment of a low abundance protein from diluted human plasma is described. This approach is based on the covalent immobilization of an aptamer ligand on electrospun microfibers made with the hydrophilic polymer poly(acrylonitrile-co-maleic acid) (PANCMA) on stainless steel rods. A plasma protein, human α-thrombin, was employed as a model protein for selective extraction by the developed Apt-SPME probe, and the detection was carried out with liquid chromatography/tandem mass spectrometry (LC–MS/MS). The SPME probe exhibited highly selective capture, good binding capacity, high stability and good repeatability for the extraction of thrombin. The protein selective probe was employed for direct extraction of thrombin from 20-fold diluted human plasma samples without any other purification. The Apt-SPME method coupled with LC–MS/MS provided a good linear dynamic range of 0.5–50 nM in diluted human plasma with a good correlation coefficient (R² = 0.9923), and the detection limit of the proposed method was found to be 0.30 nM. Finally, the Apt-SPME coupled with LC–MS/MS method was successfully utilized for the determination of thrombin in clinical human plasma samples. One shortcoming of the method is its reduced efficiency in undiluted human plasma compared to the standard solution. Nevertheless, this new aptamer affinity-based SPME probe opens up the possibility of selective enrichment of a given targeted protein from complex sample either in vivo or ex vivo.     

毛细管电泳径向基神经网络校正法定量分析核苷

采用径向基神经网络算法对一组已知样品的核苷及内标物浓度与毛细管电泳峰面积数据进行回归计算,建立峰面积与核苷浓度之间的关系模型,对未知样品中待测核苷浓度作出预测,形成了毛细管电泳定量分析新方法．将其用于鸟嘌呤核苷含量测定,所建模型预测结果平均相对误差为0．86％,明显低于线性回归及BP神经网络模型的2．60％和1．07％．研究结果表明,本方法简便易用,能有效提高毛细管电泳定量分析的准确度,优于线性回归及BP神经网络法．     

Development and Validation of a Reliable UHPLC-MS/MS Method for Simultaneous Quantification of Macrocyclic Lactones in Bovine Plasma

A fast, accurate and reliable ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) method was developed for simultaneous quantification of ivermectin (IVER), doramectin (DORA), and moxidectin (MOXI) in bovine plasma. A priority for sample preparation was the eradication of possible infectious diseases to avoid travel restrictions. The sample preparation was based on protein precipitation using 1% formic acid in acetonitrile, followed by Ostro^® 96-well plate pass-through sample clean-up. The simple and straightforward procedure, along with the short analysis time, makes the current method unique and suitable for a large set of sample analyses per day for PK studies. Chromatographic separation was performed using an Acquity UPLC HSS-T3 column, with 0.01% acetic acid in water and methanol, on an Acquity H-Class ultra-high performance liquid chromatograph (UHPLC) system. The MS/MS instrument was a Xevo TQ-S^® mass spectrometer, operating in the positive electrospray ionization mode and two multiple reaction monitoring (MRM) transitions were monitored per component. The MRM transitions of m/z 897.50 > 753.4 for IVER, m/z 921.70 > 777.40 for DORA and m/z 640.40 > 123.10 for MOXI were used for quantification. The method validation was performed using matrix-matched calibration curves in a concentration range of 1 to 500 ng/mL. Calibration curves fitted a quadratic regression model with 1/x2 weighting (r ≥ 0.998 and GoF ≤ 4.85%). Limits of quantification (LOQ) values of 1 ng/mL were obtained for all the analytes, while the limits of detection (LOD) were 0.02 ng/mL for IVER, 0.03 ng/mL for DORA, and 0.58 ng/mL for MOXI. The results of within-day (RSD < 6.50%) and between-day (RSD < 8.10%) precision and accuracies fell within acceptance ranges. No carry-over and no peak were detected in the UHPLC-MS/MS chromatogram of blank samples showing good specificity of the method. The applicability of the developed method was proved by an analysis of the field PK samples.     

Simultaneous determination of iron and aluminium by differential kinetic spectrophotometric method and chemometrics

A differential kinetic spectrophotometric method was researched and developed for the simultaneous determination of iron and aluminium in food samples. It was based on the direct reaction kinetics and spectrophotometry of these two metal ions with Chrome Azurol S (CAS) in ethylenediamine-hydrochloric acid buffer (pH 6.3). The results were interpreted with the use of chemometrics. The kinetic runs and the visible spectra of the complex formation reaction were studied between 540 and 750 nm every 30 s over a total period of 285 s. A set of synthetic metal mixture samples was used to build calibrations models. These were based on the spectral and kinetic two-way data matrices, which were processed separately by the radial basis function-artificial neural network (global RBF-ANN) method. The prediction performance of these models was poorer than that from the combined kinetic-spectral three-way array, which was similarly processed by the same method (% relative prediction error (RPE_T) = 5.6). These results demonstrate that improved predictions can be obtained from the data array, which has more information, and that appropriate chemometrics methods can enhance analytical performance of simple techniques such as spectrophotometry.Other chemometrics models were then applied: N-way partial least squares (NPLS), parallel factor analysis (PARAFAC), back propagation-artificial neural network (BP-ANN), single radial basis function-artificial neural network (RBF-ANN), and principal component neural network (PC-RBF-ANN). There was no substantial difference between the methods with the overall %RPE_T range being 5.0-5.8. These two values corresponded to the NPLS and BP-ANN models, respectively. The proposed method was applied for the determination of iron and aluminium in some commercial food samples with satisfactory results.     

Determination of clenbuterol by capillary electrophoresis immunoassay with chemiluminescence detection

A competitive immunoassay for clenbuterol (CLB) based on capillary electrophoresis with chemiluminescence (CL) detection was established. The method was based on the competitive reaction of horseradish peroxidase (HRP)-labeled CLB (CLB-HRP) and free CLB with anti-CLB antiserum. The factors affecting the electrophoresis and CL detection were systematically investigated with HRP as a model sample. Under the optimal conditions, the tracer CLB-HRP and the immunoassay complex were separated, and the linear range and the detection limit (S/N = 3) for CLB were 5.0-40 nmol l⁻¹ and 1.2 nmol l⁻¹, respectively. The proposed method has been applied satisfactorily in the analysis of urine sample.