Comparison of different calibration methods for the determination by FT-NIR spectroscopy of the hydroxyl number in polyester resins

Different calibration methods have been applied for the determination of the Hydroxyl Number in polyester resins, namely Partial Least Squares (PLS), Principal Component Regression (PCR), Ordinary Least Squares with selection of the variables by genetic algorithm (OLS-GEN) and back-propagation Artificial Neural Networks (BP-ANN). The predictive ability of the regression models was estimated by splitting the dataset in training and test sets by application of the Kohonen self-organising maps. The linear methods (OLS-GEN, PLS and PCR) showed comparable results while artificial neural networks provided the best results both in fitting and prediction.     

固相萃取-胶束电动色谱法测定牛奶中的4种β-内酰胺类抗生素

建立了同时分离检测牛奶中的氨苄西林、阿莫西林、青霉素V和头孢氨苄4种β-内酰胺类抗生素的固相萃取-胶束电动色谱法。牛奶样品经沉淀蛋白后,采用HLB固相萃取柱净化浓缩;以含十二烷基硫酸钠(SDS)的磷酸盐为缓冲液,胶束电动色谱分离,210 nm波长下检测。分离电压为18 kV,于9 min内达到基线分离。各组分在0.5～20 mg/L范围内呈良好的线性关系,相关系数(r2)为0.9943～0.9976;检出限为0.16～0.20 mg/L;除了阿莫西林外,回收率均大于70%。该方法准确可靠,重复性好,灵敏度高,可以用于牛奶中β-内酰胺类抗生素的定量检测。     

Detection of Ovine or Bovine Milk Components in Commercial Camel Milk Powder Using a PCR-Based Method

Food ingredient adulteration, especially the adulteration of milk and dairy products, is one of the important issues of food safety. The large price difference between camel milk powder, ovine, and bovine milk powder may be an incentive for the incorporation of ovine and bovine derived foods in camel milk products. This study evaluated the use of ordinary PCR and real-time PCR for the detection of camel milk powder adulteration based on the presence of ovine and bovine milk components. DNA was extracted from camel, ovine, and bovine milk powder using a deep-processed product column DNA extraction kit. The quality of the extracted DNA was detected by amplifying the target sequence from the mitochondrial Cytb gene, and the extracted DNA was used for the identification of milk powder based on PCR analysis. In addition, PCR-based methods (both ordinary PCR and real-time PCR) were used to detect laboratory adulteration models of milk powder using primers targeting mitochondrial genes. The results show that the ordinary PCR method had better sensitivity and could qualitatively detect ovine and bovine milk components in the range of 1% to 100% in camel milk powder. The commercial camel milk powder was used to verify the practicability of this method. The real-time PCR normalization system has a good exponential correlation (R² = 0.9822 and 0.9923) between ovine or bovine content and Ct ratio (specific/internal reference gene) and allows for the quantitative determination of ovine or bovine milk contents in adulterated camel milk powder samples. Accuracy was effectively validated using simulated adulterated samples, with recoveries ranging from 80% to 110% with a coefficient of variation of less than 7%, exhibiting sufficient parameters of trueness. The ordinary PCR qualitative detection and real-time PCR quantitative detection method established in this study proved to be a specific, sensitive, and effective technology, which is expected to be used for market detection.     

Micellar nanotubes dispersed electrokinetic chromatography for the simultaneous determination of antibiotics in bovine milk

A method to determine four antibiotics for veterinary use (ciprofloxacin, enrofloxacin, florfenicol, and chloramphenicol) of different families (fluoroquinolones and amphenicols) in bovine milk was developed. The determination of the analytes was carried out using micellar electrokinetic capillary chromatography (MEKC) with a common sodium borate-SDS buffer solution containing single-walled carbon nanotubes (SWCNTs). In this way, a great improvement in the electrophoretic resolution and the separation efficiency was achieved compared to MEKC. An online reverse electrode polarity-stacking mode (REPSM) was carried out to enhance sensitivity. This step was performed in only 2 min and it allowed a stacked percentage of 103. That means that all the amount of injected analytes is effectively stacked. When this stacking procedure was combined with an off-line preconcentration step, based on SPE, analytes could be detected in lower concentration than the established maximum residue limits (MRLs). The LODs for the four compounds were between 6.8 and 13.8 μg L(-1) and the RSD values were between 1.1% and 6.6%. The whole method was applied to spiked real samples with acceptable precision and satisfactory recoveries.     

On heteroaromaticity of nucleobases. Bond lengths as multidimensional phenomena

Three hundred and nine carbon-carbon, carbon-nitrogen, and carbon-oxygen pi-bond lengths in high precision crystal structures of 31 purine and pyrimidine nucleobases were related to the Pauling pi-bond order, its analogues corrected to crystal packing effects, the numbers of non-hydrogen atoms around the bond, and the sum of atomic numbers of the bond atoms. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) demonstrated that the bond lengths in the nucleobases are three-dimensional phenomenon, characterized by nine distinct classes of bonds. Bond lengths predicted by Linear Regression models, Pauling Harmonic Potential Curves, Multiple Linear Regression, Principal Component, and Partial Least Squares Regression were compared to those calculated by molecular mechanics, semiempirical, and ab initio methods using PCA-HCA procedure on the calculated bond lengths, statistical parameters, and structural aromaticity indices. Incorporation of crystal packing effects into bond orders makes multivariate models to be competitive to semiempirical results, while further improvement of quantum chemical calculations can be achieved by geometry optimization of molecular clusters.     

Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.     

Multiresidue method for simultaneous determination of ten quinolone antibacterial residues in multimatrix/multispecies animal tissues by liquid chromatography with fluorescence detection: single laboratory validation study

Quinolone antibacterials are veterinary drugs authorized for use in food animal production. The analysis of residual amounts of drugs in food from animal origin is important for quality control of products for consumers. For this purpose, Maximum Residue Limits (MRLs) have been set up by a European Union Council Regulation on Veterinary Drug Residues (No. 90/2377/EEC and subsequent), and 8 quinolones received MRLs at concentration levels depending on both the matrix and the animal species of interest. A method was developed for screening and confirming 10 quinolone residues (ciprofloxacin, danofloxacin, difloxacin, enrofloxacin, flumequine, marbofloxacin, nalidixic acid, norfloxacin, oxolinic acid, sarafloxacin) in a wide variety of matrixes of different animal species. It involves extraction of the residues from the biological tissues/fluids by acidic aqueous solution, centrifugation and filtration prior to injection on a C18 narrow-bore column, and detection through a 3-step-mode fluorescence detector. The method was validated during a 2-week study for a set of 8 species-matrixes (i.e., bovine raw milk, bovine muscle, porcine muscle, porcine kidney, porcine liver, fish flesh and skin, poultry muscle, whole egg). Residues were quantified down to 15 microg/kg with limits of detection and quantitation ranging from 4 to 11 and 13 to 36 microg/kg, respectively, which are sufficient compared to the wide range of MRLs set for these substances (from 30 microg/kg for danofloxacin in milk to 1900 microg/kg for difloxacin in poultry liver). The limit of performance of the method in terms of CCalpha and CCbeta, the critical concentrations stated in the Decision No. 2002/657/EC and the ISO Standard No. 11843, has been calculated for the authorized (MRL) substances but only estimated in the case of the nonauthorized (non-MRL) substances.     

Assessment of maximum likelihood PCA missing data imputation

Maximum likelihood principal component analysis (MLPCA) was originally proposed to incorporate measurement error variance information in principal component analysis (PCA) models. MLPCA can be used to fit PCA models in the presence of missing data, simply by assigning very large variances to the non‐measured values. An assessment of maximum likelihood missing data imputation is performed in this paper, analysing the algorithm of MLPCA and adapting several methods for PCA model building with missing data to its maximum likelihood version. In this way, known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR) methods are implemented within the MLPCA method to work as different imputation steps. Six data sets are analysed using several percentages of missing data, comparing the performance of the original algorithm, and its adapted regression‐based methods, with other state‐of‐the‐art methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Determination of Danofloxacin and Marbofloxacin in Milk Samples by Micellar Liquid Chromatography with Fluorescence Detection

Abstract

A simple, rapid, and sensitive analytical method has been developed for the determination of two fluoroquinolones, danofloxacin and marbofloxacin, in bovine milk samples. Separation and quantification were performed by micellar liquid chromatography with fluorescence detection (MLC?FD), using sodium dodecyl sulfate (SDS) as a surfactant. The influence of the principal factors, namely, the micelle concentration, the amount of organic modifier, tail‐reducing agents, the pH, and the temperature were studied. The suitable condition was found to be 75 mM SDS?10 mM phosphate buffer–18 mM tetrabutylammonium bromide/3% (v/v) 1‐propanol at pH 3.0 for the separation of marbofloxacin, danofloxacin, and tosufloxacin (internal standard) in about 20 min. The linear concentration range of application was 1.8–30.0 ng · mL^?1 for danofloxacin and 16–120 ng · mL^?1 for marbofloxacin, and the relative standard deviation ranged between 4.9 and 2.7%. The limit of detection found for danofloxacin was 0.5 ng · mL^?1 and 5 ng · mL^?1 for marbofloxacin. These values were lower than the maximum residue limits (MRLs) established by the European Union for these compounds in bovine milk. It was applied to check the eventual existence of these compounds above these limits on commercial milk samples. The validation method was completed with spiked milk samples. Recovery levels obtained were 90.3–108.2%.     

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.     

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.     

Regression models based on new local strategies for near infrared spectroscopic data

In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.     

Development of a rapid,multi-class method for the confirmatory analysis of anti-inflammatory drugs in bovine milk using liquid chromatography tandem mass spectrometry

A rapid liquid chromatography tandem mass spectrometry (LC–MS/MS) method has been developed and validated for the simultaneous identification, confirmation and quantitation of seven licensed anti-inflammatory drugs (AIDs) in bovine milk. The method was validated in accordance with the criteria defined in Commission Decision 2002/657/EC. Two classes of AIDs were investigated, corticosteroids and non-steroidal anti-inflammatory drugs (NSAIDs). The developed method is capable of detecting and confirming dexamethasone (DXM), betamethasone (BTM), prednisolone (PRED), tolfenamic acid (TLF), 5-hydroxy flunixin (5-OH-FLU), meloxicam (MLX) and 4-methyl amino antipyrine (4-MAA) at their associated maximum residue limits (MRLs). These compounds represent all the corticosteroids and NSAIDs licensed for use in bovine animals producing milk for human consumption. These compounds have never been analysed before in the same method and also 4-methyl amino antipyrine has never been analysed with the other licensed NSAIDs. The method can be considered rapid as permits the analysis of up to 30 samples in one day. Milk samples are extracted with acetonitrile; sodium chloride is added to aid partition of the milk and acetonitrile mixture. The acetonitrile extract is then subjected to liquid–liquid purification by the addition of hexane. The purified extract is finally evaporated to dryness and reconstituted in a water/acetonitrile mixture and determination is carried out by LC–MS/MS. Decision limit (CCα) values and detection capability (CCβ) values have been established for each compound.     

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.     

茶叶浸出液中农药残留表面拉曼光谱检测与初步定量分析

选取甲基对硫磷和水胺硫磷为研究对象,改良了传统的QuEChERS前处理工艺,以自制纳米金溶胶为增强基底,利用表面增强拉曼光谱(SERS)技术,对茶叶浸出液中的农药残留进行检测。通过比对两种有机磷农药的拉曼特征峰进行定性分析。同时,选取570,1034,1107和1202 cm^-1等拉曼位移附近的特征峰光谱数据,利用微分等数学手段,结合偏最小二乘法(PLSR)建立回归方程,预测样品中农药残留含量。所得预测数值与气相色谱-质谱联用(GC-MS)法检测值对比,验证本方法的可行性与可信度。结果表明:基于SERS技术对上述两种有机磷农药的检出限可达0.05 mg/L;通过数学模型分析建立回归方程,其线性相关系数范围为0.9077~0.9824,预测均方根误差(RMSEP)范围为0.77%~2.68%;利用回归方程得到的预测值与GC-MS检测结果基本接近,相对误差范围-5.16%~9.03%,回收率为81.4%~115.1%,说明可以用SERS技术对茶叶浸出液中的有机磷农药残留进行定性和初步定量分析。     

Simple Multiresidue Determination of Fluoroquinolones in Bovine Milk by Liquid Chromatography with Fluorescence Detection

Abstract

A simple and sensitive method for the simultaneous determination of five fluoroquinolones (ciprofloxacin, enrofloxacin, danofloxacin, difloxacin, and sarafloxacin) in bovine milk was developed. Protein precipitation from milk samples was achieved by the addition of acetonitrile and o‐phosphoric acid. Acetonitrile was removed with dichloromethane, leaving the fluoroquinolones in the acid aqueous extract. The aqueous extract was analyzed by liquid chromatography with fluorescence detection (LC–FD). The mobile phase was composed of acetonitrile and 10 mM citrate buffer solution of pH 4.5, with an initial composition of acetonitrile‐water (12∶88, v/v) and using linear gradient elution. Norfloxacin was used as an internal standard. The limits of detection found ranged from 1 to 6 ng · mL^?1 and were below the maximum residue limits (MRLs) established by the European Union. The proposed method was applied to the determination of these compounds in different bovine milk samples. Method validation was carried out by a recovery assay.