Elliptical Joint Confidence Region Research Articles

Sensitive method for total selenium determination in garlic and nuts by UV–photochemical vapor generation coupled to atomic absorption spectrometry using mild conditions

Photochemical vapor generation (PVG) coupled on–line to atomic absorption spectrometry using a flow injection mode was investigated for the determination of total Se in garlic and nut samples. In the presence of low molecular weight organic acid solutions, Se(IV) is converted by UV irradiation to volatile species which are transported to the heated quartz tube atomizer. Formic and acetic acids were tested as photochemical reagents and a mixture of 1.5 % (v/v) formic and 5 % (v/v) acetic acid was the best alternative. Central composite design was employed for parameters optimization. The effect of potential interfering elements such as hydride–forming elements, transition metals and nitric acid was investigated to ascertain possible effects of the matrix on Se(IV) signal. Under optimized conditions a detection limit (LOD) of 33 ng g–1 (3σ) was reached. Samples of garlic and nuts purchased at a local market were analyzed and Se concentrations spanned the range 0.36–248 µg g–1. These results were compared with ICP–MS measurements utilizing an elliptical joint confidence region (EJCR) with bilinear least squares analysis, and good results were obtained.

Comparison between Variable-Selection Algorithms in PLS Regression with Near-Infrared Spectroscopy to Predict Selected Metals in Soil.

Soil is one of the Earth's most important natural resources. The presence of metals can decrease environmental quality if present in excessive amounts. Analyzing soil metal contents can be costly and time consuming, but near-infrared (NIR) spectroscopy coupled with chemometric tools can offer an alternative. The most important multivariate calibration method to predict concentrations or physical, chemical or physicochemical properties as a chemometric tool is partial least-squares (PLS) regression. However, a large number of irrelevant variables may cause problems of accuracy in the predictive chemometric models. Thus, stochastic variable-selection techniques, such as the Firefly algorithm by intervals in PLS (FFiPLS), can provide better solutions for specific problems. This study aimed to evaluate the performance of FFiPLS against deterministic PLS algorithms for the prediction of metals in river basin soils. The samples had their spectra collected from the region of 1000-2500 nm. Predictive models were then built from the spectral data, including PLS, interval-PLS (iPLS), successive projections algorithm for interval selection in PLS (iSPA-PLS), and FFiPLS. The chemometric models were built with raw data and preprocessed data by using different methods such as multiplicative scatter correction (MSC), standard normal variate (SNV), mean centering, adjustment of baseline and smoothing by the Savitzky-Golay method. The elliptical joint confidence region (EJCR) used in each chemometric model presented adequate fit. FFiPLS models of iron and titanium obtained a relative prediction deviation (RPD) of more than 2. The chemometric models for determination of aluminum obtained an RPD of more than 2 in the preprocessed data with SNV, MSC and baseline (offset + linear) and with raw data. The metals Be, Gd and Y failed to obtain adequate models in terms of residual prediction deviation (RPD). These results are associated with the low values of metals in the samples. Considering the complexity of the samples, the relative error of prediction (REP) obtained between 10 and 25% of the values adequate for this type of sample. Root mean square error of calibration and prediction (RMSEC and RMSEP, respectively) presented the same profile as the other quality parameters. The FFiPLS algorithm outperformed deterministic algorithms in the construction of models estimating the content of Al, Be, Gd and Y. This study produced chemometric models with variable selection able to determine metals in the Ipojuca River watershed soils using reflectance-mode NIR spectrometry.

ATR-FTIR spectroscopy combined with chemometrics for quantification of total nicotine in Algerian smokeless tobacco products

Chemma, an oral moist snuff widespread used in Algeria, is prepared from grounded tobacco and inorganic salts or other plant materials. In addition to its adverse health issues, Chemma contains unknown amounts of nicotine and other harmful constituents. Twenty seven samples of commercial products and tobacco leaves varieties were characterized in the aim to quantitatively determine total nicotine by attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy coupled with two regression methods (univariate and multivariate). For this purpose, a highly specific-to-analyte extraction method was applied on calibration and test sets of standards for a wide concentration range. The optimal analytical models were evaluated by selecting the appropriate spectral measurement, spectral pre-processing strategies and critical figures of merit estimated according to the latest approaches. Then, the predictive ability of the method was demonstrated with the elliptical joint confidence region test and with comparing to results in literature.The linear baseline correction and the extended multiplicative scatter correction pre-processing of the combined reduced spectral regions 1333–1299 and 740–690 cm−1 followed by partial least squares regression (PLSR) offered the best analytical outcomes. Using this latter, total nicotine content in the manufactured smokeless tobacco (ST) products have been found ranging from 3.9 to 12.6 mg/g of dry weight; while in the ST leaves, it was between 5.9 and 45.0 mg/g, dry weight basis. The developed method, besides to the high predictive performance, provides advanced statistical tests that can qualitatively assess the target analyte in real sample spectra, allowing more reliable result.With the increasing number of new products containing complex agents and adulterants, the aforementioned method can be successfully adapted to routine analyses for rapid quality control of nicotine in ST and cigarettes.

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression.

The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12–30 °C and aw = 0.75–0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted aw, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R2 = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.

A video processing and machine vision-based automatic analyzer to determine sequentially total suspended and settleable solids in wastewater

The monitoring of total suspended (TSS) and settleable (SetS) solids in wastewater is essential to maintain the quality parameters for aquatic biota because they can transport pollutants and block light penetration. Determining them by their respective reference methods, however, is laborious, expensive, and time consuming. To overcome this, we developed a new analytical instrument called Solids in Wastewater's Machine Vision-based Automatic Analyzer (SWAMVA), which is equiped with an automatic sampler and a software for real-time digital movie capture to quantify sequentially the TSS and SetS contents in wastewater samples. The machine vision algorithm (MVA) coupled with the Red color plane (derived from color histograms in the Red-Green-Blue (RGB) system) showed the best prediction results with R2 of 0.988 and 0.964, and relative error of prediction (REP) of 6.133 and 9.115% for TSS and SetS, respectively. The constructed models were validated by Analysis of Variance (ANOVA), and the accuracy and precision of the predictions by the t- and F-tests, respectively, at a 0.05 significance level. The elliptical joint confidence region (EJCR) test confirmed the accuracy, while the coefficient of variation (CV) of 6.529 and 10.908% confirmed the good precisions, respectively. Compared with the reference method (Standard Methods For the Examination of Water and Wastewater), the proposed method reduced the analysis volume from 1.5 L to just 15 mL and the analysis time from 12 h to 24 s per sample. Therefore, SWAMVA can be considered an important alternative to the determination of TSS and SetS in wastewater as an automatic, fast, and low-cost analytical tool, following the principles of Green Chemistry and exploiting Industry 4.0 features such as intelligent processing, miniaturization, and machine vision.

Monitoring winemaking process using tyrosine influence in the excitation-emission matrices of wine

Wine samples collected during the winemaking process have been analyzed employing a previously optimized UHPLC-FD method, determining their biogenic amines and amino acids profile. The results obtained have been submitted to a statistical analysis from which it was extracted that the most influential analyte was tyrosine. Thanks to its fluorescence, a method for its determination by excitation-emission matrices has been proposed. The accuracy of the method has been checked by means of Elliptical Joint Confidence Region test. The winemaking process has been monitored with this method, obtaining a faster and cheaper way to follow the process.

Green volumetric procedure for determining biodiesel content in diesel blends or mixtures with vegetable oils exploiting solubility differences in an ethanol:water medium

A practical and environmentally friendly volumetric procedure, which requires only water and ethanol and generates low waste volumes, is proposed for determining the amount of biodiesel in diesel blends or mixtures with vegetable oils. These applications are relevant for the quality control of commercial fuels, to estimate transesterification reaction yields and to detect product adulteration. The proposed method is based on the differences in solubility of biodiesel and either diesel or vegetable oils in an aqueous-ethanol medium. From samples dissolved in ethanol, the volumes of the aqueous-ethanol solution required to achieve a cloudy suspension are taken as analytical signals and directly correlated to the biodiesel amount in the samples. For biodiesel:diesel samples, a linear response was obtained from 7.0 to 25.0% (v/v), whereas two linear ranges, 20.0–50.0% (v/v) and 50.0–80.0% (v/v), were established for biodiesel:vegetable oil samples, depending on the water content in the titrant. For biodiesel:diesel blends, the coefficient of variation (n = 10) and the detection limit (99.7% confidence level) were estimated at 6.8% and 2.5% (v/v), respectively, and the corresponding values for the biodiesel:vegetable oil mixtures were 5.8% and 7.0% (v/v). The procedure takes 5 min and generates 10 mL of waste per determination. Results agreed with those attained by a flow-based spectrophotometric reference procedure at the 95% confidence level, as evaluated by a paired t-test and the elliptical joint confidence region test. The proposed method is a fast, practical, cost-effective, and environmentally friendly alternative for routine analysis, including point-of-care determinations.

Second-order calibration in combination with fluorescence fibre-optic data modelling as a novel approach for monitoring the maturation stage of plums

In this work, non-destructive autofluorescence of plums was employed to study the chlorophylls’ concentration evolution along the maturation process. For that, excitation-emission matrices (EEMs), containing full fluorescence information, were collected with a fibre-optic, assembled to a spectrofluorometer. Data analysis was performed with several second-order multi-way algorithms, such as parallel factor analysis (PARAFAC), multi-way partial least-squares (N-PLS), unfolded partial least-squares (U-PLS), and multivariate curve resolution-alternating least-squares (MCR-ALS). Firstly, the EEMs of each plum, collected each week along the maturation process, were processed with PARAFAC. Two components were used to model the data and the excitation and emission loadings were obtained. Score values for the first PARAFAC component showed a clear evolution with time, increasing during the first five weeks, and decreasing for the last weeks. Also, the chlorophyll concentrations obtained by HPLC analysis, in the skin and the whole fruit, were compared with those obtained with different algorithms mentioned before. Best results were obtained in the case of skin for all algorithms. Similar correlation coefficients (r) were obtained in all cases (0.899 (PARAFAC); 0.940 (U-PLS); 0.936 (N-PLS) and 0.958 (MCR-ALS)). When the elliptical joint confidence region (EJCR), for the slope and intercept, were calculated, the theoretically expected values of 1 and 0, for the slope and intercept, respectively, were included in all ellipses. However, it was observed that for the skin data and U-PLS and N-PLS algorithms, the EJCR confidence region was smaller than in the other cases.

Simultaneous Recognition of Species, Quality Grades, and Multivariate Calibration of Antioxidant Activities for 12 Famous Green Teas Using Mid- and Near-Infrared Spectroscopy Coupled with Chemometrics.

In this paper, mid- and near-infrared spectroscopy fingerprints were combined to simultaneously discriminate 12 famous green teas and quantitatively characterize their antioxidant activities using chemometrics. A supervised pattern recognition method based on partial least square discriminant analysis (PLSDA) was adopted to classify the 12 famous green teas with different species and quality grades, and then optimized sample-weighted least-squares support vector machine (OSWLS-SVM) based on particle swarm optimization was employed to investigate the quantitative relationship between their antioxidant activities and the spectral fingerprints. As a result, 12 famous green teas can be discriminated with a recognition rate of 100% by MIR or NIR data. However, compared with individual instrumental data, data fusion was more adequate for modeling the antioxidant activities of samples with RMSEP of 0.0065. Finally, the performance of the proposed method was evaluated and validated by some statistical parameters and the elliptical joint confidence region (EJCR) test. The results indicate that fusion of mid- and near-infrared spectroscopy suggests a new avenue to discriminate the species and grades of green teas. Moreover, the proposed method also implies other promising applications with more accurate multivariate calibration of antioxidant activities.

In vitro assays predicts mineral retention and apparent protein digestibility of different fish feed measured using a juvenileP. mesopotamicusmodel

Feed chemical composition only provides information about nutrient content of a diet, but not its bioavailability. The aims were to determine mineral bioaccessibility and protein digestibility of three different fish feed, using in vitro methods and to correlate them with feed mineral retention and apparent protein digestibility measured by juvenile Piaractus mesopotamicus model, considering elliptical joint confidence region (EJCR) of weighted least-squares analysis. For in vivo assays, juvenile P. mesopotamicus were randomly stocked into conical tanks and fed during 38 days with a commercial diet, an experimental diet, or the same experimental diet added with phytase (0.2 g/kg). Total body mineral retention (mineral bioavailability indicator) and apparent protein digestibility of different diets were measured. For in vitro methods, mineral dialysability (mineral bioaccessibility indicator) and protein digestibility of the same diets were determined. No significant difference in morphometric and haematological markers were detected between diets. For iron, zinc and phosphorous bioavailability, and protein digestibility, weighted least-squares analysis showed the optimal point (1, 0) was included in EJCR, indicating in vivo and in vitro methods were comparable and correlated satisfactorily. In vitro methods were able to reproduce accurately the results obtained from traditional in vivo assays using juvenile P. mesopotamicus model.

Chemometrics-enhanced high performance liquid chromatography strategy for simultaneous determination on seven nitroaromatic compounds in environmental water

Nitroaromatic compounds pose a significant risk to human health and other living organisms. However, conventionally high performance liquid chromatography (HPLC) methods are complicated and time-consuming for complete separation of multiple nitroaromatic compounds due to their high structure-similarity. In the work, a facile yet effective strategy, which combines HPLC embedded by diode array detection (HPLC-DAD) with multivariate curve resolution-alternating least squares (MCR-ALS), is explored to improve the HPLC analysis. Based on the strategy, seven nitroaromatic compounds with similar structures could be rapidly quantified under a simple isocratic elution condition (acetonitrile/water: 65:35, v/v) within 10 min. With the aid of the second-order advantage of MCR-ALS, acceptable quantification results are still achieved for the real water samples, despite of significantly overlapping peaks resulted from the similar structures of the analytes and the unexpected interferences from the real water sample. The error analysis and the elliptical joint confidence region (EJCR) test further confirm the reliability of the predicted result. Thereby, the HPLC method coupled with the second-order calibration algorithm could provide a simple, quick and accurate strategy for simultaneous determination on the multiple nitroaromatic compounds in environmental monitoring.

A comparison of different strategies in multivariate regression models for the direct determination of Mn, Cr, and Ni in steel samples using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) analysis was carried out on eleven steel samples to quantify the concentrations of chromium, nickel, and manganese. LIBS spectral data were correlated to known concentrations of the samples using different strategies in partial least squares (PLS) regression models. For the PLS analysis, one predictive model was separately generated for each element, while different approaches were used for the selection of variables (VIP: variable importance in projection and iPLS: interval partial least squares) in the PLS model to quantify the contents of the elements. The comparison of the performance of the models showed that there was no significant statistical difference using the Wilcoxon signed rank test. The elliptical joint confidence region (EJCR) did not detect systematic errors in these proposed methodologies for each metal.

Chemometrics-enhanced liquid chromatography-full scan-mass spectrometry for interference-free analysis of multi-class mycotoxins in complex cereal samples

Mycotoxins are a class of highly carcinogenic substances often naturally occurring in the moldy foods especially cereals. In the present work, a smart chemometrics-assisted analytical strategy that combines liquid chromatography-full scan-mass spectrometry (LC-MS) detection with second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm is developed for direct, fast and interference-free determination of multi-class regulated mycotoxins in complex cereal samples with one-step ultrasound-assisted extraction. Ten mycotoxins with different property are fast eluted out (9.0min) under a simple gradient LC-separation and detected by full scanning MS with a segmented fragment program. With the aid of prominent "second-order advantage" of the algorithm, the problems of co-eluted peaks, unknown interferences and baseline drifts occurring in the LC-MS profiles are mathematically resolved, making "pure signals" of targeted analytes can be extracted out from heavily interferential information. The new strategy avoids intricate physical/chemical clean-up steps to significantly improve the accuracy of the trace analysis of mycotoxins, average recoveries of ten analytes in both complex cereal samples (maize and rice) range from 93.8 to 109% with standard deviations (SD) lower than 9.8%, and the limits of detection (LOD) range from 0.01 to 1.17 μg kg−1. In order to further confirm the reliability of the method, the same batch of samples is analyzed using LC-MS2 method with complicated immunoaffinity column (IAC) purification step, the elliptical joint confidence region (EJCR) tests of quantitative results of ten mycotoxins show that higher precision is obtained using the proposed method. Therefore, this new strategy can be as an attractive alternative for simple, fast and accurate determination of multi-class mycotoxins in complex cereal samples.

Simultaneous spectrophotometric quantification of dinitrobenzene isomers in water samples using multivariate calibration methods

A facile yet efficient strategy was proposed by means of a combination of ultraviolet (UV) spectrophotometry with multivariate calibration methods, through which 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene in water samples could be simultaneously determined without any pre-separation process. The competitive adaptive reweighted sampling combined with successive projections algorithm (CARS–SPA) approach was used to diminish uninformative variables and select the important ones from spectral data measured. The multivariate calibration models were constructed by partial least squares (PLS-1) regression with high accuracy, in which the coefficients of determination of prediction (Rpred2) were 0.9935, 0.9969, and 0.9971 and the root mean square error of prediction (RMSEP) were 0.7850, 0.5411 and 0.5414 for 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene, respectively. The optimized model was successfully applied to simultaneously determine the content of the three studied analytes in several real water samples with good recovery close to 100%. Finally, the elliptical joint confidence region (EJCR) tests further confirm that the proposed method has no proportional and constant error in the predicted concentrations, providing a statistic support for the accuracy of the model. These results indicate that it is promise for UV spectroscopy coupled to the multivariate calibration technique to establish a simple, quick, accurate and reliable analysis method for simultaneous determination of some nitroaromatic compounds in real environments. Also, the strategy proposed by the work will advance the analytical methods used in the other complicated sample systems.

Simultaneous determination of Repaglinide and Irbesartan in biological plasmas using micellar enhanced excitation-emission matrix fluorescence coupled with ATLD method

A highly sensitive and selective 3D excitation-emission fluorescence method has been proposed to rapidly quantify the combined antidiabetics Repaglinide (Re) and Irbesartan (Ir) in rat and human plasmas with the aid of second-order calibration method based on alternating trilinear decomposition (ATLD) method. Re and Ir with weak fluorescence can be endowed with strong fluorescent property by changing the microenvironment in samples and improving the fluorescence quantum yield by using an appropriate micellar enhanced surfactant. The enhanced excitation-emission matrix fluorescence of Re and Ir can be accurately resolved and can simultaneously attain the optimal concentration even in the presence of a potentially strong intrinsic fluorescence from complex biological matrices, such as rat and human plasmas, by using the ATLD method, which completely exploits the “second-order advantage”. The average recoveries of Re and Ir obtained from ATLD with the factor number of 3 (N=3) were 101.0%±4.3% and 99.1%±4.1% for rat plasma and 100.5%±5.4% and 97.1%±3.6% for human plasma. Several statistical methods, including Student’s t-test, figures of merit, and elliptical joint confidence region, have been utilized to evaluate the accuracy of the proposed method. Results show that the developed method can maintain second-order advantage in simultaneous determinations of the weak fluorescent analytes of interest in different biological plasma matrices.

Development of a Dissolution Test for Fenbendazole-Praziquantel Capsules Using UV-PLS Method

A dissolution test for capsules containing 50 mg of praziquantel and 500 mg of fenbendazole was developed and validated. The optimal conditions were an USP apparatus 2 with paddles rotating at 75 rpm, 900 mL dissolution medium (a mixture of 300 mL of ethanol and 600 mL 0.5 mol L-1 HCl), at 37.0 ± 0.5 °C. Both analytes achieved with sink conditions. A published high-performance liquid chromatography (HPLC) method was used to monitoring dissolution test during the optimization. Additionally, a chemometrics method based on UV-VIS spectrophotometry and partial least-squares (PLS) was developed and validated for the simultaneous determination of both analytes in the dissolution media. The coefficients of determination were 0.9986 and 0.9959 for fenbendazole and praziquantel, respectively, and the elliptical joint confidence region (EJRC) test concluded that constant and proportional biases were absent. The optimized model was applied to build dissolution profile and its results did not show statistical differences with HPLC method.

Quantitative determination on binary-component polymer bonded explosives: A joint study of ultraviolet spectrophotometry and multivariate calibration methods

Explosive determination is of great importance in national defense and security fields. Simple, quick and reliable analytical techniques have been highly demanded in the field. In this work, we proposed a novel method for simultaneous determination of pentaerythritol tetranitrate (PETN), 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), and 2,4,6-trinaitrotolunene (TNT) in binary-component polymer bonded explosive (PBX) samples by means of a combination of ultraviolet (UV) spectrophotometry with multivariable calibration methods. An orthogonal array design (DAD) was employed to construct the calibration set, which contains 27 reference samples. The calibration models were constructed using Partial Least Square regression (PLS-1) and Multiple Linear Regression (MLR). The variables were selected by the Successive Projection Algorithm (SPA) in MLR model. The predictive ability of the optimized models was validated by a test set including 18 samples. Finally, the two optimized models were successfully applied to simultaneously determine the content of PETN, RDX and TNT in five real binary-component PBX samples. Satisfactory results were obtained for the two models, in which the recovery yields were close to 100% for all the analytes. The computed elliptical joint confidence region (EJCR) further shows that the two models have no proportional and constant errors in the predicted concentrations. In addition, the statistical analysis indicates that MLR model with reasonable variable selection (SPA-MLR) could exhibit a slight superiority toward PLS-1 in the system. In a word, UV-spectroscopy in combination with multivariable calibration techniques has high potential to be a simple, quick and accurate analysis method for explosive determination in practice.

Infrared Spectroscopy and Multivariate Calibration for Quantification of Soybean Oil as Adulterant in Biodiesel Fuels

AbstractThis work quantifies the adulteration of ethyl and methyl soybean biodiesels/diesel (B5) blended with soybean oil using mid‐infrared spectroscopy associated with multivariate calibration. The models constructed by the method of partial least squares (PLS) presented low values of root‐mean‐square error of prediction 0.22 % (w/w) and 0.26 % (w/w), respectively, for models containing ethyl and methyl soybean biodiesel. Along with the parameters of error, accuracy was evaluated by the use of an elliptical joint confidence region (EJCR). The EJCR for the both PLS models showed there was no significant difference between the prepared concentration values and PLS predicted concentration values, and that there was no evidence of bias within the 95 % confidence level. The PLS models showed excellent correlation in the prediction set (R = 0.999) and did not present systematic errors according to the ASTM E1655 standard. Therefore, the models presented excellent performance in quantifying soybean oil as an adulterant in B5 blends, in concentrations within the range 1.00–30.00 % (w/w). The proposed methodology showed itself to be efficient for quality control of B5 contaminated with vegetable oil.

Micellar Enhanced Three-Dimensional Excitation-Emission Matrix Fluorescence for Rapid Determination of Antihypertensives in Human Plasma with Aid of Second-Order Calibration Methods

A highly sensitive three-dimensional excitation-emission fluorescence method was proposed to determine antihypertensives including valsartan and amlodipine besylate in human plasma with the aid of second-order calibration methods based on parallel factor analysis (PARAFAC) and alternating trilinear decomposition (ATLD) algorithms. Antihypertensives with weak fluorescent can be transformed into a strong fluorescent property by changing microenvironment in samples using micellar enhanced surfactant. Both the adopted algorithms with second-order advantage can improve the resolution and directly attain antihypertensives concentration even in the presence of potential strong intrinsic fluorescence from human plasma. The satisfactory results can be achieved for valsartan and amlodipine besylate in complicated human plasma. Furthermore, some statistical parameters and figures of merit were evaluated to investigate the performance of the proposed method, and the accuracy and precision of the proposed method were also validated by the elliptical joint confidence region (EJCR) test and repeatability analysis of intraday and interday assay. The proposed method could not only light a new avenue to directly determine valsartan or amlodipine besylate in human plasma, but also hold great potential to be extended as a promising alternative for more practical applications in the determination of weak fluorescent drugs.

On the performance of multiway methods for simultaneous quantification of two fluoroquinolones in urine samples by fluorescence spectroscopy and second-order calibration strategies

In the present work, the analytical performance of three multi-way algorithms has been evaluated. The proposed analytical problem was the simultaneous determination of moxifloxacin and ciprofloxacin in human urine samples using fluorescence spectroscopy. Parallel factor analysis (PARAFAC), self-weighted alternating trilinear decomposition (SWATLD) and unfolded partial least squares combined with the residual bilinearization procedure (U-PLS/RBL) have been compared, regarding their ability to solve the proposed problem. In this study, “second-order advantage” was also exploited for the mentioned algorithms through different calibration strategies. The three-way data was obtained via fluorescence spectroscopy, so that excitation–emission matrices (EEM) of the samples were recorded as the analytical signals. The accuracy and precision of each individual algorithm for analyzing the drugs in urine samples were compared using root mean square error of prediction (RMSEP), recovery and elliptical joint confidence region (EJCR) plots. The results revealed that each of the three algorithms could be applied for determination of moxifloxacin and ciprofloxacin, despite different EEM subsets and calibration strategies. However, better analytical performances were observed through PARAFAC and U-PLS/RBL modeling for MOX and CIP, respectively. So, by coupling the multi-way decomposition algorithms with fluorescence spectroscopy, a main part of preliminary sample preparation steps can be eliminated and experimental procedure might be significantly simplified, while achieving desirable analytical performance.