A kinetic spectrophotometric method for the determination of ternary mixtures of reducing sugars with the aid of artificial neural networks and multivariate calibration

A differential spectrophotometric method has been developed for the simultaneous quantitative determination of glucose (GLU), fructose (FRU) and lactose (LAC) in food samples. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K₃Fe(CN)₆) as the oxidant. The reaction data were recorded at the analytical wavelength (420 nm) of the K₃Fe(CN)₆ spectrum. Since the kinetic runs of glucose, fructose and lactose overlap seriously, the condition number was calculated for the data matrix to assist with the optimisation of the experimental conditions. Values of 80 °C and 1.5 mol l⁻¹ were selected for the temperature and concentration of sodium hydroxide (NaOH), respectively. Linear calibration graphs were obtained in the concentration range of 2.96-66.7, 3.21-67.1 and 4.66-101 mg l⁻¹ for glucose, fructose and lactose, respectively. Synthetic mixtures of the three reducing sugar were analysed, and the data obtained were processed by chemometrics methods, such as partial least square (PLS), principal component regression (PCR), classical least square (CLS), back propagation-artificial neural network (BP-ANN) and radial basis function-artificial neural network (RBF-ANN), using the normal and the first-derivative kinetic data. The results show that calibrations based on first-derivative data have advantages for the prediction of the analytes and the RBF-ANN gives the lowest prediction errors of the five chemometrics methods. Following the validation of the proposed method, it was applied for the determination of the three reducing sugars in several commercial food samples; and the standard addition method yielded satisfactory recoveries in all instances.     

Simultaneous enzymatic kinetic determination of pesticides, carbaryl and phoxim, with the aid of chemometrics

A sensitive and selective enzymatic kinetic method for the simultaneous determination of mixtures of carbaryl and phoxim pesticides was researched and developed. It was based on the inhibitory effect of the pesticides on acetylcholinesterase (AChE), and the use of 5,5′-dithiobis(2-nitrobenzoic) acid (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The DTNB-thiocholine reaction was investigated by a spectrophotometric-kinetic approach. The complex rate equation for the formation of the chromogenic product, P, was solved under certain experimental conditions, which enabled the absorbance (A_P, at λ_max = 412 nm) from the mixtures of the two pesticide inhibitors to be directly related to their concentrations provided the absorbance additivity was followed. The spectra were measured for mixtures of carbaryl and phoxim at different concentrations, and at t = 904 s, T = 35 °C, pH = 7.5, c_ATChI = 0.14, and c_AChE = 0.10 mg mL⁻¹. The detection limits of the enzymatic kinetic spectrophotometric procedures for the determination of the carbaryl and phoxim were 4.7 and 0.59 μg L⁻¹, respectively.Calibration models for chemometrics methods, such as principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural network (RBF-ANN) were constructed and verified with synthetic samples of the mixtures of the two pesticides. The best performing model was based on the RBF-ANN method yielding at approximately 10 ppb analyte concentrations, %RPE_T (carbaryl = 5.2; phoxim = 6.5), %Recovery (approx.105%) and %RPE_T (6.5). Various spiked town-water samples produced recoveries in the range of 98.8-103% for each pesticide.     

Simultaneous enzymatic kinetic determination of carbamate pesticides with the aid of chemometrics

A method for the simultaneous enzymatic kinetic determination of the pesticides, oxamyl, aldicarb and aminocarb in fruit, vegetables and water samples, has been researched and developed. It was based on enzymatic reaction kinetics and spectrophotometric measurements, and results were interpreted with the aid of chemometrics. The analytical method relies on the inhibitory effect of the pesticides on acetylcholinesterase (AChE), and the use of 5,5′-dithiobis (2-nitrobenzoic) acid (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The complex rate equation for the formation of the chromogenic product, P, was solved under certain experimental conditions, and this enabled the absorbance (A _p, at λ _max = 412 nm) from the mixtures of the three pesticide inhibitors to be directly related to their concentrations. The detection limits of the enzymatic kinetic spectrophotometric procedures for the determination of the oxamyl, aldicarb and aminocarb were 0.81, 2.13 and 1.25 ng mL^?1, respectively. Calibration models were constructed for principal component regression (PCR), partial least squares (PLS), and radial basis function-artificial neural network (RBF-ANN), and verified with synthetic samples of the three pesticides. The prediction performance of these models showed generally satisfactory results, and the RBF-ANN one performed slightly better than the other two (RPE_T = 7.59% and average %recovery = 99%). This model was then successfully applied to estimate the amounts of the three compounds in fruit, vegetables and water with satisfactory results.     

Simultaneous kinetic-spectrophotometric determination of carbidopa, levodopa and methyldopa in the presence of citrate with the aid of multivariate calibration and artificial neural networks

The kinetic methodology based on the difference of reaction rates, is based on the reaction between a common oxidizing agents such as tris(1,10-phenanthroline) and iron(III) complex (ferriin, [Fe (phen)₃]³⁺) in the presence of citrate and spectrophotometrically, monitoring the changes of absorbance at the maximum wavelength of 511 nm. Experimental conditions such as pH, reagents and citrate concentrations were optimized, and the data obtained from the experiments were processed by several chemometric approaches, such as artificial neural network (ANN) and partial least squares (PLS). A set of synthetic mixtures of carbidopa (CD), levodopa (LD) and methyldopa (MD) was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the back propagation artificial neural network (BP-ANN) method afforded better precision relatively than those of radial basis function artificial neural networks (RBF-ANN) and PLS. The proposed method was also applied satisfactorily to the determination of carbidopa, levodopa and methyldopa in real samples.     

Simultaneous kinetic spectrophotometric analysis of five synthetic food colorants with the aid of chemometrics

This paper describes a simple and sensitive kinetic spectrophotometric method for the simultaneous determination of Amaranth, Ponceau 4R, Sunset Yellow, Tartrazine and Brilliant Blue in mixtures with the aid of chemometrics. The method involved two coupled reactions, viz. the reduction of iron(III) by the analytes to iron(II) in sodium acetate/hydrochloric acid solution (pH 1.71) and the chromogenic reaction between iron(II) and hexacyanoferrate(III) ions to yield a Prussian blue peak at 760 nm. The spectral data were recorded over the 500-1000 nm wavelength range every 2 s for 600 s. The kinetic data were collected at 760 nm and 600 s, and linear calibration models were satisfactorily constructed for each of the dyes with detection limits in the range of 0.04-0.50 mg L⁻¹. Multivariate calibration models for kinetic data were established and verified for methods such as the Iterative target transform factor analysis (ITTFA), principal component regression (PCR), partial least squares (PLS), and principal component-radial basis function-artificial neural network (PC-RBF-ANN) with and without wavelet packet transform (WPT) pre-treatment. The PC-RBF-ANN with WPT calibration performed somewhat better than others on the basis of the %RPE_T (∼9) and %Recovery parameters (∼108), although the effect of the WPT pre-treatment was marginal (∼0.5% RPE_T). The proposed method was applied for the simultaneous determination of the five colorants in foodstuff samples, and the results were comparable with those from a reference HPLC method.     

化学计量学-分光光度法同时测定饲料中的诺氟沙星、氧氟沙星和洛美沙星

在pH1.81的Britton-Robinson(B-R)缓冲溶液中对诺氟沙星、氧氟沙星和洛美沙星三组分混合溶液进行光度测定,所得的重叠光谱数据用经典最小二乘(CLS),主成分回归(PCR),偏最小二乘(PLS)和径向基人工神经网络(RBF-ANN)方法处理和分析,结果表明RBF-ANN对合成样中三种药物浓度的预报结果...     

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.     

Simultaneous determination of three fluoroquinolones by linear sweep stripping voltammetry with the aid of chemometrics

A linear sweep stripping voltammetric (LSSV) method has been researched and developed for simultaneous quantitative determination of mixtures of three antibiotic drugs, ofloxacin, norfloxacin and ciprofloxacin. It relies on reductive reaction of the antibiotics at a mercury electrode in a Britton-Robinson buffer (pH 3.78). The voltammograms of these three compounds overlap strongly, and show non-linear character. Thus, it is difficult to analyse the compounds individually in their mixtures. In this work, chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN) were applied for the simultaneous determination of these compounds. The prediction performance of the calibration models constructed on the basis of these methods was compared. It was shown that satisfactory quantitative results were obtained with the use of the RBF-ANN calibration model relative prediction error (RPE_T) of 8.1% and an average recovery of 101%. This method is able to accommodate non-linear data quite well. The proposed analytical method based on LSSV was applied for the analysis of ofloxacin, norfloxacin and ciprofloxacin antibiotics in bird feedstuffs and their spiked samples, as well as in eye drops with satisfactory results.     

Multicomponent kinetic spectrophotometric determination of pefloxacin and norfloxacin in pharmaceutical preparations and human plasma samples with the aid of chemometrics

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526nm, and the accompanying increase of the product, potassium manganate, at 608nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0-11.5mgL(-1) at 526 and 608nm for pefloxacin, and 0.15-1.8mgL(-1) at 526 and 608nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526nm, were the preferred methods-%RPE(T) approximately 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06mgL(-1), respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.     

Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis

Three machine learning techniques including back propagation artificial neural network (BP-ANN), radial basis function artificial neural network (RBF-ANN) and support vector regression (SVR) were applied to predicting the peptide mobility in capillary zone electrophoresis through the development of quantitative structure-mobility relationship (QSMR) models. A data set containing 102 peptides with a large range of size, charge and hydrophobicity was used as a typical study. The optimal modeling parameters of the models were determined by grid-searching approach using 10-fold cross-validation. The predicted results were compared with that obtained by the multiple linear regression (MLR) method. The results showed that the relative standard errors (R.S.E.) of the developed models for the test set obtained by MLR, BP-ANN, RBF-ANN and SVR were 11.21%, 7.47%, 5.79% and 5.75%, respectively, while the R.S.E.s for the external validation set were 11.18%, 7.87%, 7.54% and 7.18%, respectively. The better generalization ability of the QSMR models developed by machine learning techniques over MLR was exactly presented. It was shown that the machine learning techniques were effective for developing the accurate and relaible QSMR models.     

Application of differential pulse stripping voltammetry and chemometrics for the determination of three antibiotic drugs in food samples

A reliable method for simultaneous determination of three antibiotic drugs(levofloxacin,gatifloxacin and lomefloxacin) by differential pulse stripping voltammetry(DPSV) in Britton-Robinson buffer(pH 7.96) was presented.The method is based on adsorptive accumulation of the antibacterial drugs on a hanging mercury dropping electrode(HMDE),followed by the reduction of the adsorptive species by the technique of DPSV.Optimal conditions,the deposition time of 80 s,the deposition potential of—1250 mV,and the scan rate of 25 mV/s,were obtained.The linear concentration ranges of 0.010-0.080μg/mL were obtained for all these three antibiotic drugs,while the detection limits were 2.38,3.20 and 1.60ng/mL for levofloxacin,gatifloxacin and lomefloxacin,respectively.In this work,chemometrics methods,such as classical least squares(CLS),partial least squares(PLS), principle component regression(PCR) and radial basis function-artificial neural networks(RBF-ANN),were used to quantitatively resolve the overlapping signals.It was found that PCR gave the best results with total relative prediction error(RPE_T) of 7.71%.The proposed method was applied to determine these three drugs in several commercial food samples with spiked method and yielded satisfactory recoveries.     

Simultaneous spectrophotometric determination of four preservatives in foodstuffs by multivariate calibration and artificial neural networks

Benzoic acid(BA),methylparaben(MP),propylparaben(PP)and sorbic acid(SA)are food preservatives,and they have well defined UV spectra.However,their spectra overlap seriously,and it is difficult to determine them individually from their mixtures without preseparation.In this paper,seven different chemometric approaches were applied to resolve the overlapping spectra and to determine these compounds simultaneously.With respect to the criteria of%relative prediction error(RPE)and%recovery, principal component...     

Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry

A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800-1500 cm^− 1 spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample preparation step.     

Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods

Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin.     

Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics

In food production, reliable analytical methods for confirmation of purity or degree of spoilage are required by growers, food quality assessors, processors, and consumers. Seven parameters of physico-chemical properties, such as acid number, colority, density, refractive index, moisture and volatility, saponification value and peroxide value, were measured for quality and adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods such exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA.In general, the soybean and rapeseed oils were discriminated by PCA, and the two spoilt oils behaved differently with the rancid rapeseed samples exhibiting more object scatter on the PC-scores plot, than the adulterated soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models were devised, which were able to predict satisfactorily the category of the four different oil samples in the verification set. Rank ordering with the use of MCDM models indicated that the oil types can be discriminated on the PROMETHEE II scale. For the first time, it was demonstrated how ranking of oil objects with the use of PROMETHEE and GAIA could be utilized as a versatile indicator of quality performance of products on the basis of a standard selected by the stakeholder. In principle, this approach provides a very flexible method for assessment of product quality directly from the measured data.     

Simultaneous kinetic spectrophotometric determination of cephalexin and trimethoprim in pharmaceutical preparation and human urine with the aid of chemometrics

A procedure for the simultaneous kinetic spectrophotometric determination of cephalexin and trimethoprim was described. It was based on the different reaction rate of oxidation of these compounds with yellow ammonium cerous （Ⅳ） sulfate in acidic medium and colorless cerous （Ⅲ） sulfate was produced. The overlapped kinetic data was quantitatively resolved by the use of chemometric methods, partial least squares （PLS）, principal component regression （PCR） and radial basis function-artificial neural network （RBF-ANN）. The proposed method was also applied to the simultaneous determination of cephalexin and trimethoprim in pharmaceutical preparation and human urine with satisfied results, which compared well with those obtained by HPLC.     

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%.     

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10 + 2 °C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R² = 0.99) between actual and predicted data.     

Differential Kinetic Spectrophotometric Determination of Methamidophos and Fenitrothion in Water and Food Samples by Use of Chemometrics

A spectrophotometric method for simultaneous analysis of methamidophos and fenitrothion was proposed by application of chemometrics to the spectral kinetic data, which was based upon the difference in the inhibitory effect of the two pesticides on acetylcholinesterase (AChE) and the use of 5,5′‐dithiobis(2‐nitrobenzoic acid) (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The absorbance of the chromogenic product was measured at 412 nm. The different experimental conditions affecting the development and stability of the chromogenic product were carefully studied and optimized. Linear calibration graphs were obtained in the concentration range of 0.5–7.5 ng·mL^?1 and 5–75 ng·mL^?1 for methamidophos and fenitrothion, respectively. Synthetic mixtures of the two pesticides were analysed, and the data obtained processed by chemometrics, such as partial least square (PLS), principal component regression (PCR), back propagation‐artificial neural network (BP‐ANN), radial basis function‐artificial neural network (RBF‐ANN) and principal component‐radial basis function‐artificial neural network (PC‐RBF‐ANN). The results show that the RBF‐ANN gives the lowest prediction errors of the five chemometric methods. Following the validation of the proposed method, it was applied to the determination of the pesticides in several commercial fruit and vegetable samples; and the standard addition method yielded satisfactory recoveries.     

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.