Authentication and identification of Lamiaceae family with cyclic voltammetry fingerprint-PCA-LDA and determination of the used phenolic contents for classification using chromatographic analyses.

Todays, it is essential to evaluate and check the quality of herbal medicines in to protect the public health. As medicinal plants, the extracts of labiate herbs are used directly or indirectly to treat a variety of diseases. Increase in their consumption has led to the fraud in herbal medicines. Hence, modern accurate diagnostic methods must be introduced to differentiate and authenticate these samples. Electrochemical fingerprints have not been evaluated for their capacity to distinguish and classify various genera within a family. Since it is essential to classify, identify, and distinguish between these closely related plants in order to guarantee the quality of the raw materials, the authenticity and quality of 48 dried and fresh Lamiaceae samples, which include Mint, Thyme, Oregano, Satureja, Basil, and Lavender with various geographic origins, were examined. The present study focused on (a) classification and authentication Labiate herbs extracts and (b) identification of active compounds in samples by Gas chromatography and HPLC methods. This was accomplished using principal component analysis (PCA) and PCA-linear discriminate analysis (PCA-LDA). The results of the clustering revealed that PCA-LDA categorized mint species more accurately than PCA. In addition to certain flavonoids including ferulic acid, apigenin, luteolin, and quercetin, HPLC and GC analysis of the ethanolic extract revealed the presence of phenolic acids such as rosmarenic acid, methyl rosmarenate, caffeic acid, cinnamic acid, and chlorogenic acid. Comparing results of PCA-LDA with chromatographic analysis show that the authentication and detection of fraud samples were correctly performed using chemometyrics technique based on CV fingerprints. Even, there was no need to completely identify components of the mint samples.

Mechanistic Insights and Potential Therapeutic Approaches in PolyQ Diseases via Autophagy.

Polyglutamine diseases are a group of congenital neurodegenerative diseases categorized with genomic abnormalities in the expansion of CAG triplet repeats in coding regions of specific disease-related genes. Protein aggregates are the toxic hallmark for polyQ diseases and initiate neuronal death. Autophagy is a catabolic process that aids in the removal of damaged organelles or toxic protein aggregates, a process required to maintain cellular homeostasis that has the potential to fight against neurodegenerative diseases, but this pathway gets affected under diseased conditions, as there is a direct impact on autophagy-related gene expression. The increase in the accumulation of autophagy vesicles reported in neurodegenerative diseases was due to an increase in autophagy or may have been due to a decrease in autophagy flux. These reports suggested that there is a contribution of autophagy in the pathology of diseases and regulation in the process of autophagy. It was demonstrated in various disease models of polyQ diseases that autophagy upregulation by using modulators can enhance the dissolution of toxic aggregates and delay disease progression. In this review, interaction of the autophagy pathway with polyQ diseases was analyzed, and a therapeutic approach with autophagy inducing drugs was established for disease pathogenesis.

Orthogonal projection approach and continuous wavelet transform-feed forward neural networks for simultaneous spectrophotometric determination of some heavy metals in diet samples.

Simultaneous spectrophotometric determination of a mixture of overlapped complexes of Fe(3+), Mn(2+), Cu(2+), and Zn(2+) ions with 2-(3-hydroxy-1-phenyl-but-2-enylideneamino) pyridine-3-ol(HPEP) by orthogonal projection approach-feed forward neural network (OPA-FFNN) and continuous wavelet transform-feed forward neural network (CWT-FFNN) is discussed. Ionic complexes HPEP were formulated with varying reagent concentration, pH and time of color formation for completion of complexation reactions. It was found that, at 5.0 × 10(-4) mol L(-1) of HPEP, pH 9.5 and 10 min after mixing the complexation reactions were completed. The spectral data were analyzed using partial response plots, and identified non-linearity modeled using FFNN. Reducing the number of OPA-FFNN and CWT-FFNN inputs were simplified using dissimilarity pure spectra of OPA and selected wavelet coefficients. Once the pure dissimilarity plots ad optimal wavelet coefficients are selected, different ANN models were employed for the calculation of the final calibration models. The performance of these two approaches were tested with regard to root mean square errors of prediction (RMSE %) values, using synthetic solutions. Under the working conditions, the proposed methods were successfully applied to the simultaneous determination of metal ions in different vegetable and foodstuff samples. The results show that, OPA-FFNN and CWT-FFNN were effective in simultaneously determining Fe(3+), Mn(2+), Cu(2+), and Zn(2+) concentration. Also, concentrations of metal ions in the samples were determined by flame atomic absorption spectrometry (FAAS). The amounts of metal ions obtained by the proposed methods were in good agreement with those obtained by FAAS.

Chemometrics-assisted spectrophotometric method for simultaneous determination of Pb²âº and Cu²âº ions in different foodstuffs, soil and water samples using 2-benzylspiro [isoindoline-1,5'-oxazolidine]-2',3,4'-trione using continuous wavelet transformation and partial least squares - calculation of pKf of complexes with rank annihilation factor analysis.

A new multi-component analysis method based on zero-crossing point-continuous wavelet transformation (CWT) was developed for simultaneous spectrophotometric determination of Cu(2+) and Pb(2+) ions based on the complex formation with 2-benzyl espiro[isoindoline-1,5 oxasolidine]-2,3,4 trione (BSIIOT). The absorption spectra were evaluated with respect to synthetic ligand concentration, time of complexation and pH. Therefore according the absorbance values, 0.015 mmol L(-1) BSIIOT, 10 min after mixing and pH 8.0 were used as optimum values. The complex formation between BSIIOT ligand and the cations Cu(2+) and Pb(2+) by application of rank annihilation factor analysis (RAFA) were investigated. Daubechies-4 (db4), discrete Meyer (dmey), Morlet (morl) and Symlet-8 (sym8) continuous wavelet transforms for signal treatments were found to be suitable among the wavelet families. The applicability of new synthetic ligand and selected mother wavelets were used for the simultaneous determination of strongly overlapped spectra of species without using any pre-chemical treatment. Therefore, CWT signals together with zero crossing technique were directly applied to the overlapping absorption spectra of Cu(2+) and Pb(2+). The calibration graphs for estimation of Pb(2+) and Cu (2+)were obtained by measuring the CWT amplitudes at zero crossing points for Cu(2+) and Pb(2+) at the wavelet domain, respectively. The proposed method was validated by simultaneous determination of Cu(2+) and Pb(2+) ions in red beans, walnut, rice, tea and soil samples. The obtained results of samples with proposed method have been compared with those predicted by partial least squares (PLS) and flame atomic absorption spectrophotometry (FAAS).

Spectrophotometric simultaneous determination of metal ions in cows' milk and vegetables with the aid of artificial neural networks using synthetic 2-benzylspiro[isoindoline-1,5'-oxazolidine]-2',3,4'-trione.

BACKGROUND: A simple, selective and sensitive multi-component method for the simultaneous determination of Zn(2+) , Mn(2+) and Fe(3+) based on complex formation with 2-benzylspiro[isoindoline-1,5'-oxazolidine]-2',3,4'-trione using artificial neural networks is proposed. RESULTS: The analytical data showed that metal-to-ligand ratios in Zn(2+) and Fe(3+) complexes was 1:1 and for Mn(2+) complex was 1:2. It was found at pH 6.5 and 5 min after mixing, the complexation reactions were completed. The coloured complexes exhibited absorption bands in the wavelength range 200-400 nm. The results showed that Zn(2+) , Mn(2+) and Fe(3+) could be determined in the range 0.1-18.0, 0.3-10.0 and 0.5-20.0 mg L(-1) , respectively. CONCLUSION: The data obtained from synthetic mixtures of metal ions were processed by radial basis function networks (RBFNs) and back-propagation neural network. The optimal conditions of the neural networks were obtained by adjusting various parameters. Satisfactory precision and accuracy were obtained with all networks, although, because of surprisingly lower root mean square error (%) values, RBFNs were the preferred approach. The proposed approach was tested by analysing the composition of the different mixtures containing Zn(2+) , Mn(2+) and Fe(3+) . The proposed method was successfully applied to the simultaneous determination of Zn(2+) , Mn(2+) and Fe(3+) ions in milk and vegetable samples.

Simultaneous determination of Mn2+ and Fe3+ as 4,4'[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol) complexes in some foods, vegetable and water samples by artificial neural networks.

A simple and sensitive spectrophotometric method to the simultaneous determination of Mn(2+) and Fe(3+) in foods, vegetable and water sample with the aid of artificial neural networks (ANNs) is described. It relies on the complexation of analytes with recently synthesised bis pyrazol base ligand as 4,4'[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol)(CMBPP). The analytical data show that the ratio of ligand to metal in metal complexes is 1:1 and 1:2 for Fe(3+) and Mn(2+), respectively. It was found that the complexation reactions are completed at pH 6.7 and 5 min after mixing. The results showed that Mn(2+) and Fe(3+) could be determined simultaneously in the range of 0.20-7.5 and 0.30-9.0 mgl(-1), respectively. The analytical characteristics of the method such as the detection limit and the relative standard error predictions were calculated. The data obtained from synthetic mixtures of the metal ions were processed by radial basis function networks (RBFNs) and feed forward neural networks (FFNNs). The optimal conditions of the neural networks were obtained by adjusting various parameters by trial-and-error. Under the working conditions, the proposed methods were successfully applied to the simultaneous determination of elements in different water, tablet, rice, tea leaves, tomato, cabbage and lettuce samples.

Biomimetic synthesis and characterization of cobalt nanoparticles using apoferritin, and investigation of direct electron transfer of Co(NPs)-ferritin at modified glassy carbon electrode to design a novel nanobiosensor.

Oxyhydroxy cobalt CoO(OH) nanoparticles (Co-NPs) were prepared in horse spleen apoferritin (HsAFr) cavity. Transmission electron microscopy revealed the particle size was 5.5-6 nm. Mineralization effect on HsAFr was investigated by fluorescence and far-UV circular dichroism (far-UV CD) spectroscopies. The far-UV CD experiments indicated an increase in the α-helical content after mineralization. Intrinsic fluorescence data showed that mineralization acts as a quencher of HsAFr. For the first time, direct electron transfer between Co(NPs)-HsAFr and a glassy carbon electrode in the thin film of dihexadecylphosphate (DHP) was investigated by cyclic voltammetry (CV) to design a biosensor. The anionic surfactant DHP was used to achieve direct electron-transfer between Co(NPs)-HsAFr molecules and the GC electrode surface. CV result showed clearly a pair of well-defined and quasi-reversible redox peaks arise from Co(NPs)-HsAFr embedded in DHP film. This novel biosensor can be used in medical and industrial fields to detect different analytes.

Application of continuous wavelet transformation to the simultaneous kinetic determination of binary mixtures.

Wavelet transformation of kinetic profiles as a new and simple method was developed for the simultaneous determination of binary mixtures without prior separation steps. The mathematical explanation of the procedure is illustrated. Daubechies (db), symlet (sym) and discrete meyer wavelet (meyr) from the family of wavelet transforms were selected and applied under the optimal conditions for the resolution of binary mixtures. A model data as well as experimental data were tested. The results from the experimental data relating to the simultaneous spectrophotometric determination of phosphate and silicate based on the formation of phospho- and silico-molybdenum blue complexes in the presence of ascorbic acid, and also simultaneous determination of Co(2+) and Ni(2+) based on their complexation reactions with 1-(2-pyridylazo)-2-naphthol (PAN) in micellar media at pH 6.0 were presented as real models. The proposed method was validated by simultaneous determination of phosphate and silicate in detergent and tap water and also Co(2+) and Ni(2+) in tap water samples.

Simultaneous spectrophotometric determination of binary mixtures of surfactants using continuous wavelet transformation.

This work presents a simple, rapid, and novel method for simultaneous determination of binary mixtures of some surfactants using continuous wavelet transformation. The method is based on the difference in the effect of surfactants Cetyltrimethylammoniumbromide (CTAB), dodecyl trimethylammonium bromide (DTAB), cetylpyridinium bromide (CPB) and TritonX-100 (TX-100) on the absorption spectra of complex of Beryllium with Chrome Azurol S (CAS) at pH 5.4. Binary mixtures of CTAB-DTAB, DTAB-CPB and CTAB-TX-100 were analyzed without prior separation steps. Different mother wavelets from the family of continuous wavelet transforms were selected and applied under the optimal conditions for simultaneous determinations. The proposed methods, under the working conditions, were successfully applied to simultaneous determination of surfactants in hair conditioner and mouthwash samples.

Simultaneous determination of Co2+, Ni2+, Cu2+ and Zn2+ ions in foodstuffs and vegetables with a new Schiff base using artificial neural networks.

New complexes of Co(2+), Ni(2+), Cu(2+) and Zn(2+) with a recently synthesized Schiff base derived from 3,6-bis((aminoethyl)thio)pyridazine were applied for their simultaneous determination with artificial neural networks. The analytical data show the ratio of metal to ligand in all metal complexes is 1:1. The absorption spectra were evaluated with respect to Schiff base concentration, pH and time of the color formation reactions. It was found that at pH 10.0 and 60min after mixing, the complexation reactions are completed and the colored complexes exhibited absorption bands in the wavelength range 300-500nm. Spectral data was reduced using principal component analysis and subjected to artificial neural networks. The data obtained from synthetic mixtures of four metal ions were processed by principal component-feed forward neural networks (PCFFNNs) and principal component-radial basis function networks (PCRBFNs). Performances of the proposed methods were tested with regard to root mean square errors of prediction (RMSEP%), using synthetic solutions. Under the working conditions, the proposed methods were successfully applied to simultaneous determination of Co(2+), Ni(2+), Cu(2+) and Zn(2+) in different vegetable, foodstuff and pharmaceutical product samples.

Comparative determination of phosphate and silicate using molybdenum blue by radial basis function and feed-forward neural networks assisted by principal component analysis.

In the present study, chemometric analysis of visible spectral data of phospho-and silico-molybdenum blue complexes was used to develop artificial neural networks (ANNs) for the simultaneous determination of the phosphate and silicate. Combinations of principal component analysis (PCA) with feed-forward neural networks (FFNNs) and radial basis function networks (RBFNs) were built and investigated. The structures of the models were simplified by using the corresponding important principal components as input instead of the original spectra. Number of inputs and hidden nodes, learning rate, transfer functions and number of epochs and SPREAD values were optimized. Performances of methods were tested with root mean square errors prediction (RMSEP, %), using synthetic solutions. The obtained satisfactory results indicate the applicability of this ANN approach based on PCA input selection for determination in highly spectral overlapping. The results obtained by FFNNs and by RBF networks were compared. The applicability of methods was investigated for synthetic samples, for detergent formulations, and for a river water sample.

A new strategy for solving matrix effect in multivariate calibration standard addition data using combination of H-point curve isolation and H-point standard addition methods.

This work presents a new and simple strategy for solving matrix effects using combination of H-point curve isolation method (HPCIM) and H-point standard addition method (HPSAM). The method uses spectrophotometric multivariate calibration data constructed by successive standard addition of an analyte into an unknown matrix. By successive standard addition of the analyte, the concentrations of remaining components (interferents) remain constant and therefore give constant cumulative spectrum for interferents in the unknown mixture. The proposed method firstly extracts such spectrum using H-point curve isolation method and then applies the obtained cumulative interferents spectrum for determination of analyte by H-point standard addition method. In order to evaluate the applicability of the method a simulated as well as several experimental data sets were tested. The method was then applied to the determination of paracetamol in pharmaceutical tablets and copper in urine samples and in a copper alloy.

Artificial neural networks for determination of enantiomeric composition of alpha-phenylglycine using UV spectra of cyclodextrin host-guest complexes: comparison of feed-forward and radial basis function networks.

In this work feed-forward neural networks and radial basis function networks were used for the determination of enantiomeric composition of alpha-phenylglycine using UV spectra of cyclodextrin host-guest complexes and the data provided by two techniques were compared. Wavelet transformation (WT) and principal component analysis (PCA) were used for data compression prior to neural network construction and their efficiencies were compared. The structures of the wavelet transformation-radial basis function networks (WT-RBFNs) and wavelet transformation-feed-forward neural networks (WT-FFNNs), were simplified by using the corresponding wavelet coefficients of three mother wavelets (Mexican hat, daubechies and symlets). Dilation parameters, number of inputs, hidden nodes, learning rate, transfer functions, number of epochs and SPREAD values were optimized. Performances of the proposed methods were tested with regard to root mean square errors of prediction (RMSE%), using synthetic solutions containing a fixed concentration of beta-cyclodextrin (beta-CD) and fixed concentration of alpha-phenylglycine (alpha-Gly) with different enantiomeric compositions. Although satisfactory results with regard to some statistical parameters were obtained for all the investigated methods but the best results were achieved by WT-RBFNs.