Evaluation of adulteration in distillate samples of Rosa damascena Mill using colorimetric sensor arrays, chemometric tools and dispersive liquid-liquid microextraction-GC-MS.

INTRODUCTION: Rosa damascena Mill distillate and its essential oil are widely used in cosmetics, perfumes and food industries. Therefore, the methods of detection for its authentication is an important issue. OBJECTIVES: We suggest colorimetric sensor array and chemometric methods to discriminate natural Rosa distillate from synthetic adulterates. MATERIAL AND METHODS: The colour responses of 20 indicators spotted on polyvinylidene fluoride (PVDF) substrate were monitored with a flatbed scanner; then their digital representation was analysed with principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA). RESULTS: Accurate discrimination of the diluted- and synthetic-mixture samples from the original ones was achieved by PLS-DA and SIMCA models with error rate of 0.01 and 0, specificity of 0.98 and 1, sensitivity of 1 and 1, and accuracy of 0.98 and 0.96, respectively. Discrimination of the synthetic adulterate from the original samples was achieved with error rate of 0.03 and 0.03, specificity of 0.94 and 0.93, sensitivity of 1 and 1, and accuracy of 0.93 and 0.71 with PLS-DA and SIMCA models, respectively. Moreover, the chemical constituents of the samples were analysed using dispersive liquid-liquid microextraction and gas chromatography-mass spectrometry (GC-MS). The main constituents of the distillate were geraniol, citronellol, and phenylethyl alcohol in different percentages, in both original and synthetic adulterate samples. CONCLUSION: These results point out the successful combination of colorimetric sensor array and PLS-DA and SIMCA as a fast, sensitive and inexpensive screening tool for discrimination of original samples of R. damascena Mill distillate from those prepared from synthetic Rosa essential oils.

Clinical and genetic spectrum of glycogen storage disease in Iranian population using targeted gene sequencing.

Glycogen storage diseases (GSDs) are known as complex disorders with overlapping manifestations. These features also preclude a specific clinical diagnosis, requiring more accurate paraclinical tests. To evaluate the patients with particular diagnosis features characterizing GSD, an observational retrospective case study was designed by performing a targeted gene sequencing (TGS) for accurate subtyping. A total of the 15 pediatric patients were admitted to our hospital and referred for molecular genetic testing using TGS. Eight genes namely SLC37A4, AGL, GBE1, PYGL, PHKB, PGAM2, and PRKAG2 were detected to be responsible for the onset of the clinical symptoms. A total number of 15 variants were identified i.e. mostly loss-of-function (LoF) variants, of which 10 variants were novel. Finally, diagnosis of GSD types Ib, III, IV, VI, IXb, IXc, X, and GSD of the heart, lethal congenital was made in 13 out of the 14 patients. Notably, GSD-IX and GSD of the heart-lethal congenital (i.e. PRKAG2 deficiency) patients have been reported in Iran for the first time which shown the development of liver cirrhosis with novel variants. These results showed that TGS, in combination with clinical, biochemical, and pathological hallmarks, could provide accurate and high-throughput results for diagnosing and sub-typing GSD and related diseases.

Quantitative structure-activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks.

BACKGROUND: After years of efforts on the control of malaria, it remains as a most deadly infectious disease. A major problem for the available anti-malarial drugs is the occurrence of drug resistance in Plasmodium. Developing of new compounds or modification of existing anti-malarial drugs is an effective approach to face this challenge. Quantitative structure activity relationship (QSAR) modelling plays an important role in design and modification of anti-malarial compounds by estimation of the activity of the compounds. METHODS: In this research, the QSAR study was done on anti-malarial activity of 33 imidazolopiperazine compounds based on artificial neural networks (ANN). The structural descriptors of imidazolopiperazine molecules was used as the independents variables and their activity against 3D7 and W2 strains was used as the dependent variables. During modelling process, 70% of compound was used as the training and two 15% of imidazolopiperazines were used as the validation and external test sets. In this work, stepwise multiple linear regression was applied as the valuable selection and ANN with Levenberg-Marquardt algorithm was utilized as an efficient non-linear approach to correlate between structural information of molecules and their anti-malarial activity. RESULTS: The sufficiency of the suggested method to estimate the anti-malarial activity of imidazolopiperazine compounds at two 3D7 and W2 strains was demonstrated using statistical parameters, such as correlation coefficient (R2), mean square error (MSE). For instance R2train = 0.947, R2val = 0.959, R2test = 0.920 shows the potential of the suggested model for the prediction of 3D7 activity. Different statistical approaches such as and applicability domain (AD) and y-scrambling was also showed the validity of models. CONCLUSION: QSAR can be an efficient way to virtual screening the molecules to design more efficient compounds with activity against malaria (3D7 and W2 strains). Imidazolopiperazines can be good candidates and change in the structure and functional groups can be done intelligently using QSAR approach to rich more efficient compounds with decreasing trial-error runs during synthesis.

Evaluation of long-heating kinetic process of edible oils using ATR-FTIR and chemometrics tools.

Long thermal oxidative kinetic and stability of four different edible oils (colza, corn, frying, sunflower) from various brands were surveyed with the use of attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with multivariate curve resolution-alternative least square (MCR-ALS). Sampling from the heated oils (at 170 °C) was performed each 3 h during a 36-h period. Changes in the ATR-FTIR spectra of the oil samples in the range of 4000-550 cm-1 were followed as a function of heating time. MCR-ALS was utilized to resolve the concentration and spectral profiles of three detected kinetic components. Three variations in resolved concentration profiles were related to the thermal-deduction of triacylglycerol of unsaturated acid, appearance of hydroperoxides form of triacylglycerols and generation of secondary oxidation products. The kinetic profiles of these species were dependent on the type of oil. The proposed method can define a new way to monitor the oils' quality.

Classification of Edible Oils Based on ATR-FTIR Spectral Information During a Long Heating Treatment.

Identification of oil type and its QC are important concerns in food control laboratories. Classifying edible oils that have not been used (i.e., unheated) with the aid of vibrational spectroscopy has previously been reported. However, the classification of used (i.e., heat-treated) oils needs special attention. The effect of long heating times on the classification of four kinds of edible oils (canola, corn, frying, and sunflower) based on attenuated total reflectance (ATR)-FTIR spectra was surveyed. The sampling was done on the oils during a 36 h heating process (at 170°C). The ATR-FTIR spectra of the samples were collected in the range of 4000-550 cm-1. Interval extended canonical variates analysis (ECVA), as a variable selection and classification tool, was used to determine the best intervals during the heating procedure for classification. Principal component analysis discriminate analysis, partial least-squares discriminate analysis, and ECVA were performed on the selected intervals and on the total heating time. The effect of autoscaling and mean-centering, as data preprocessing methods, was also investigated. The ECVA method resulted in the best performances for classification, with a 94% cross-validated nonerror rate (one misclassification) for the heating process times of 24-27 and 33-36 h.