Fungal growth inhibition of regenerated cellulose nanofibrous membranes containing Quillaja saponin.

Antifungal properties were introduced in nonwoven regenerated cellulose (RC) nanofibrous membrane using Quillaja saponin. To generate cellulose membranes, deacetylation of electrospun cellulose acetate (CA) nanofibrous membranes was performed using 0.05 M NaOH and ethanol for membranes both loaded and unloaded with Quillaja saponin. Chemical and physical properties of nonwoven CA and RC nanofibrous membrane were characterized using scanning electron microscopy, attenuated total reflectance-Fourier transform infrared spectroscopy, differential scanning calorimetry, and tensile properties. Our results showed that saponin doping did not affect the morphology of the resulting fibers and that the membrane structure was maintained during deacetylation. The antifungal properties of saponin-loaded fabric were determined at 0 and 24 h against two household fungi, Penicillium roguefortii and Aspergillus ochraceus, and compared with control samples. Our findings show that after 24 h the saponin-loaded fabrics had spores kill of 80.4% and 53.6% for P. roguefortii and A. ochraceus, respectively. Fabric containing Quillaja saponin has potential for household applications and could be evaluated further for medical applications.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy.

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1,100 to 2,500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient (R(2)). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R(2) value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Identification and quantification of industrial grade glycerol adulteration in red wine with fourier transform infrared spectroscopy using chemometrics and artificial neural networks.

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900-1500 cm(-1). Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis (r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

Identifications of household's spores using mid infrared spectroscopy.

Exposure to household fungi is very common both inside and outside the house and can cause health issues. The application of fourier transforms mid infrared spectroscopy (FTIR) as a screening technique for the detection and identification of household fungi was investigated. Early detection and identification of these household pathogens is very important and critical for their control. The current available methods for identification of fungi are time consuming, expensive and not very specific. Mid IR spectroscopy is a reliable and sensitive technique for the detection of spores. FTIR Spectra of four household fungi such as Aspergillus Ochraceus, Aspergillus Niger, Candida Glabrata and Penicillium Roguefortii were recorded in the mid infrared range from 600 to 4000cm(-1) using attenuated total reflectance (ATR) sampling accessory. Chemometrics analysis using principal component analysis (PCA), canonical variate analysis (CVA) and linear discriminant analysis (LDA) were performed to discriminate the fungi samples. Correspondence analysis (CA) was performed in order to visualize the relationship between different spores. An optimum classification of 100% was achieved for four different fungi. Results demonstrated that discriminant analysis of the FTIR spectra of fungi could be used for rapid detection of household fungi.

Identification of degraded products of aldicarb due to the catalytic behavior of titanium dioxide/polyacrylonitrile nanofiber.

Photocatalytic properties of fibers containing TiO2 nanoparticles were explored for use as a self-decontaminating material using degradation of the pesticide aldicarb as the model toxin. During the analysis of the aldicarb treated sample by liquid chromatography (LC) with diode array detector (DAD), an unidentified peak was found at relative retention time (RT) 3.9 min when compared to aldicarb and major metabolites, aldicarb sulfoxide, and aldicarb sulfone. An analytical method was developed to confirm and identify this degradation product. LC-APCI/MS techniques were used first to analyze molecular ions and major fragments comparing retention times and spectra with those of known standards. FTIR and LC-MS/MS techniques were used to confirm the identity of the degradation product as 2-propenal, 2-methyl-, O-[(methylamino)carbonyl]oxime.

Synthesis and grafting of thioctic acid-PEG-folate conjugates onto Au nanoparticles for selective targeting of folate receptor-positive tumor cells.

This paper reports the creation of Au nanoparticles (AuNP) that are soluble in aqueous solution over a broad range of pH and ionic strength values and that are capable of selective uptake by folate receptor positive (FR+) cancer cells. A novel poly(ethylene glycol) (PEG) construct with thioctic acid and folic acid coupled on opposite ends of the polymer chain was synthesized for targeting the AuNP to FR+ tumor cells via receptor-mediated endocytosis. These folic acid-PEG-thioctic acid conjugates were grafted onto 10-nm-diameter Au particles in aqueous solution. The resulting folate-PEG-coated nanoparticles do not aggregate over a pH range of from 2 to 12 and at electrolyte concentrations of up to 0.5 M NaCl with particle concentrations as high as 1.5 x 10(13) particles/mL. Transmission electron microscopy was used to document the performance of these coated nanoparticles in cell culture. Selective uptake of folate-PEG grafted AuNPs by KB cells, a FR+ cell line that overexpress the folate receptor, was observed. AuNP uptake was minimal in cells that (1) do not overexpress the folate receptor, (2) were exposed to AuNP lacking the folate-PEG conjugate, or (3) were co-incubated with free folic acid in large excess relative to the folate-PEG grafted AuNP. Understanding this process is an important step in the development of methods that use targeted metal nanoparticles for tumor imaging and ablation.