At-line determination of pharmaceuticals small molecule's blending end point using chemometric modeling combined with Fourier transform near infrared spectroscopy.

This article summarizes the development and validation of a Fourier transform near infrared spectroscopy (FT-NIR) method for the rapid at-line prediction of active pharmaceutical ingredient (API) in a powder blend to optimize small molecule formulations. The method was used to determine the blend uniformity end-point for a pharmaceutical solid dosage formulation containing a range of API concentrations. A set of calibration spectra from samples with concentrations ranging from 1% to 15% of API (w/w) were collected at-line from 4000 to 12,500cm-1. The ability of the FT-NIR method to predict API concentration in the blend samples was validated against a reference high performance liquid chromatography (HPLC) method. The prediction efficiency of four different types of multivariate data modeling methods such as partial least-squares 1 (PLS1), partial least-squares 2 (PLS2), principal component regression (PCR) and artificial neural network (ANN), were compared using relevant multivariate figures of merit. The prediction ability of the regression models were cross validated against results generated with the reference HPLC method. PLS1 and ANN showed excellent and superior prediction abilities when compared to PLS2 and PCR. Based upon these results and because of its decreased complexity compared to ANN, PLS1 was selected as the best chemometric method to predict blend uniformity at-line. The FT-NIR measurement and the associated chemometric analysis were implemented in the production environment for rapid at-line determination of the end-point of the small molecule blending operation.

In-Process Control Assay of Pharmaceutical Microtablets Using Hyperspectral Imaging Coupled with Multivariate Analysis.

Monitoring the amount of active pharmaceutical ingredient (API) in finished dosage form is important to ensure the content uniformity of the product. In this report, we summarize the development and validation of a hyperspectral imaging (HSI) technique for rapid in-line prediction of the active pharmaceutical ingredient (API) in microtablets with concentrations varying from 60 to 130% API (w/w). The tablet spectra of different API concentrations were collected in-line using an HSI system within the visible/near-infrared (vis/NIR; 400-1000 nm) and short-wave infrared (SWIR; 1100-2500 nm) regions. The ability of the HSI technique to predict the API concentration in the tablet samples was validated against a reference high-performance liquid chromatography (HPLC) method. The prediction efficiency of two different types of multivariate data modeling methods, that is, partial least-squares regression (PLSR) and principle component regression (PCR), were compared. The prediction ability of the regression models was cross-validated against results generated with the reference HPLC method. The results obtained from the PLSR models showed reliable performance for predicting the API concentration in SWIR region. The highest coefficient of determination (R2p) was 0.96, associated with the lowest predicted error and bias of 4.45 and -0.35%, respectively. Furthermore, the concentration-mapped images of PLSR and PCR models were used to visually characterize the API concentration present on the tablet surface. Based on these results, we suggest that HSI measurement combined with multivariate data analysis and chemical imaging could be implemented in the production environment for rapid in-line determination of pharmaceutical product quality.

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.

Transposon insertion in a cinnamyl alcohol dehydrogenase gene is responsible for a brown midrib1 mutation in maize.

Maize brown midrib1 (bm1) mutant plants have reduced lignin content and offer significant advantages when used in silage and biofuel applications. Cinnamyl alcohol dehydrogenase (CAD) catalyzes the conversion of hydroxycinnamyl aldehydes to monolignols, a key step in lignin biosynthesis. Maize CAD2 has been implicated as the underlying gene for bm1 phenotypes since bm1 plants have reduced CAD activity and lower CAD2 transcript level. Here, we describe a Dow AgroSciences maize bm1 mutant (bm1-das1) that contains a 3,444-bp transposon insertion in the first intron of CAD2 gene. As a result of chimeric RNA splicing, cad2 mRNA from bm1-das1 contains a 409-bp insert between its 1st and 2nd exons. This insertion creates a premature stop codon and is predicted to result in a truncated protein of 48 amino acids (AA), compared to 367 AA for the wild type (WT) CAD2. We have also sequenced cad2 from the reference allele bm1-ref in 515D bm1 stock and showed that it contains a two-nucleotide (AC) insertion in the 3rd exon, which is predicted to result in a truncated protein of 147 AA. The levels of cad2 mRNA in the midribs of bm1-das1 and bm1-ref are reduced by 91 and 86 % respectively, leading to reductions in total lignin contents by 24 and 30 %. Taken together, our data show that mutations in maize CAD2 are responsible for maize bm1 phenotypes. Based on specific changes in bm1-das1 and bm1-ref, high throughput TaqMan and KBioscience's allele specific PCR assays capable of differentiating mutant and WT alleles have been developed to accelerate bm1 molecular breeding.

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.

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.

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.

Neural network analyses of infrared spectra for classifying cell wall architectures.

About 10% of plant genomes are devoted to cell wall biogenesis. Our goal is to establish methodologies that identify and classify cell wall phenotypes of mutants on a genome-wide scale. Toward this goal, we have used a model system, the elongating maize (Zea mays) coleoptile system, in which cell wall changes are well characterized, to develop a paradigm for classification of a comprehensive range of cell wall architectures altered during development, by environmental perturbation, or by mutation. Dynamic changes in cell walls of etiolated maize coleoptiles, sampled at one-half-d intervals of growth, were analyzed by chemical and enzymatic assays and Fourier transform infrared spectroscopy. The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans, and mixed-linkage (1 --> 3),(1 --> 4)-beta-D-glucans, together with smaller amounts of glucomannans, xyloglucans, pectins, and a network of polyphenolic substances. During coleoptile development, changes in cell wall composition included a transient appearance of the (1 --> 3),(1 --> 4)-beta-D-glucans, a gradual loss of arabinose from glucuronoarabinoxylans, and an increase in the relative proportion of cellulose. Infrared spectra reflected these dynamic changes in composition. Although infrared spectra of walls from embryonic, elongating, and senescent coleoptiles were broadly discriminated from each other by exploratory principal components analysis, neural network algorithms (both genetic and Kohonen) could correctly classify infrared spectra from cell walls harvested from individuals differing at one-half-d interval of growth. We tested the predictive capabilities of the model with a maize inbred line, Wisconsin 22, and found it to be accurate in classifying cell walls representing developmental stage. The ability of artificial neural networks to classify infrared spectra from cell walls provides a means to identify many possible classes of cell wall phenotypes. This classification can be broadened to phenotypes resulting from mutations in genes encoding proteins for which a function is yet to be described.

The Arabidopsis MUM2 gene encodes a beta-galactosidase required for the production of seed coat mucilage with correct hydration properties.

Seed coat development in Arabidopsis thaliana involves a complex pathway where cells of the outer integument differentiate into a highly specialized cell type after fertilization. One aspect of this developmental process involves the secretion of a large amount of pectinaceous mucilage into the apoplast. When the mature seed coat is exposed to water, this mucilage expands to break the primary cell wall and encapsulate the seed. The mucilage-modified2 (mum2) mutant is characterized by a failure to extrude mucilage on hydration, although mucilage is produced as normal during development. The defect in mum2 appears to reside in the mucilage itself, as mucilage fails to expand even when the barrier of the primary cell wall is removed. We have cloned the MUM2 gene and expressed recombinant MUM2 protein, which has beta-galactosidase activity. Biochemical analysis of the mum2 mucilage reveals alterations in pectins that are consistent with a defect in beta-galactosidase activity, and we have demonstrated that MUM2 is localized to the cell wall. We propose that MUM2 is involved in modifying mucilage to allow it to expand upon hydration, establishing a link between the galactosyl side-chain structure of pectin and its physical properties.

Floral classification of honey using mid-infrared spectroscopy and surface acoustic wave based z-Nose Sensor.

Fourier transform infrared spectroscopy (FTIR) and z-Nose were used as screening tools for the identification and classification of honey from different floral sources. Honey samples were scanned using microattenuated total reflectance spectroscopy in the region of 600-4000 cm(-1). Spectral data were analyzed by principal component analysis, canonical variate analysis, and artificial neural network for classification of the different honey samples from a range of floral sources. Classification accuracy near 100% was achieved for clover (South Dakota), buckwheat (Missouri), basswood (New York), wildflower (Pennsylvania), orange blossom (California), carrot (Louisiana), and alfalfa (California) honey. The same honey samples were also analyzed using a surface acoustic wave based z-Nose technology via a chromatogram and a spectral approach, corrected for time shift and baseline shifts. On the basis of the volatile components of honey, the seven different floral honeys previously mentioned were successfully discriminated using the z-Nose approach. Classification models for FTIR and z-Nose were successfully validated (near 100% correct classification) using 20 samples of unknown honey from various floral sources. The developed FTIR and z-Nose methods were able to detect the floral origin of the seven different honey samples within 2-3 min based on the developed calibrations.

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.

Quantification of saccharides in multiple floral honeys using fourier transform infrared microattenuated total reflectance spectroscopy.

Fourier transform infrared (FTIR) spectroscopy with microattenuated total reflectance (mATR) sampling accessory and chemometrics (partial least squares and principal component regression) was used for the simultaneous determination of saccharides such as fructose, glucose, sucrose, and maltose in honey. Two calibration models were developed. The first model used a set of 42 standard mixtures of fructose, glucose, sucrose, and maltose prepared over the range of concentrations normally present in honey, whereas the second model used a set of 45 honey samples from various floral and regional sources. The developed models were validated with different data sets and verified by high-performance liquid chromatography (HPLC) measurements. The R (2) values between the FTIR-mATR predicted and HPLC results of the different sugars were between 0.971 and 0.993, demonstrating the predictive ability and accuracy of the procedure.

Simultaneous monitoring of organic acids and sugars in fresh and processed apple juice by Fourier transform infrared-attenuated total reflection spectroscopy.

A combination of Fourier transform infrared spectroscopy (FT-IR) and chemometrics was used as a screening tool for the determination of sugars and organic acids such as sucrose, glucose, fructose, sorbitol, citric acid, and malic acid in processed commercial and extracted fresh apple juices. Prepared samples of synthetic apple juice in different constituent concentration ranges were scanned by attenuated total reflectance (ATR) accessory and the spectral region in the range between 950 and 1500 cm(-1) was selected for calibration model development using partial least squares (PLS) regression and principal component regression (PCR). The calibration models were successfully validated by high-performance liquid chromatography (HPLC) measurements against several commercial juice varieties as well as juice extracted from different apple varieties to provide an overall R2 correlation of 0.998. The present study demonstrates that Fourier transform infrared spectroscopy could be used for rapid and nondestructive determination of multiple constituents in commercial and fresh apple juices. Results indicate this approach to be a rapid and cost-effective tool for routine monitoring of multiple constituents in a fruit juice production facility.